applied computing skills to problem solving

• Advisory Board
• Biological Sciences
• Computing & Software Systems
• Engineering & Mathematics
• Physical Sciences
• STEM Inclusivity Awards
• School of STEM contact
• School of STEM Employment
• Student Forms & Resources
• Investigative Biology
• Research & Internships
• Student Organizations
• Friday Harbor Laboratories
• Earth System Science
• Student Projects
• Actuarial Science
• Computer Science & Software Engineering (CSSE)
• Data Science
• Information Technology (IT)
• Marine Biology
• Mathematics
• Neuroscience
• Capstone & Symposium Event
• Undergraduate Policies & Procedures
• CSS Registration & Waitlists
• Getting Started with Computer Science
• Excellence in Cybersecurity Education
• How to Apply
• Core Courses
• Elective Courses
• Project or Thesis
• Computer Engineering
• Power & Energy
• Signal Processing
• Biomedical Systems and Devices
• Microelectronics and Devices
• Wireless, RF/Microwave, and Telecommunications
• Prerequisites guide
• Frequently Asked Questions
• Career Resources
• Previous Quarters
• Division of Biological Sciences Faculty
• Division of Computing & Software Systems Faculty
• Division of Engineering & Mathematics Faculty
• Faculty in Chemistry
• Faculty in Earth System Science
• Faculty in Physics
• Associate Professor with Tenure
• Full Professor
• Teaching Track Faculty
• Promotion and Tenure Public Statement
• Teaching Effectiveness
• Capstones & internships
• Center for Biotechnology Innovation & Training
• National Center of Academic Excellence in Cybersecurity
• Research Experiences for Undergraduates (REU)
• Faculty Research Projects
• Research Experiences for Undergraduates (REU) from Heritage University
• Ocean Engineering
• Safety and Security Studies and Modeling in Fluids
• Instrumentation
• Room Access
• Support Services
• Quick Links & Forms
• EE Open lab schedule

Quick Links

• Information Technology
• Commuter Services
• Human Resources
• Campus Safety

Applied Computing

The Bachelor of Arts in Applied Computing is a multidisciplinary major that helps you to become an expert in combining computing technology with another interest area in preparation for your future career. This program is highly relevant today, as computing expertise is needed in almost every field – the possibilities are endless!

To see some examples of careers our previous graduates have entered, visit the Careers & Testimonials page .

Competencies

The program offers a multidisciplinary approach that will enable students to develop a wide range of competencies:

• Analysis and problem solving techniques
• Communication and business management skills
• Fundamental understanding of the software design and development process
• Solid technical foundation for applied critical thinking and learning in the workplace
• Expertise in your choice of a Second Discipline of study
• Confidence in career navigation , including understanding your personal values that lead to happiness and balance between work and life

Visit the Division of Computing & Software Systems (CSS) page to learn more about the learning goals of this major.

Your education in Applied Computing combines your computer science coursework with your Second Discipline:

• Computer science core courses
• Computer science electives
• Second Discipline courses
• General electives and Areas of Inquiry

Visit the Curriculum Overview page to learn more about the specific degree requirements.

• Getting Started with Computer Programming
• Curriculum Overview
• Capstone Requirement
• Academic Advising
• Careers & Testimonials

CS for CA News & Updates

Computer science skills: computational thinking explained.

It’s a common misconception that computer science (CS) is only applicable to people working in a technology or STEM careers. However, skills learnt through CS are used in our everyday lives, and in a variety of subjects.

One of these skills is known as computational thinking (CT). 

What is computational thinking?

There are many problem-solving skills involved in computer science, including those needed to design, develop, and debug software. Computational thinking is a way of describing these skills.

Computational thinking refers to the thought processes involved in defining a problem and its solution so that the solution can be expertly carried out by a computer. We don't need computers to engage in computational thinking, but CT can leverage the power of computers to solve a problem.

Computational thinking helps build these skills:

• Decomposition – the process of breaking down a complex problem into smaller parts that are more manageable, and helps us see problems as less overwhelming.
• Abstraction – identifying common features, recognizing patterns, and filtering out what we don’t need. 
• Algorithmic Thinking – designing a set of steps to accomplish a specific task. 
• Debugging and Evaluation – testing and refining a potential solution, and ensuring it’s the best fit for the problem.

These skills relate to critical thinking and problem solving skills across different subject matter, highlighting how concepts of computing can be combined with other fields of study to assist in problem-solving.

Computational thinking is a way of describing the many problem solving skills involved in computer science, including those needed to design, develop, and debug software. However, computer science is more than just skills, it also includes concepts about the Internet, networking, data, cybersecurity, artificial intelligence, and interfaces. Computational thinking can be relevant beyond computer science, overlapping with skills also used in other STEM subjects, as well as the arts, social sciences, and humanities.

Why is computational thinking important? 

Computational thinking can apply these problem-solving techniques to a variety of subjects. For example, CT is established as one of the Science and Engineering Practices in the Next Generation Science Standards , and can also be found in several math state standards . Computational thinking also overlaps with skills used in other STEM subjects, as well as the arts, social sciences, and humanities. Computational thinking encourages us to use the power of computing beyond the screen and keyboard. 

It can also allow us to advance equity in computer science education...

By centering the problem-solving skills that are at the heart of computer science, we can promote its integration with other subject areas, and expose more students to the possibilities of computer science. 

Not only that, but computational thinking also opens the door for us to examine the limitations and opportunities of technology as it’s being developed. We’re able to analyze who is creating technology and why, as well as think critically about the ways in which it can impact society. 

Want to learn more about computational thinking?

To learn more about computational thinking, check out the resources:

• This framework for CS for K-12 places CT at the core of its practices and is what the California standards are based on. 
• Part of the British Computing Society, Computing at School put forth resources to assist teachers in the UK in embedding  CT in their classrooms. 
• This is one of the earliest definitions of CT for educators, and noteworthy for its inclusion of certain dispositions as being essential for effective CT.  
• The developers of Scratch divide CT into concepts, practices, and perspectives, and focus on the expressive and creative nature of computing. 
• Instead of focusing solely on standards for students, ISTE  compiled a set of knowledge, skills, and mindsets needed for educators to be successful in integrating  CT across the K-12 content areas and grade bands.  
• Bebras began as an international competition to promote CT for students, regardless of programming experience. It is now increasingly being used as a form of CT assessment. 

Explore Related Articles

New Report on Equity in Computer Science Education in California’s Schools

California Governor's Budget Supports Computer Science education

SBE Approves Computer Science Strategic Implementation Plan

Unable to load Tweets

Home › All Programs › Applied Computing (Applied Science BAS)

Home › All Programs › Applied Computing (Applied Science BAS) ›

Applied Computing

Bachelor of applied science.

Online Bachelor's Program in the Nation

- U.S. News & World Report, 2024

Public Flagship University

Quick Facts

*Up to 60 credits may be transferred in from a regionally accredited institution.

College of Applied Science & Technology

Develop core skills to propel your career in the field of applied computing. Choose from one of six areas of emphasis to specialize your degree: Software Development, Information Management, Network Operations, Applied Artificial Intelligence, Cloud Computing and DevOps.

Learn to use computation and technology as universal tools to solve problems and design and build computer-based systems and digital artifacts. This knowledge will prepare you to advance your career across a wide range of government, private, and public organizations including military, finance, retail, education and manufacturing.

This program features state-of-the-art online technologies to engage students in interactive assessments and exercises. Coursework is designed to maximize your ability to learn by doing. This hands-on learning provides real-world experience that will set you apart in a competitive job market.

*Residents of some U.S. Territories may not be eligible. Please see our Eligibility & State Authorization page for more information.

The curriculum for this program includes:

APCV 302: Statistics in the Information Age

Receive an introduction to descriptive and inferential statistics, as well as data complexity, uncertainty and variation in information age. Discuss techniques for interpreting the data.

APCV 320: Computational Thinking and Doing

Explore basic programming and techniques used by computing professionals in a variety of application areas. Topics include computation, programs, algorithms, programming languages, and complexity, as well as how these concepts and techniques are used to solve problems in computing.

CYBV 301: Fundamentals of Cyber Security

Gain an introduction to cyber security policy, doctrine and operational constraints. A broad survey of cybersecurity concepts, tools, technologies and best practices will be presented. Use hands-on activities to become familiar with and practice cybersecurity techniques and procedures.

APCV 310: Introduction to Computing

Explore computing concepts in hardware, software, networking, data processing, and other emerging technologies. Topics cover information representation, relational databases, system design, web development, and cutting edge technologies for CPU, operating systems and networks.

Earning your Bachelor of Applied Science in Applied Computing will build core skills, including:

• Security operations development
• Computer science
• Language programming
• Code review
• Operating systems
• Information systems
• Software engineering
• Systems engineering
• Agile methodology
• Scalability
• Markup languages
• Object-Oriented Programming
• Data analysis
• Application development

Graduates of the Applied Science BAS program will be prepared to pursue the following careers:

• Cybersecurity Application Analyst
• Database Administrator
• Development Operations Architect/Engineer
• Information Architect
• Machine Learning Specialist/Engineer
• Network Manager
• Security Analyst
• Software Architect
• System/Network Administrator
• Web Developer

Areas of Emphasis

Software Development

In the Software Development emphasis, you will build a strong foundation in computer programming, web development and application development that will prepare you to increase your earning potential. Upon graduation, you may choose to pursue careers including software and web developer, data engineer, mobile app developer and data analyst.

View detailed program information

Specialized courses in this emphasis include:

• CSCV 335: Object-Oriented Programming and Design
• CSCV 337: Web Programming
• CSCV 352: Systems Programming and UNIX

APCV 361: Data Analysis and Visualization

Information Management

In the Information Management emphasis, you will develop your understanding of database systems, web design, programming and data visualization and analysis. These skills will help prepare you to pursue careers in high-demand fields ranging from database administration, data analysis and engineering, web development and information architecture.

APCV 360: Database Management Fundamentals

• CSCV 460: Database Design

Network Operations

In the Network Operations emphasis, you will learn the engineering and operational activities required to create, operate and defend networks. This advanced program will challenge you to merge theory, understanding and practice. The curriculum includes operational labs, modern network architecture, advanced routing and switching, systems administration, cloud computing, network defense, wireless networking and network security.

• NETV/INFV 370: Intro to Network Design and Architecture

NETV 371: Network Security Principles

• NETV 375: Advanced Routing and WAN Technologies

NETV 379: Cloud Computing

Applied Artificial Intelligence

The Applied Artificial Intelligence (AI) emphasis focuses on AI algorithm development and applying AI to approach practical application problems. In this emphasis, you will master concepts and tools including machine learning, statistical analysis and data analytics in applied computing. This emphasis will prepare you to pursue careers such as AI Specialist/Developer, Data Engineer and Security Programmer.

CSCV 471: Artificial Intelligence

Cybv 373: violent python, cybv 474: advanced analytics for security operations.

This course will lay a foundation for understanding how to process, analyze and visualize data. Topics include data collection and integration, exploratory data analysis, statistical inference and modeling, machine learning, and data visualization. The emphasis of the course topics will be placed on integration and synthesis of concepts and their application to solving problems. 

This course is an introduction to Artificial Intelligence from a computer science perspective. The main focus of the course is knowledge representation and reasoning techniques in the design and implementation of intelligent systems. Topics include problem formulation, problem-solving and search, knowledge-based systems and inference, and machine learning. You will be expected to identify and analyze real problems in the world around us that might benefit from AI and to design and implement possible solutions.

In this course, you will be provided with advanced practical applications of Python programming to support offensive and defensive cybersecurity operations. A crosscut of Python concepts, tools, and techniques will be presented. Use interactive programming activities to master and create advanced Python tools to support common cybersecurity tasks.

This course is an in-depth examination of how the Python scripting language can be used to support advanced analysis in offensive and defensive security operations. You will use hands-on scripting exercises to evaluate the strengths and weaknesses of automated tools to solve complex security-related problems, practice creating and using Python-based algorithmic solutions, and gain a technical understanding of how to apply the existing Python libraries to support common security-related tasks.

Cloud Computing

In the Cloud Computing emphasis, you will become familiar with the complicated infrastructure related to virtualization, Amazon Web Services and Microsoft Azure. The course outline provides a baseline on virtualization technologies, introduction to cloud technologies, as well as courses focusing on Amazon and Microsoft, with advanced discussion topics on cloud computing.

NETV 301: Virtualization: Applications & Best Practices

Netv 380: introduction to microsoft azure, netv 381: introduction to amazon web services.

This course introduces the theory and application of virtualization. You will delve into advanced virtualization concepts including containerization, microservices, software-defined architectures and virtualization security. Topics cover the basics of virtual machines, containers and microservices; CPU, memory, storage and network virtualization; paravirtualization, hardware virtualization, and OS-level virtualization (containers); hardware features supporting virtualization and nested virtualization. Actual virtualization software will be used to provide hands-on experience with virtualization.

The theory and application of cloud computing, including Cloud Computing network design and connectivity, server management, best practices, security, and provider service level agreements, will be covered in this course. Case studies of industry examples are used as applications to reinforce the discussed theories. Hands-on laboratory exercises in Amazon AWS or Microsoft Azure are used to complement the instructional material.

This course develops technical expertise in cloud computing architecture, design and implementation using Microsoft Azure. This course will address designing Azure computer infrastructures, including virtual machines, web applications, serverless and microservices. It will address designing effective network implementations in Azure as well as designing data implementations using different data services, relational database storage, and NoSQL storage. It will include practical hands-on experience solving real-world cloud computing problems with Azure.

This course develops technical expertise in cloud computing architecture, design and implementation using Amazon Web Services (AWS). This course will address applying AWS business and technical tools and architecting and designing cloud solutions using AWS. We will address how AWS can help meet compliance, governance, and regulatory requirements. It will include practical hands-on experience solving real-world cloud computing problems with AWS.

The DevOps emphasis teaches core principles of Applied Computing, enabling you to develop a solid foundation in statistics, programming, networking and cybersecurity. Courses focus on the development and application of DevOps to approach practical application problems in secure computing. In this emphasis, you will gain hands-on, interdisciplinary experience alongside peers and expert faculty.

CYBV 302: Linux Security Essentials

Cybv 303: windows security essentials, apcv/netv 378: system administration.

This course is an in-depth analysis of Linux and Unix security issues. This includes configuration guidance using industry standards and benchmarks and implementation through practical, real-world examples. The course will examine how to mitigate or eliminate general problems that apply to Nix like OSs, including vulnerabilities in passwords and password authentication systems, virtual memory system, and applications most commonly run.

Gain foundational knowledge of the Windows Operating System and securing Windows environments including learning PowerShell scripting, host hardening and active directory scripting, smart tokens and Public Key Infrastructure (PKI), protecting admin credentials, and thwarting hackers inside the network. You will use hands-on labs and exercises to secure Windows systems, networks, applications and data.

This course provides an introduction to database management concepts including definitions of data elements, basic data structures, data modeling and systems architectures. Topics also cover some of the leading database management products and design tools currently in use.

This course focuses on in-depth coverage of current risks and threats to an organization's information including methods of addressing the safeguarding of these critical assets. Coverage includes the theoretical and historical background necessary to understand the various risks and hands-on techniques for working in the security field.

This course covers the theory and application of system administration from a UNIX and Windows perspective, including installation, management, optimization and security. Case studies of industry examples are used as applications to reinforce the discussed theories.

You may also be interested in

How to Apply

Financial Aid

Keep in Touch

Start the application by logging into the portal

Undergraduates

First time users

Create Account

Returning users

Subscribe to our newsletter

From Big Idea to Problem-solving: How AI Shaped a Computer Scientist’s Career

Abigail Gertner’s career has centered on artificial intelligence. The department manager of AI-Enhanced Discovery and Decisions at MITRE talked with Karina Wright about having a front row seat to AI’s evolution. 

Abigail Gertner has watched artificial intelligence (AI) grow from a primarily academic study with few practical applications to today’s vast number of uses. 

“When I started out, we were developing rule-based systems and belief propagation algorithms to make inferences on probabilistic graphical models. The computing hardware didn’t exist to support the kind of large-scale model development we can do today.”

The turning point, Gertner says, came with “the availability of  GPUs [graphics processing units] for training larger AI models, along with the massive amount of data available on the internet.”

Both led to the rapid progress in machine learning (computers learning models based on data) we’ve seen in recent years, “moving the field forward to solve a lot of the problems AI aimed to solve decades ago.”

Any situation where a human needs information or analysis—or there's some complexity to deal with that AI is better suited to handle—we develop tools to help make those kinds of consequential choices. Abigail Gertner

“In our  AI and Autonomy Innovation Center , we're working on large-scale problems across the full spectrum of our sponsors, including  defense ,  transportation , and  health . Each sector has unique challenges, and solutions can bring meaningful impact for the country and, in some cases, the world.”

MITRE sponsors, Gertner explains, are using AI-enabled applications to increase public safety and boost the efficiency of government services, among many other uses. 

Gertner emphasizes the importance of AI assurance—the process of ensuring that AI systems perform effectively, with acceptable levels of risk. 

Her team’s research runs the gamut, from using language processing tools to help fight disinformation, to policy equity analysis, to applying AI to improve social services delivery. 

“We're developing new approaches to problems no one has worked on before,” she says. 

And that’s right where Gertner wants to be. 

“It’s not just the variety of applications, but also the technical challenge that I enjoy.” 

AI for Public Good 

As an AI researcher who's also a department manager, Gertner juggles many priorities. 

“Last year I was co-principal investigator of a research project focused on using large language models (LLMs) for understanding strategic messaging. For example, we can reverse engineer a synthetic message to get an understanding of the intention behind it. How was it generated? What prompt was used? What model?”

The goal is to potentially combat disinformation that LLMs might help proliferate. 

Another current focus is the use of LLMs as decision aids. 

“No matter how powerful your LLM is, there's still a lot of higher-level system development needed to ensure it’s used in a safe, secure, ethical way, and that it interacts well with humans.”

On that front, Gertner collaborates with The University of Texas at Austin’s  Good Systems initiative to promote responsible, ethical, and socially beneficial use of AI. She heads a project developing tools to facilitate services for people experiencing or at risk of homelessness. 

Client-focused tools can help a person understand what services they might be eligible for—and make the application process easier, Gertner explains. For case workers, AI tools can help summarize large volumes of case notes into an easy-to-digest format.

Gertner and her team also work with stakeholders to establish priorities and identify potential risks associated with using AI in the social services domain.

“A recognized weakness of LLMs is their potential to hallucinate, to make up things that aren’t true. It’s important to design systems in such a way that users don’t act on incorrect information.”

Persistence Is Everything 

MITRE landed on Gertner’s radar during graduate school, when she met researchers at a user modeling conference. She submitted her resume, but there wasn’t an open position at the time. 

She instead did postdoctoral research at the University of Pittsburgh. There, Gertner applied Bayesian networks, a type of model for reasoning under uncertainty, to model students’ knowledge and understanding of physics. The goal was to build an intelligent tutoring system. 

Four years after her first interview, Gertner joined MITRE in 2000.

“It turned out the work I did in Pittsburgh was applicable to my early MITRE work—developing training applications as part of our  independent R&D program .”

Gertner holds a Ph.D. in computer science, but, she says, “machine learning wasn’t a big thing when I got here. Along the way, I've been able to take classes and learn about rapidly growing areas.” 

The question now, as Gertner sees it, is “just how far can we take things with these machine learning model-based approaches?”

When she’s not pursuing better AI or spending time with family, Gertner enjoys what she calls her “odd collection of hobbies.” She sings in a chorus. She lifts weights. She’s also an “obsessed knitter,” which helps her focus.

“Right now, I'm learning to play the mandolin. I love to learn new things.” 

Interested in solving problems for a safer world? Join our community of innovators, learners, knowledge-sharers, and risk takers. View our  Job Openings . Subscribe to our   MITRE 360 Newsletter .

Bachelor of Science Applied Computing

Request more information.

Download the degree guide for details on admission requirements, tuition and courses.

By selecting yes, I agree to receive updates about online degrees, events, and application deadlines from the Universities of Wisconsin.

Msg frequency varies depending on the activity of your record. Message and data rates may apply. Text HELP for help. You can opt out by responding STOP at any time. View our Terms and Conditions and Privacy Policy for more details.

UW Extended Campus will not share your personal information. Privacy Policy

• Course Schedule

Future-focused, Real-world Application of Technology and Business

Applied computing is the study of both theoretical and applied computer science. Considered the intersection of information technology, computer science, and business, applied computing focuses on technical computing concepts and the development of skills in organizational leadership and business strategy.

Organizations across industries need computing professionals that are great programmers, and can drive business success with skills like project management, communication, and IT strategy. Designed for working adults, the Bachelor’s in Applied Computing degree completion program will help you develop skills in both programming technology and business to prepare you to stand out and secure your spot in this growing field.

Future-focused Learning.

The UW Applied Computing program proves to employers that you have the tools required to solve their organization’s day-to-day technical and operational issues. More importantly, it teaches you how to develop new technologies and participate in future tech innovation so you’ll know how to fix tomorrow’s problems.

Technology and Business Combined.

Today’s employers need well-rounded IT professionals. This unique computer science curriculum has a sharp focus on business skills like project management, IS strategy, and legal and ethical issues, so you can use your technical expertise to propel the organization forward.

Real-world Application.

Because the program was designed with working adults in mind, skills are combined and taught in the same way professionals utilize them day-to-day in the workplace. With the capstone course, you’ll get a chance to apply your knowledge to a real-world project, giving you the practical experience employers will notice. This applied program will teach you current technologies, as well as the techniques you need to successfully learn future technology systems to keep you ahead of the IT curve.

Upon graduation, you will be well prepared to work in organizations public and private, in virtually any industry: healthcare, computer science, information technology, retail, marketing, manufacturing, transportation, communication, education, insurance, finance, science, security, law enforcement, and more. The dual-natured curriculum focuses both on developing a strong technical foundation, as well as the nuanced professional skills required to thrive in any IT role.

Completing this applied degree will indicate to employers that you’re skilled in the hands-on application of the tools and technologies you’ll need on a day-to-day basis.  This degree can be the foundation for a variety of positions, including:

• Software developer
• Database developer
• Systems administrator
• Application/full stack developer
• Software engineer
• Video game developer
• Business analyst
• Web developer
• Project manager

Who Should Apply?

The UW Bachelor of Science in Applied Computing is intended for individuals who would like to advance their current role in IT or those who would like to change careers to pursue this in-demand field. Working parents, professionals, and veterans will find the flexibility of online courses especially convenient.

As a bachelor’s completion program, the applied computing degree is a great option for anyone with existing, transferable college credits who wants to complete their degree.  Ideal candidates will hold at least 45-60* general education credits and can come from nearly any professional field. Your Success Coach at UW Extended Campus can help you find a program to complete your general education requirements. *See admission tab for more specific information.

UW System Collaboration

The UW Bachelor of Science in Applied Computing is a collaboration of the University of Wisconsin Extended Campus and UW-Milwaukee, UW-Oshkosh, UW-Platteville, UW-River Falls, and UW-Stevens Point , bringing you the best and brightest IT instructors from across the UW System. Learn more about our campus partners and choosing a home campus .

Accreditation

The UW Bachelor of Science in Applied Computing program is approved by the University of Wisconsin Board of Regents and approved by the Higher Learning Commission .

To be eligible for the Bachelor of Science in Applied Computing students must meet the following requirements:

• Approximately 45-60 transferable general education credits with a 2.0 minimum grade point average (GPA). There are options available for those who do not meet this general education requirement, such as potentially taking them through your chosen home campus or through one of these associate degrees .  Please contact an enrollment adviser for more information and to find out what options may be available to you.
• Prerequisite coursework in college algebra or equivalent coursework
• Official college transcripts

Application Deadlines

All application materials need to be completed two weeks prior to the semester start to be considered for admission. Starting your application early will help ensure you have plenty of time to gather required materials (such as transcripts), transfer credits, apply for financial aid, and complete the University of Wisconsin System Online Admission Application .

How to Apply

While you are free to apply on your own, many prospective students speak with an enrollment adviser first. Our friendly staff is here to answer your questions, talk with you about your career goals, and help you decide if this program is a good choice for you.

Step 1. Select a “home” campus from our list of program partners: UW-Milwaukee, UW-Oshkosh, UW-Platteville, UW-River Falls, or UW-Stevens Point.

Step 2. Apply to your preferred home campus using the University of Wisconsin System Online Admission Application . Choose the “Applied Computing-Collaborative” program. There is no application fee for all undergraduate degree seeking applicants (domestic and international).

Step 3. Send official college transcripts from all institutions attended directly to the home campus admissions office to which you are applying.  If you have an associate degree, bachelor’s degree, or equivalent coursework, the ACT or SAT is not required.

Formal admission to the program will be determined by the campus to which you apply.

The UW Applied Computing Program Curriculum

The UW Bachelor of Science in Applied Computing offers 100% online courses. All course content, from multimedia lectures and e-learning tools to homework assignments, will be delivered to you through the program’s online learning management system . You can study and do homework whenever and wherever it’s convenient for you.

Students are required to take each of the 21 technical- and business-focused courses in the curriculum. Due to the unique nature of the program, courses from other degree programs may not transfer in directly for Applied Computing courses. Only your home campus can determine how previous coursework might satisfy Applied Computing requirements.

Preview lectures, assignments, and discussions in this Course Inside Look: Programming I .

Spring 2024

Course Preview Week: January 16 - January 22, 2024 Semester Dates: January 23 - May 03, 2024

Summer 2024

Course Preview Week: May 21 - May 27, 2024 Semester Dates: May 28 - August 09, 2024

Course Preview Week: August 27 - September 02, 2024 Semester Dates: September 03 - December 13, 2024

Spring 2025

Registration Opens: November 11, 2024 Course Preview Week: January 21 - January 27, 2025 Semester Dates: January 28 - May 09, 2025

Our Mission : The Bachelor of Science in Applied Computing program is dedicated to equipping a diverse student community with the knowledge, practical skills, and experiential learning needed to excel in the field of technology. Rooted in a foundation of computer science theory, programming languages, and interdisciplinary problem-solving, our mission focuses on developing the skills required to have a successful career in the IT Industry. Our program’s dedication to continuous learning, supported by dedicated faculty and industry-leading resources, shapes graduates into forward-thinking professionals poised to drive technological progress in a framework of ethical action and sustainability.

Upon completion of your bachelor’s degree, you will possess the following IT skills and abilities:

Demonstrate a solid foundation in core computer science

You will be able to:

• Apply fundamental programming knowledge and techniques to write software of varying complexities
• Utilize standard data structures and algorithms in the software development process
• Develop software using operating system theory and concepts
• Demonstrate the understanding of computer networks, protocols, and devices
• Describe the professional, ethical, and social issues and responsibilities in the computing field

Demonstrate a solid foundation in software engineering practices

• Analyze a problem and identify and define the computing requirements for a solution
• Design, create, and document software to solve a defined problem
• Use testing methodologies to ensure software meets requirements

Recognize and address security issues

• Describe the elements needed to implement a comprehensive security plan for an organization (e.g. asset security, communication/network security, and identity/access management)
• Utilize best practices in security engineering when developing software and managing data
• Describe the privacy, legal, and regulatory compliance environment under which systems

Implement a computing solution for a business problem

• Apply Agile and traditional project management methodologies to the development of systems
• Use systems analysis methodologies to solve a business problem
• Describe the role and responsibilities of the functional areas of business
• Evaluate the risk, capability, and benefits of adopting new technologies
• Explain the role of IT in supporting organizational process and strategy

Demonstrate effective oral and written communication skills

• Write, format, disseminate, and orally communicate technical materials
• Help non-technical professionals visualize, explore, and act on technical information
• Facilitate discussions with stakeholders through listening, questioning, and presenting
• Effectively collaborate in a team environment

Demonstrate a solid foundation in data management

• Design and implement relational database systems to support computer-based information systems
• Design and implement non-relational database systems to support computer-based information systems
• Demonstrate knowledge of contemporary data management issues

Tuition for the online Bachelor of Science in Applied Computing is a flat fee of $525 per credit whether you live in Wisconsin or out of state.

There are no additional course or program fees, however, textbooks are purchased separately and are not included in tuition. As this is an online program, you will not pay segregated fees—fees in addition to tuition that cover the costs of student-organized activities, facility maintenance, and operations. Also you will not be charged a technology fee as part of this program. If software or special technology is required in one of your courses it will be provided to you and is included in your tuition.

Financial Aid

Financial aid may be available to you as a returning adult student and is awarded by your home campus. Learn more about our campus partners and choosing a home campus .

Your first step when applying for federal and state financial aid is completing the Free Application for Federal Student Aid (FAFSA).

Please check with your home campus regarding minimum credits required to qualify for financial aid as a full- or part-time student.

Veteran Benefits 

Benefits are available to qualifying veterans and those currently serving. Contact your home campus veteran services office for details.

Ways to Pay for Your Degree

As a returning adult student, you may consider the following sources of financial aid to help with the cost of your online degree:

• Grants —award is usually based on financial need. Grants, unlike loans, generally do not have to be repaid.
• Scholarships —usually based on academic merit, financial need, or other criteria, awarded by a wide range of organizations. Scholarships do not need to be repaid.
• Loans —a loan is money you borrow and must pay back with interest. Student loans are available from the federal government, private sources such as a bank or financial institution, or other sources. Federal student loans usually have lower interest rates than private loans, and offer flexible repayment plans.
• Military benefits —aid available to eligible veterans and current members of the military.
• Tuition reimbursement —a benefit offered by companies to their employees to help pay for education. Ask your human resources department if your company offers this benefit.
• E ducation tax benefits —research possible tax benefits with the Internal Revenue Service (IRS).

UW Extended Campus Grants and Scholarships

You may be eligible for a grant or scholarship as a student in a semester-based collaborative program through UW Extended Campus. More information can be found here .

Experience UW Applied Computing

Learn about the applied computing industry, program faculty, read student stories, and more. Explore the blog. 

Program: Applied Bioinformatics, Applied Biotechnology, Applied Computing, Biodiversity Conservation and Management, Cybersecurity, Data Science, Health Information Management and Technology, IT Management

Online Degree and Certificate Programs for the Next Generation of Women In STEM

March 8, 2024 · 3 minute read

‘Dress For STEM’ Celebrates Women in STEM and Brings Awareness to Gender Gap

March 1, 2024 · 6 minute read

Program: Applied Computing

Industry Experts Examine the Current Trends and Career Outlook for the Computing Field

January 24, 2024 · 11 minute read

Academic Director Uses Project Management Background to Help Students With UW Applied Computing Capstone Project

March 23, 2023 · 9 minute read

Meet the Faculty of the UW Bachelor of Science in Applied Computing

August 10, 2022 · 9 minute read

Meet the Advisory Board of the UW Bachelor of Science in Applied Computing

August 10, 2022 · 5 minute read

• Undergraduate open days

Order a prospectus

• Subject areas
• Why study at Manchester Met?
• Chat to our current students

How to apply

• Schools and colleges
• Parents and guardians
• Mature students
• Online learning
• Admissions policies and procedures
• Virtual tour
• Postgraduate open days
• Join us in January 2024
• Find a postgraduate course
• Professional development
• Research study
• Information for employers
• Funding and the levy
• Employer case studies
• Apprenticeship information for students
• Student case studies
• Apprenticeship Research Unit
• How to apply for accommodation
• Living in halls
• Your contract
• Rent a private property
• Course enquiries Ask us a question
• Find your country
• Before you apply
• When you have an offer
• Apply for your visa
• Exchange to Manchester Metropolitan
• Study abroad
• Becoming a partner
• Innovation Work with world-leading academics
• Leadership and growth Get training and support from specialists
• Talent, recruitment and development Connect with our students. Develop your people
• Conferences and events Hire our spaces, technology and facilities
• Championing creative excellence
• Driving economic growth
• Leading sustainability
• Tackling inequalities
• Transforming health
• Case studies
• Research Excellence Framework
• Engaging the public with our research
• Equality, Diversity and Inclusion
• Our commitment to researchers
• Scholarships
• News and events
• Academic partnerships
• Ethics and Governance
• Responsible metrics
• Internationalisation
• Board of Governors
• Vice-Chancellor
• University Executive Group
• Faculty of Arts and Humanities
• Faculty of Business and Law
• Faculty of Health and Education
• Faculty of Science and Engineering
• Professional Services
• Honorary graduates
• Environment
• Equity, Diversity and Inclusion
• Close Search mmu.ac.uk Search
• Undergraduate
• Postgraduate
• Apprenticeships
• Become a degree apprentice
• Accommodation
• Course enquiries
• International
• Study at Manchester Met
• International partnerships
• Business and employers
• Leadership and growth
• Talent, recruitment and development
• Conferences and events
• Our research
• Research Integrity
• Our strategy
• Current students
• BSc (Hons) Applied Computing

Applied Computing

Gain a holistic perspective of IT and computing industry practices by building your expertise in a broad range of subjects.

Course overview

With our BSc (Hons) Applied Computing degree, you’ll gain an insight into IT and computing industry practices. Alongside developing your technical knowledge and skills, you will also gain an understanding of how these skills are applied in an organisational context by learning about systems analysis, project management, software development, the management of data and the administration of information systems.

You’ll start by studying the core technical subjects of programming, computing mathematics, web development, computer architectures and databases, developing your team working and communication skills as you go. You’ll then begin to specialise, studying topics such as IT project management, systems analysis and design, the software development process, computer net...

What you need to know

• When does the course start? September 2024

3 years full-time

4 years with placement

4 years with foundation year

• How many UCAS points do I need? 112-120
• Where will I study this course? Manchester

Features and benefits

“In addition to seeing students on the applied computing course develop their technical skills, it’s also very rewarding to see their communication and teamworking abilities improve through team projects and engaging with industry events. Likewise, observing their project management and time management abilities improve as they work towards becoming young professionals is very gratifying.” Dr Andrew Schofiled BSc (Hons) Applied Computing Programme Leader

Course Information

Alongside gaining specialist technical knowledge, on our BSc (Hons) Applied Computing you’ll develop the analytical, logical thinking, problem-solving and professional skills necessary not just to keep up with this rapidly developing subject area, but to drive it forward.

We provide teaching in specialist labs with high-performance computers and the latest industry-standard software. We also have strong industry links in a city with one of the biggest creative and digital sectors in the UK. All of our computing courses are developed with industry in mind, giving you the skills and knowledge you’ll need in the workplace. This ensures that by the time you leave us, you’ll be well placed to pursue a range of careers across a variety of sectors.

Accreditations, Awards and Endorsements

Endorsement.

The Chartered Institute for IT We are an educational affiliate of the BCS, the chartered professional body for IT in the UK.

Chartered Institute of Information Security The Department of Computing and Mathematics is an academic partner of the Chartered Institute of Information Security (CIISec).

Computer Technology Industry Association (CompTIA) We are an Academy of the Computer Technology Industry Association and deliver their partner programme which provides a pathway for students towards a rewarding, high-growth IT career.

Institute of Coding We are part of the £40m national Institute of Coding, and leading the charge to make coding accessible for all and to train the North West’s future digital workforce.

Teaching Excellence Framework 2023-2027 We have received an overall gold status in the Teaching Excellence Framework (TEF), meaning we're rated as an outstanding university for our student experience.

In Year 1, you will study core disciplines of computing, which typically include an introduction to programming, mathematics for computing, computer architectures, web design and development, and databases. You’ll also develop your study, communication and teamworking skills. Please note that the following list of units is indicative and may be subject to change.

Programming

This unit introduces computer programming in a high-level programming language and includes principles and practice in problem-solving, program design, solution implementation and testing. You will gain practical experience in developing software using industry-standard programming languages and tools.

This unit introduces you to the use of the relational model to structure data for efficient storage and retrieval. You will gain practical experience in the construction and usage of relational databases in an industry-standard relational database management system.

Graduate Skills

This unit introduces a range of skills required for you to succeed both on your degree and into graduate employment. The unit aims to help you to develop your own personal, independent, and proactive studying methodology to be effective life-long learners.

Mathematics for Computing

This unit provides the essential mathematical foundation for further study in computing, covering a variety of applied mathematical topics suitable for a range of computing disciplines.

Team Project

This unit offers you the opportunity to collaborate on a programme-specific team project, collecting ideas from across their first year of study. Working in a team offers you the chance to develop both independent and team working skills, project planning, and helps you prepare for the second year of study.

Web Development

This unit introduces you to the key concepts, standards and technologies that underpin the modern web. You will gain practical experience with contemporary client-side web programming tools and techniques to build websites compliant with widely adopted industry standards.

Computer Architecture

This unit introduces you to the fundamental building blocks of digital systems, including the basic architecture of microprocessors and digital logic. You will learn how microprocessors are programmed at a low level and gain an appreciation for working with numeral systems commonly used in the computing domain (eg: binary).

Study and assessment breakdown

• Year 1    25% lectures, seminars or similar; 75% independent study
• Year 2    25% lectures, seminars or similar; 75% independent study
• Year 3    20% lectures, seminars or similar; 80% independent study  
• Year 1    100% coursework
• Year 2    100% coursework
• Year 3    100% coursework  

Optional foundation year

• Study 30% lectures, seminars or similar; 70% independent study
• Assessment 80% coursework; 20% examination

Placement options

The full-time four-year placement route provides the opportunity to go on a placement for at least 36 weeks, where you’ll get a taste of professional life. A placement not only gives you the opportunity to develop your core skills and experience, but also shows employers that you’re ready to get to work. We offer a wide range of services to help you find the right placement, including employer presentations, advice and fairs. But it’s also up to you – the more proactive you are about applying for placements, the better. This may be subject to availability.

In your second year, you’ll study topics such as IT project management, systems analysis and design, software development processes, computer networks and the implementation of database models. You’ll also start developing your employability by engaging with the computing industry and community. Please note that the following list of units is indicative and may be subject to change.

Thematic Project

Building on the knowledge and skills that you have developed so far on the programme, the thematic project will allow you to further develop your project management, team working and communication skills by applying those skills to a given industry, or research, inspired project. Wider aspects such as social, security, ethical and legal issues will be embedded in the project work where appropriate.

IT Project Management

This unit will introduce the concepts, principles, techniques and tools associated with the management and governance of IT projects. You will learn about the different types of IT system and how projects to design, development and maintain these systems are managed, illustrated using case studies.

Software Development Processes

You will study the software development lifecycle, including the analysis, modelling, specification, design, implementation, testing and maintenance of software systems. You will be able to contrast different project management methodologies, selecting individual techniques to suit the project at hand.

Database Models and Implementation

This unit continues to explore the storage and retrieval of data, furthering your appreciation for the selection, usage and evaluation of different models and architectures of database for different tasks.

Industry and Community Engagement

This unit capitalises on the uniquely vibrant technology community in Manchester, challenging you to curate a portfolio of professional interests within the field by engaging with the local tech community at various events. Through self-reflection, you will develop new interests, and learn to see your course in a wider technological, social and ethical context.

This unit explores the theoretical and practical operation of computer networks, exploring different models and protocols of networking, and an exploration of contemporary problems and challenges in modern networking solutions.

Systems Analysis and Design

In this unit, you will study the theory and practices of systems analysis and design with an emphasis on systems modelling for stakeholder communications and documentation. The steps in the systems analysis and design process are described and appropriate, industry-standard modelling tools are examined.

In your third year, you will have the choice to either go on a placement, where you'll work for a year in industry, or continue directly into your final year of study.

In your final year, you will study topics such as IT systems operations and administration, data governance and management and database architecture and performance. You will also complete a large-scale technical project in line with your own interests. Please note that the following list of units is indicative and may be subject to change.

Synoptic Project

The synoptic project allows you to consolidate your learning from your degree programme into a final project, bringing together ideas and techniques from throughout your study. From this project, you will produce the centrepiece of their degree work portfolio.

Database Architecture and Performance

In this unit, you will learn about various architectures of database systems, such as distributed database systems, and the core principles of scalability, reliability and issues affecting database performance.

Research Methods

This unit will prepare you for the synoptic project by introducing the scientific method and presenting the main approaches to perform, analyse, classify and communicate research.

IT Systems Operation and Administration

In this unit, you will learn about the operational and administrative issues involved in managing and maintaining IT systems. You will also study the core principles of the administration of IT systems.

Data Governance and Management

This unit explores aspects and tasks relating to the life-cycle of data in organisations and the wider issues relating to data handling. You will explore the societal impact of data and models, and contemporary issues relating to the legal and ethical challenges of data storage and processing.

Option units

Mobile computing.

This unit offers you the opportunity to explore the development of applications designed to run on mobile computing devices. You will gain practical experience of using device emulators, frameworks, APIs and libraries for the development of mobile apps that utilise specialist features of mobile devices.

Rapid Applied Problem Solving

This unit challenges you to develop your problem-solving skills under pressure. By participating in a series of rapid, applied problem solving challenges, you will develop core essential skills aimed at bridging the employment gap. You will become well-versed in analysing problems, identifying appropriate algorithms and data structures, creatively solving those problems, implementing and evaluating solutions. You will enhance your employability by practising assessment tasks commonly used in tech industry recruitment.

Research in Computing

The Research in Computing option cluster offers you the opportunity to engage with the Department of Computing and Mathematics’ Centre for Advanced Computational Science, by choosing an option which relates to one of the centre’s main themes. By undertaking a focused research study in a specialist area, you have the opportunity to engage in cutting-edge computational research informed by world-leading theoretical and applied research.

User Experience and Interaction Design

Stressing the importance of a user-centred design approach from the outset, this unit affords you the chance to widen your knowledge of the ways in which humans interact with digital systems and services. It draws upon theories and principles from human-computer interaction, user experience, and interaction design, exploring how these can be applied and evaluated in a variety of technological contexts with the overarching aim of promoting an improved and impactful solution.

Whether you’ve already made your decision about what you want to study, or you’re just considering your options, there are lots of ways you can meet us and find out more about student life at Manchester Met.

• a virtual experience campus tour
• chats with current students

Taught by Experts

Your studies are supported by a department of committed and enthusiastic teachers and researchers, experts in their chosen field.

We often link up with external professionals too, helping to enhance your learning and build valuable connections to the working world.

Entry Requirements

Ucas tariff points.

GCE A levels - grades BBC or equivalent, and to include minimum grade C in one of the following subjects: IT, Computer Science, Mathematics, CCEA Digital Technology, Software Systems Development or a science subject.

Pearson BTEC National Extended Diploma or OCR Cambridge Technical Extended Diploma - grade DMM in IT, Computing or Applied Science. Applicants with a qualification in Engineering will be considered on a case-by-case basis. 

Access to HE Diploma - Pass overall in Computing, IT or Science with a minimum 112 UCAS Tariff points

T level - Overall grade Merit in Digital Production, Design and Development  or Digital Support Services. Applicants with T level in Digital Business Services or Science will be considered on a case-by-case basis.

IB Diploma - Pass overall with a minimum overall score of 28 or minimum 112 UCAS Tariff points from three Higher Level subjects, including HL5 in at least one of IT, Computing. Mathematics or a science subject.

Other Level 3 qualifications equivalent to GCE A level are also considered. The equivalent of A level grade C in a relevant subject will be required as part of any offer.

A maximum of three A level-equivalent qualifications will be accepted towards meeting the UCAS tariff requirement. 

AS levels, or qualifications equivalent to AS level, are not accepted. The Extended Project qualification (EPQ) may be accepted towards entry, in conjunction with two A level-equivalent qualifications.

Please contact the University directly if you are unsure whether you meet the minimum entry requirements for the course.

Specific GCSE Requirements

GCSE grade C/4 in English Language or equivalent, e.g. Pass in Level 2 Functional Skills English

GCSE grade C/4 in Mathematics or equivalent, e.g. Pass in Level 2 Functional Skills Mathematics

International Baccalaureate points

Ielts score required for international students.

There’s further information for international students on our international website if you’re applying with non-UK qualifications.

Fees and Funding

Uk and channel island students.

Full-time fee: £9,250 per year. This tuition fee is agreed subject to UK government policy and parliamentary regulation and may increase each academic year in line with inflation or UK government policy for both new and continuing students.

EU and Non-EU International Students

Full-time fee: £20,000 per year. Tuition fees will remain the same for each year of your course providing you complete it in the normal timeframe (no repeat years or breaks in study).

Additional Information

A degree typically comprises 360 credits, a DipHE 240 credits, a CertHE 120 credits, and an integrated masters 480 credits. The tuition fee for the placement year for those courses that offer this option is £1,850, subject to inflationary increases based on government policy and providing you progress through the course in the normal timeframe (no repeat years or breaks in study). The tuition fee for the study year abroad for those courses that offer this option is £1,385, subject to inflationary increases based on government policy and providing you progress through the course in the normal timeframe (no repeat years or breaks in study).

Additional Costs

Specialist costs.

All of the books required for the course are available from the library. The University also has PC labs and a laptop loan service. However, many students choose to buy some of the core textbooks for the course and/or a laptop. Students may also need to print their assignments and other documents. Campus printing costs start from 5p per page. Estimate costs are £300 for a laptop and up to £100 each year for books and printing.

professional Costs

Students can choose to join the BCS at any point in their study. It is not required but is useful. The annual charge is identified for every year, there is also an option to take course membership, which costs £20 for one year and £30 for four years.

other Costs

Students may incur costs for external storage media, such as USB or HDD drives. Level 5 students are encouraged to attend events as part of the Industry and Community Engagement unit – this may incur some travel costs.

Find out more about financing your studies and whether you may qualify for one of our bursaries and scholarships

First Generation

Dedicated funding and support for first generation students

Career Prospects

Our BSc (Hons) Applied Computing degree will prepare you for a wide range of careers in a fast-growing industry. Potential jobs in computing include systems analyst and architect, IT project manager, IT infrastructure technician, database administrator and web developer, as well as roles managing technology to support a range of public and private sector organisations.

In addition, the skills you learn on this degree are highly valued by a range of employers and opportunities may exist in areas such as e-commerce, search and social media marketing, technology consultation, project management, education and many more.

Manchester is a major hub for the digital technology industry. The close proximity of MediaCityUK and a large number of both established companies and innovative tech start-ups means that the opportunities for technological collaboration are huge. Situated in the ‘Oxford Road Corridor’ innovation district of Manchester, the University and the Department of Computing and Mathematics are perfectly placed to work with key players in the digital technology and new media sectors, making it the perfect destination for your studies and beyond.

Want to know more

Got a question.

You can apply for the full-time option of this course through UCAS.

Institution code: M40

Get advice and support on making a successful application.

You can review our current Terms and Conditions before you make your application. If you are successful with your application, we will send you up to date information alongside your offer letter.

Manchester is your city, be part of it

Your new home, your new city, why university, related courses, computer science, ai and data science, cyber security, software engineering, science and engineering with a foundation year (computing and mathematics route), computer games development, computer animation and visual effects.

Programme Review Our programmes undergo an annual review and major review (normally at 6 year intervals) to ensure an up-to-date curriculum supported by the latest online learning technology. For further information on when we may make changes to our programmes, please see the changes section of our Terms and Conditions .

Important Notice This online prospectus provides an overview of our programmes of study and the University. We regularly update our online prospectus so that our published course information is accurate. Please check back to the online prospectus before making an application to us to access the most up to date information for your chosen course of study.

Confirmation of Regulator The Manchester Metropolitan University is regulated by the Office for Students (OfS). The OfS is the independent regulator of higher education in England. More information on the role of the OfS and its regulatory framework can be found at officeforstudents.org.uk .

All higher education providers registered with the OfS must have a student protection plan in place. The student protection plan sets out what students can expect to happen should a course, campus, or institution close. Access our current Student Protection Plan .

Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

• Post author By bicycle-u
• Post date 08.12.2023

The world of computer science is built on the foundation of problem solving. Whether it’s finding a solution to a complex algorithm or analyzing data to make informed decisions, the problem solving cycle is at the core of every computer science endeavor.

At its essence, problem solving in computer science involves breaking down a complex problem into smaller, more manageable parts. This allows for a systematic approach to finding a solution by analyzing each part individually. The process typically starts with gathering and understanding the data or information related to the problem at hand.

Once the data is collected, computer scientists use various techniques and algorithms to analyze and explore possible solutions. This involves evaluating different approaches and considering factors such as efficiency, accuracy, and scalability. During this analysis phase, it is crucial to think critically and creatively to come up with innovative solutions.

After a thorough analysis, the next step in the problem solving cycle is designing and implementing a solution. This involves creating a detailed plan of action, selecting the appropriate tools and technologies, and writing the necessary code to bring the solution to life. Attention to detail and precision are key in this stage to ensure that the solution functions as intended.

The final step in the problem solving cycle is evaluating the solution and its effectiveness. This includes testing the solution against different scenarios and data sets to ensure its reliability and performance. If any issues or limitations are discovered, adjustments and optimizations are made to improve the solution.

In conclusion, the problem solving cycle is a fundamental process in computer science, involving analysis, data exploration, algorithm development, solution implementation, and evaluation. It is through this cycle that computer scientists are able to tackle complex problems and create innovative solutions that drive progress in the field of computer science.

Understanding the Importance

In computer science, problem solving is a crucial skill that is at the core of the problem solving cycle. The problem solving cycle is a systematic approach to analyzing and solving problems, involving various stages such as problem identification, analysis, algorithm design, implementation, and evaluation. Understanding the importance of this cycle is essential for any computer scientist or programmer.

Data Analysis and Algorithm Design

The first step in the problem solving cycle is problem identification, which involves recognizing and defining the issue at hand. Once the problem is identified, the next crucial step is data analysis. This involves gathering and examining relevant data to gain insights and understand the problem better. Data analysis helps in identifying patterns, trends, and potential solutions.

After data analysis, the next step is algorithm design. An algorithm is a step-by-step procedure or set of rules to solve a problem. Designing an efficient algorithm is crucial as it determines the effectiveness and efficiency of the solution. A well-designed algorithm takes into consideration the constraints, resources, and desired outcomes while implementing the solution.

Implementation and Evaluation

Once the algorithm is designed, the next step in the problem solving cycle is implementation. This involves translating the algorithm into a computer program using a programming language. The implementation phase requires coding skills and expertise in a specific programming language.

After implementation, the solution needs to be evaluated to ensure that it solves the problem effectively. Evaluation involves testing the program and verifying its correctness and efficiency. This step is critical to identify any errors or issues and to make necessary improvements or adjustments.

In conclusion, understanding the importance of the problem solving cycle in computer science is essential for any computer scientist or programmer. It provides a systematic and structured approach to analyze and solve problems, ensuring efficient and effective solutions. By following the problem solving cycle, computer scientists can develop robust algorithms, implement them in efficient programs, and evaluate their solutions to ensure their correctness and efficiency.

Identifying the Problem

In the problem solving cycle in computer science, the first step is to identify the problem that needs to be solved. This step is crucial because without a clear understanding of the problem, it is impossible to find a solution.

Identification of the problem involves a thorough analysis of the given data and understanding the goals of the task at hand. It requires careful examination of the problem statement and any constraints or limitations that may affect the solution.

During the identification phase, the problem is broken down into smaller, more manageable parts. This can involve breaking the problem down into sub-problems or identifying the different aspects or components that need to be addressed.

Identifying the problem also involves considering the resources and tools available for solving it. This may include considering the specific tools and programming languages that are best suited for the problem at hand.

By properly identifying the problem, computer scientists can ensure that they are focused on the right goals and are better equipped to find an effective and efficient solution. It sets the stage for the rest of the problem solving cycle, including the analysis, design, implementation, and evaluation phases.

Gathering the Necessary Data

Before finding a solution to a computer science problem, it is essential to gather the necessary data. Whether it’s writing a program or developing an algorithm, data serves as the backbone of any solution. Without proper data collection and analysis, the problem-solving process can become inefficient and ineffective.

The Importance of Data

In computer science, data is crucial for a variety of reasons. First and foremost, it provides the information needed to understand and define the problem at hand. By analyzing the available data, developers and programmers can gain insights into the nature of the problem and determine the most efficient approach for solving it.

Additionally, data allows for the evaluation of potential solutions. By collecting and organizing relevant data, it becomes possible to compare different algorithms or strategies and select the most suitable one. Data also helps in tracking progress and measuring the effectiveness of the chosen solution.

Data Gathering Process

The process of gathering data involves several steps. Firstly, it is necessary to identify the type of data needed for the particular problem. This may include numerical values, textual information, or other types of data. It is important to determine the sources of data and assess their reliability.

Once the required data has been identified, it needs to be collected. This can be done through various methods, such as surveys, experiments, observations, or by accessing existing data sets. The collected data should be properly organized, ensuring its accuracy and validity.

Data cleaning and preprocessing are vital steps in the data gathering process. This involves removing any irrelevant or erroneous data and transforming it into a suitable format for analysis. Properly cleaned and preprocessed data will help in generating reliable and meaningful insights.

Data Analysis and Interpretation

After gathering and preprocessing the data, the next step is data analysis and interpretation. This involves applying various statistical and analytical methods to uncover patterns, trends, and relationships within the data. By analyzing the data, programmers can gain valuable insights that can inform the development of an effective solution.

During the data analysis process, it is crucial to remain objective and unbiased. The analysis should be based on sound reasoning and logical thinking. It is also important to communicate the findings effectively, using visualizations or summaries to convey the information to stakeholders or fellow developers.

In conclusion, gathering the necessary data is a fundamental step in solving computer science problems. It provides the foundation for understanding the problem, evaluating potential solutions, and tracking progress. By following a systematic and rigorous approach to data gathering and analysis, developers can ensure that their solutions are efficient, effective, and well-informed.

Analyzing the Data

Once you have collected the necessary data, the next step in the problem-solving cycle is to analyze it. Data analysis is a crucial component of computer science, as it helps us understand the problem at hand and develop effective solutions.

To analyze the data, you need to break it down into manageable pieces and examine each piece closely. This process involves identifying patterns, trends, and outliers that may be present in the data. By doing so, you can gain insights into the problem and make informed decisions about the best course of action.

There are several techniques and tools available for data analysis in computer science. Some common methods include statistical analysis, data visualization, and machine learning algorithms. Each approach has its own strengths and limitations, so it’s essential to choose the most appropriate method for the problem you are solving.

Statistical Analysis

Statistical analysis involves using mathematical models and techniques to analyze data. It helps in identifying correlations, distributions, and other statistical properties of the data. By applying statistical tests, you can determine the significance and validity of your findings.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, or maps. It allows for a better understanding of complex data sets and facilitates the communication of findings. Through data visualization, patterns and trends can become more apparent, making it easier to derive meaningful insights.

Machine Learning Algorithms

Machine learning algorithms are powerful tools for analyzing large and complex data sets. These algorithms can automatically detect patterns and relationships in the data, leading to the development of predictive models and solutions. By training the algorithm on a labeled dataset, it can learn from the data and make accurate predictions or classifications.

In conclusion, analyzing the data is a critical step in the problem-solving cycle in computer science. It helps us gain a deeper understanding of the problem and develop effective solutions. Whether through statistical analysis, data visualization, or machine learning algorithms, data analysis plays a vital role in transforming raw data into actionable insights.

Exploring Possible Solutions

Once you have gathered data and completed the analysis, the next step in the problem-solving cycle is to explore possible solutions. This is where the true power of computer science comes into play. With the use of algorithms and the application of scientific principles, computer scientists can develop innovative solutions to complex problems.

During this stage, it is important to consider a variety of potential solutions. This involves brainstorming different ideas and considering their feasibility and potential effectiveness. It may be helpful to consult with colleagues or experts in the field to gather additional insights and perspectives.

Developing an Algorithm

One key aspect of exploring possible solutions is the development of an algorithm. An algorithm is a step-by-step set of instructions that outlines a specific process or procedure. In the context of problem solving in computer science, an algorithm provides a clear roadmap for implementing a solution.

The development of an algorithm requires careful thought and consideration. It is important to break down the problem into smaller, manageable steps and clearly define the inputs and outputs of each step. This allows for the creation of a logical and efficient solution.

Evaluating the Solutions

Once you have developed potential solutions and corresponding algorithms, the next step is to evaluate them. This involves analyzing each solution to determine its strengths, weaknesses, and potential impact. Consider factors such as efficiency, scalability, and resource requirements.

It may be helpful to conduct experiments or simulations to further assess the effectiveness of each solution. This can provide valuable insights and data to support the decision-making process.

Ultimately, the goal of exploring possible solutions is to find the most effective and efficient solution to the problem at hand. By leveraging the power of data, analysis, algorithms, and scientific principles, computer scientists can develop innovative solutions that drive progress and solve complex problems in the world of technology.

Evaluating the Options

Once you have identified potential solutions and algorithms for a problem, the next step in the problem-solving cycle in computer science is to evaluate the options. This evaluation process involves analyzing the potential solutions and algorithms based on various criteria to determine the best course of action.

Consider the Problem

Before evaluating the options, it is important to take a step back and consider the problem at hand. Understand the requirements, constraints, and desired outcomes of the problem. This analysis will help guide the evaluation process.

Analyze the Options

Next, it is crucial to analyze each solution or algorithm option individually. Look at factors such as efficiency, accuracy, ease of implementation, and scalability. Consider whether the solution or algorithm meets the specific requirements of the problem, and if it can be applied to related problems in the future.

Additionally, evaluate the potential risks and drawbacks associated with each option. Consider factors such as cost, time, and resources required for implementation. Assess any potential limitations or trade-offs that may impact the overall effectiveness of the solution or algorithm.

Select the Best Option

Based on the analysis, select the best option that aligns with the specific problem-solving goals. This may involve prioritizing certain criteria or making compromises based on the limitations identified during the evaluation process.

Remember that the best option may not always be the most technically complex or advanced solution. Consider the practicality and feasibility of implementation, as well as the potential impact on the overall system or project.

In conclusion, evaluating the options is a critical step in the problem-solving cycle in computer science. By carefully analyzing the potential solutions and algorithms, considering the problem requirements, and considering the limitations and trade-offs, you can select the best option to solve the problem at hand.

Making a Decision

Decision-making is a critical component in the problem-solving process in computer science. Once you have analyzed the problem, identified the relevant data, and generated a potential solution, it is important to evaluate your options and choose the best course of action.

Consider All Factors

When making a decision, it is important to consider all relevant factors. This includes evaluating the potential benefits and drawbacks of each option, as well as understanding any constraints or limitations that may impact your choice.

In computer science, this may involve analyzing the efficiency of different algorithms or considering the scalability of a proposed solution. It is important to take into account both the short-term and long-term impacts of your decision.

Weigh the Options

Once you have considered all the factors, it is important to weigh the options and determine the best approach. This may involve assigning weights or priorities to different factors based on their importance.

Using techniques such as decision matrices or cost-benefit analysis can help you systematically compare and evaluate different options. By quantifying and assessing the potential risks and rewards, you can make a more informed decision.

Remember: Decision-making in computer science is not purely subjective or based on personal preference. It is crucial to use analytical and logical thinking to select the most optimal solution.

In conclusion, making a decision is a crucial step in the problem-solving process in computer science. By considering all relevant factors and weighing the options using logical analysis, you can choose the best possible solution to a given problem.

Implementing the Solution

Once the problem has been analyzed and a solution has been proposed, the next step in the problem-solving cycle in computer science is implementing the solution. This involves turning the proposed solution into an actual computer program or algorithm that can solve the problem.

In order to implement the solution, computer science professionals need to have a strong understanding of various programming languages and data structures. They need to be able to write code that can manipulate and process data in order to solve the problem at hand.

During the implementation phase, the proposed solution is translated into a series of steps or instructions that a computer can understand and execute. This involves breaking down the problem into smaller sub-problems and designing algorithms to solve each sub-problem.

Computer scientists also need to consider the efficiency of their solution during the implementation phase. They need to ensure that the algorithm they design is able to handle large amounts of data and solve the problem in a reasonable amount of time. This often requires optimization techniques and careful consideration of the data structures used.

Once the code has been written and the algorithm has been implemented, it is important to test and debug the solution. This involves running test cases and checking the output to ensure that the program is working correctly. If any errors or bugs are found, they need to be fixed before the solution can be considered complete.

In conclusion, implementing the solution is a crucial step in the problem-solving cycle in computer science. It requires strong programming skills and a deep understanding of algorithms and data structures. By carefully designing and implementing the solution, computer scientists can solve problems efficiently and effectively.

Testing and Debugging

In computer science, testing and debugging are critical steps in the problem-solving cycle. Testing helps ensure that a program or algorithm is functioning correctly, while debugging analyzes and resolves any issues or bugs that may arise.

Testing involves running a program with specific input data to evaluate its output. This process helps verify that the program produces the expected results and handles different scenarios correctly. It is important to test both the normal and edge cases to ensure the program’s reliability.

Debugging is the process of identifying and fixing errors or bugs in a program. When a program does not produce the expected results or crashes, it is necessary to go through the code to find and fix the problem. This can involve analyzing the program’s logic, checking for syntax errors, and using debugging tools to trace the flow of data and identify the source of the issue.

Data analysis plays a crucial role in both testing and debugging. It helps to identify patterns, anomalies, or inconsistencies in the program’s behavior. By analyzing the data, developers can gain insights into potential issues and make informed decisions on how to improve the program’s performance.

In conclusion, testing and debugging are integral parts of the problem-solving cycle in computer science. Through testing and data analysis, developers can verify the correctness of their programs and identify and resolve any issues that may arise. This ensures that the algorithms and programs developed in computer science are robust, reliable, and efficient.

Iterating for Improvement

In computer science, problem solving often involves iterating through multiple cycles of analysis, solution development, and evaluation. This iterative process allows for continuous improvement in finding the most effective solution to a given problem.

The problem solving cycle starts with problem analysis, where the specific problem is identified and its requirements are understood. This step involves examining the problem from various angles and gathering all relevant information.

Once the problem is properly understood, the next step is to develop an algorithm or a step-by-step plan to solve the problem. This algorithm is a set of instructions that, when followed correctly, will lead to the solution.

After the algorithm is developed, it is implemented in a computer program. This step involves translating the algorithm into a programming language that a computer can understand and execute.

Once the program is implemented, it is then tested and evaluated to ensure that it produces the correct solution. This evaluation step is crucial in identifying any errors or inefficiencies in the program and allows for further improvement.

If any issues or problems are found during testing, the cycle iterates, starting from problem analysis again. This iterative process allows for refinement and improvement of the solution until the desired results are achieved.

Iterating for improvement is a fundamental concept in computer science problem solving. By continually analyzing, developing, and evaluating solutions, computer scientists are able to find the most optimal and efficient approaches to solving problems.

Documenting the Process

Documenting the problem-solving process in computer science is an essential step to ensure that the cycle is repeated successfully. The process involves gathering information, analyzing the problem, and designing a solution.

During the analysis phase, it is crucial to identify the specific problem at hand and break it down into smaller components. This allows for a more targeted approach to finding the solution. Additionally, analyzing the data involved in the problem can provide valuable insights and help in designing an effective solution.

Once the analysis is complete, it is important to document the findings. This documentation can take various forms, such as written reports, diagrams, or even code comments. The goal is to create a record that captures the problem, the analysis, and the proposed solution.

Documenting the process serves several purposes. Firstly, it allows for easy communication and collaboration between team members or future developers. By documenting the problem, analysis, and solution, others can easily understand the thought process behind the solution and potentially build upon it.

Secondly, documenting the process provides an opportunity for reflection and improvement. By reviewing the documentation, developers can identify areas where the problem-solving cycle can be strengthened or optimized. This continuous improvement is crucial in the field of computer science, as new challenges and technologies emerge rapidly.

In conclusion, documenting the problem-solving process is an integral part of the computer science cycle. It allows for effective communication, collaboration, and reflection on the solutions devised. By taking the time to document the process, developers can ensure a more efficient and successful problem-solving experience.

Communicating the Solution

Once the problem solving cycle is complete, it is important to effectively communicate the solution. This involves explaining the analysis, data, and steps taken to arrive at the solution.

Analyzing the Problem

During the problem solving cycle, a thorough analysis of the problem is conducted. This includes understanding the problem statement, gathering relevant data, and identifying any constraints or limitations. It is important to clearly communicate this analysis to ensure that others understand the problem at hand.

Presenting the Solution

The next step in communicating the solution is presenting the actual solution. This should include a detailed explanation of the steps taken to solve the problem, as well as any algorithms or data structures used. It is important to provide clear and concise descriptions of the solution, so that others can understand and reproduce the results.

Overall, effective communication of the solution in computer science is essential to ensure that others can understand and replicate the problem solving process. By clearly explaining the analysis, data, and steps taken, the solution can be communicated in a way that promotes understanding and collaboration within the field of computer science.

Reflecting and Learning

Reflecting and learning are crucial steps in the problem solving cycle in computer science. Once a problem has been solved, it is essential to reflect on the entire process and learn from the experience. This allows for continuous improvement and growth in the field of computer science.

During the reflecting phase, one must analyze and evaluate the problem solving process. This involves reviewing the initial problem statement, understanding the constraints and requirements, and assessing the effectiveness of the chosen algorithm and solution. It is important to consider the efficiency and accuracy of the solution, as well as any potential limitations or areas for optimization.

By reflecting on the problem solving cycle, computer scientists can gain valuable insights into their own strengths and weaknesses. They can identify areas where they excelled and areas where improvement is needed. This self-analysis helps in honing problem solving skills and becoming a better problem solver.

Learning from Mistakes

Mistakes are an integral part of the problem solving cycle, and they provide valuable learning opportunities. When a problem is not successfully solved, it is essential to analyze the reasons behind the failure and learn from them. This involves identifying errors in the algorithm or solution, understanding the underlying concepts or principles that were misunderstood, and finding alternative approaches or strategies.

Failure should not be seen as a setback, but rather as an opportunity for growth. By learning from mistakes, computer scientists can improve their problem solving abilities and expand their knowledge and understanding of computer science. It is through these failures and the subsequent learning process that new ideas and innovations are often born.

Continuous Improvement

Reflecting and learning should not be limited to individual problem solving experiences, but should be an ongoing practice. As computer science is a rapidly evolving field, it is crucial to stay updated with new technologies, algorithms, and problem solving techniques. Continuous learning and improvement contribute to staying competitive and relevant in the field.

Computer scientists can engage in continuous improvement by seeking feedback from peers, participating in research and development activities, attending conferences and workshops, and actively seeking new challenges and problem solving opportunities. This dedication to learning and improvement ensures that one’s problem solving skills remain sharp and effective.

In conclusion, reflecting and learning are integral parts of the problem solving cycle in computer science. They enable computer scientists to refine their problem solving abilities, learn from mistakes, and continuously improve their skills and knowledge. By embracing these steps, computer scientists can stay at the forefront of the ever-changing world of computer science and contribute to its advancements.

Applying Problem Solving in Real Life

In computer science, problem solving is not limited to the realm of programming and algorithms. It is a skill that can be applied to various aspects of our daily lives, helping us to solve problems efficiently and effectively. By using the problem-solving cycle and applying the principles of analysis, data, solution, algorithm, and cycle, we can tackle real-life challenges with confidence and success.

The first step in problem-solving is to analyze the problem at hand. This involves breaking it down into smaller, more manageable parts and identifying the key issues or goals. By understanding the problem thoroughly, we can gain insights into its root causes and potential solutions.

For example, let’s say you’re facing a recurring issue in your daily commute – traffic congestion. By analyzing the problem, you may discover that the main causes are a lack of alternative routes and a lack of communication between drivers. This analysis helps you identify potential solutions such as using navigation apps to find alternate routes or promoting carpooling to reduce the number of vehicles on the road.

Gathering and Analyzing Data

Once we have identified the problem, it is important to gather relevant data to support our analysis. This may involve conducting surveys, collecting statistics, or reviewing existing research. By gathering data, we can make informed decisions and prioritize potential solutions based on their impact and feasibility.

Continuing with the traffic congestion example, you may gather data on the average commute time, the number of vehicles on the road, and the impact of carpooling on congestion levels. This data can help you analyze the problem more accurately and determine the most effective solutions.

Generating and Evaluating Solutions

After analyzing the problem and gathering data, the next step is to generate potential solutions. This can be done through brainstorming, researching best practices, or seeking input from experts. It is important to consider multiple options and think outside the box to find innovative and effective solutions.

For our traffic congestion problem, potential solutions can include implementing a smart traffic management system that optimizes traffic flow or investing in public transportation to incentivize people to leave their cars at home. By evaluating each solution’s potential impact, cost, and feasibility, you can make an informed decision on the best course of action.

Implementing and Iterating

Once a solution has been chosen, it is time to implement it in real life. This may involve developing a plan, allocating resources, and executing the solution. It is important to monitor the progress and collect feedback to learn from the implementation and make necessary adjustments.

For example, if the chosen solution to address traffic congestion is implementing a smart traffic management system, you would work with engineers and transportation authorities to develop and deploy the system. Regular evaluation and iteration of the system’s performance would ensure that it is effective and making a positive impact on reducing congestion.

By applying the problem-solving cycle derived from computer science to real-life situations, we can approach challenges with a systematic and analytical mindset. This can help us make better decisions, improve our problem-solving skills, and ultimately achieve more efficient and effective solutions.

Building Problem Solving Skills

In the field of computer science, problem-solving is a fundamental skill that is crucial for success. Whether you are a computer scientist, programmer, or student, developing strong problem-solving skills will greatly benefit your work and studies. It allows you to approach challenges with a logical and systematic approach, leading to efficient and effective problem resolution.

The Problem Solving Cycle

Problem-solving in computer science involves a cyclical process known as the problem-solving cycle. This cycle consists of several stages, including problem identification, data analysis, solution development, implementation, and evaluation. By following this cycle, computer scientists are able to tackle complex problems and arrive at optimal solutions.

Importance of Data Analysis

Data analysis is a critical step in the problem-solving cycle. It involves gathering and examining relevant data to gain insights and identify patterns that can inform the development of a solution. Without proper data analysis, computer scientists may overlook important information or make unfounded assumptions, leading to subpar solutions.

To effectively analyze data, computer scientists can employ various techniques such as data visualization, statistical analysis, and machine learning algorithms. These tools enable them to extract meaningful information from large datasets and make informed decisions during the problem-solving process.

Developing Effective Solutions

Developing effective solutions requires creativity, critical thinking, and logical reasoning. Computer scientists must evaluate multiple approaches, consider various factors, and assess the feasibility of different solutions. They should also consider potential limitations and trade-offs to ensure that the chosen solution addresses the problem effectively.

Furthermore, collaboration and communication skills are vital when building problem-solving skills. Computer scientists often work in teams and need to effectively communicate their ideas, propose solutions, and address any challenges that arise during the problem-solving process. Strong interpersonal skills facilitate collaboration and enhance problem-solving outcomes.

• Mastering programming languages and algorithms
• Staying updated with technological advancements in the field
• Practicing problem solving through coding challenges and projects
• Seeking feedback and learning from mistakes
• Continuing to learn and improve problem-solving skills

By following these strategies, individuals can strengthen their problem-solving abilities and become more effective computer scientists or programmers. Problem-solving is an essential skill in computer science and plays a central role in driving innovation and advancing the field.

Questions and answers:

What is the problem solving cycle in computer science.

The problem solving cycle in computer science refers to a systematic approach that programmers use to solve problems. It involves several steps, including problem definition, algorithm design, implementation, testing, and debugging.

How important is the problem solving cycle in computer science?

The problem solving cycle is extremely important in computer science as it allows programmers to effectively tackle complex problems and develop efficient solutions. It helps in organizing the thought process and ensures that the problem is approached in a logical and systematic manner.

What are the steps involved in the problem solving cycle?

The problem solving cycle typically consists of the following steps: problem definition and analysis, algorithm design, implementation, testing, and debugging. These steps are repeated as necessary until a satisfactory solution is achieved.

Can you explain the problem definition and analysis step in the problem solving cycle?

During the problem definition and analysis step, the programmer identifies and thoroughly understands the problem that needs to be solved. This involves analyzing the requirements, constraints, and possible inputs and outputs. It is important to have a clear understanding of the problem before proceeding to the next steps.

Why is testing and debugging an important step in the problem solving cycle?

Testing and debugging are important steps in the problem solving cycle because they ensure that the implemented solution functions as intended and is free from errors. Through testing, the programmer can identify and fix any issues or bugs in the code, thereby improving the quality and reliability of the solution.

What is the problem-solving cycle in computer science?

The problem-solving cycle in computer science refers to the systematic approach that computer scientists use to solve problems. It involves various steps, including problem analysis, algorithm design, coding, testing, and debugging.

Related posts:

• The Stages of the Problem Solving Cycle in Cognitive Psychology – Understanding, Planning, Execution, Evaluation, and Reflection
• A Comprehensive Guide to the Problem Solving Cycle in Psychology – Strategies, Techniques, and Applications
• The Step-by-Step Problem Solving Cycle for Effective Solutions
• The Importance of Implementing the Problem Solving Cycle in Education to Foster Critical Thinking and Problem-Solving Skills in Students
• The Importance of the Problem Solving Cycle in Business Studies – Strategies for Success
• The Comprehensive Guide to the Problem Solving Cycle in PDF Format
• A Comprehensive Guide on the Problem Solving Cycle – Step-by-Step Approach with Real-Life Example
• The Seven Essential Steps of the Problem Solving Cycle

• Search UNH.edu
• Search UNH Manchester

Commonly Searched Items:

• Academic Calendar
• Programs of Study
• Graduate Programs
• Academic Advising
• Academic Services
• Course Schedule
• Millyard Scholars Program
• Registration Office
• Departments
• Non-Degree Students
• Student Services
• UNH First Step
• How to Apply
• First-Year Students
• Transfer Students
• International Students
• Graduate Students
• Military & Veteran Students
• Financial Aid
• Tuition & Costs
• Scholarships
• Campus Tours & Events
• Accepted Students
• Career and Professional Success
• Internships
• Career Peers
• Career Technology
• For Employers
• Wildcat Way to Professional Success
• Biotechnology Innovation Center
• NASA STEM on Station
• Undergraduate Research
• Winter Symposium
• About Our City
• Assistant Dean of Student Success
• Campus Safety
• Clubs & Organizations
• Commencement
• Current Students
• Diversity & Inclusion
• Housing & Dining
• Student Engagement
• Student Government
• Wildcats at the YMCA
• Directions & Parking
• Faculty & Staff Resources
• Event Calendar
• STEM Discovery Lab
• Manchester CREATES
• STEM-MoBILE

Applied Computing (Minor)

Add breadth to your degree by gaining the technical skills that employers in all industries look for

What is an applied computing minor?

Fusing the theory and application of computer science, the applied computing minor develops technical knowledge and real-world skills in IT, programming languages, web development and more. Applied computing focuses on critical thinking and problem solving, which prepares students from all majors with skills that can contribute to the mission of virtually any organization.

Why study applied computing at UNH Manchester?

Led by faculty who are experts in the field, the applied computing minor will help you develop the technical skills that employers in all industries look for. From programming to web design to network architecture, you’ll learn foundational knowledge and practical computing skills in a hands-on environment. You’ll also build the critical thinking, technical and problem-solving skills that are valuable in virtually all industries.

Potential careers

• Applications architect
• Big data engineer
• Computer network architect
• Data security analyst
• Database developer
• Full stack developer
• Information security analyst
• Mobile application developer
• Software engineer
• Software systems engineer

Curriculum & Requirements

Program description.

For more information, contact Michael Jonas , minor supervisor.

Requirements for the Program

The minor requires five COMP courses (20 credit hours). Students must earn grades of at least C- in each course and maintain an overall GPA of 2.0 in minor courses. Transfer students may transfer up to two courses, subject to the approval of the minor supervisor. Courses taken on a pass/fail basis may not be used for the minor. No more than 8 credits used by the student to satisfy major requirements may be used in the minor.

Explore Program Details

Faculty directory.

Labs and infrastructure

Two large labs set up with peer programming and shared learning in mind, stocked with:

• 16 Dell Latitude E6420 and14 Dell Latitude E4500 with a dual-booting configuration to run Windows 7 and Fedora 17.
• Additional external USB monitor and keyboard and two mice for each Dell Latitude E6420 to improve collaboration on team projects.
• 60 dedicated Ethernet data ports to allow for network design experiments.
• Wireless access for all 30 client computers and any personal computing device that students bring in.

A spacious server room equipped with:

• Three Dell PowerEdge server computers, Ethernet data ports, and networking gear to provide instructional support for the Computing Technology courses.

A stack of 10 Dell PowerEdge server computers running a Linux server operating system to run experiments in the Capstone Project course.

Four monitoring consoles to optimize system and network administrative operations.

Two server clusters:

• Speech Server Cluster consisting of a stack of 12 Dell PowerEdge servers running Red Hat Linux server operating system to run Speech experiments in the Capstone Project course.
• GPU Computing Cluster is under construction, made possible with a recent grant from NVIDIA, the world leader in visual computing. The state-of-the-art cluster will allow students to analyze medical imagery, explore models of speech and leverage GPU computing and CUDA C/C++ in their courses.

Our lab laptops are powerful development platforms configured to run a large variety of tools and utilities. Visit our  Lab Laptops Software wiki page  for a complete list of installed software products.

• A private cloud of four to eight virtual machines running Windows and Linux server operating systems, managed with VMware vSphere, is updated each semester to meet course instruction and student project needs.
• Server applications and run-time environments (BinNami and XAMPP) are configured to provide MediaWiki, Apache web, and MySQL database services.
• Shared network drives and staging server virtual machines support student project activities.
• OpenComputing  public wiki, set up to share computing resources and document student projects.
• A Balsamiq academic license offers mockup building tools to design user experiences for course projects.
• A Microsoft Developer Network Academic Alliance (MSDN AA) membership gives students access to Microsoft developer and designer tools software.
• Student Experience
• Study Abroad

UNH Manchester

• Advising Appointments
• Advising Forms
• Foundations of Success Track
• Frequently Asked Questions
• Graduate Student Advising
• New Student Advising Guide
• New Student Registration Guide
• Peer and Professional Tutoring
• Become a Peer Tutor
• Math Assessment
• ESOL Programs
• Student Accessibility Services
• Student Wellness
• Course Delivery Modality Glossary
• Grade Rules
• Honors Convocation
• January Term
• Summer Session
• Transcripts
• Check the Status of Your Application
• Great Bay Community College Pathways
• Manchester Community College Pathways
• NHTI Community College Pathways
• Nashua Community College Pathways
• Northern Essex Community College
• White Mountains Community College
• Previously Enrolled Students
• Military & Veteran Services
• Apply for Aid
• Loan Requirements
• New England Regional Student Program
• Undergraduate Research Conference
• Funding Your Research
• Safety and Storm Closing
• Peer Leaders
• Leadership Opportunities
• Rent Our Space
• Business Office
• CAE Faculty Resources
• Event Resources
• Academic Alerts
• Employee Orientation
• Marketing & Communications
• Center for Community Engagement and Experiential Learning
• College Advisory Board
• STEM Programs for Youth
• STEM Programs and Resources for Teachers
• Tech for Teachers Institute
• Request Information

Search form

Critical thinking and problem solving with technology.

Brief Summary: Critical thinking and problem solving is a crucial skill in a technical world that can immediately be applied to academics and careers. A highly skilled individual in this competency can choose the appropriate tool to accomplish a task, easily switch between tools, has a basic understanding of different file types, and can troubleshoot technology when it’s not working properly. They can also differentiate between true information and falsified information online and has basic proficiency in data gathering, processing and interpretation. 

Learners with proficient skills in critical thinking and problem solving should be able to: 

• Troubleshoot computers and mobile devices when issues arise, like restarting the device and checking if it requires a software or operating system update 
• Move across tools to complete a task (for example, adding PowerPoint slides into a note taking app for annotation) 
• Differentiate between legitimate and falsified information online 
• Understand basic file types and know when to use them (for example, the difference between .doc and .pdf files) 

Market/Employer Trends: Employers indicate value in employee ability to problem solve using technology, particularly related to drawing information from data to identify and solve challenges. Further, knowing how to leverage technology tools to see a problem, break it down into manageable pieces, and work toward solving is of important value. Employers expect new employees to be able to navigate across common toolsets, making decisions to use the right tool for the right task.  

Self-Evaluation: 

Key questions for reflection: 

• How comfortable are you when technology doesn’t work the way you expect?  
• Do you know basic troubleshooting skills to solve tech issues?  
• Do you know the key indicators of whether information you read online is reliable? 

Strong digital skills in this area could appear as: 

• Updating your computer after encountering a problem and resolving the issue 
• Discerning legitimate news sources from illegitimate ones to successfully meet goals 
• Converting a PowerPoint presentation into a PDF for easy access for peers who can’t use PowerPoint 
• Taking notes on a phone and seamlessly completing them on a computer

Ways to Upskill: 

Ready to grow your strength in this competency? Try: 

• Reviewing University Libraries’ resources on research and information literacy  
• Read about troubleshooting in college in the Learner Technology Handbook 
• Registering for ESEPSY 1359: Critical Thinking and Collaboration in Online Learning  

Educator Tips to Support Digital Skills: 

• Create an assignment in Carmen prompting students to find legitimate peer-reviewed research  
• Provide links to information literacy resources on research-related assignments or projects for student review 
• Develop assignments that require using more than one tech tool to accomplish a single task 

Computational Thinking

July 28, 2023

Explore the power of computational thinking! Learn how it enhances problem-solving, boosts critical thinking, and prepares you for the future workforce.

Main, P (2023, July 28). Computational Thinking. Retrieved from https://www.structural-learning.com/post/computational-thinking

What is Computational Thinking?

Computational thinking is the mental process of formulating concepts with enough clarity, and in a systematic enough way, that one can tell a computer how to do them. This skill, which is increasingly being recognized as foundational, equips individuals with the ability to approach and solve problems in a logical and systematic manner .

It involves breaking down complex problems into smaller, more manageable parts, abstracting these parts into forms that can be computed, and then using computational tools to compute the solutions.

The integration of computational thinking into education has been found to have significant benefits. For one, it promotes critical thinking and problem-solving skills , equipping learners with the ability to analyze and solve real-world problems more effectively. 

This is particularly valuable in today's highly digitized and connected world, where the ability to understand and manipulate digital systems is increasingly important.

Moreover, computational thinking has a significant impact on future employment opportunities. As technology continues to advance, the demand for individuals with computational thinking skills is growing in various industries. From software development to data analysis, computational thinkers are sought after for their ability to tackle complex problems and develop innovative solutions.

In conclusion, computational thinking is a valuable skill with numerous benefits. By promoting critical thinking and problem-solving skills, it not only enhances an individual's ability to approach and solve problems, but also opens up opportunities for advancement in the increasingly digital job market.

Key Insights:

• Computational thinking is a foundational skill that involves formulating concepts in a way that a computer can understand.
• It promotes critical thinking and problem-solving skills.
• Computational thinking is increasingly important in today's digitized world.
• The demand for individuals with computational thinking skills is growing in various industries.
• Computational thinking opens up opportunities for advancement in the digital job market.

The 4 Cornerstones of Computational Thinking

Computational thinking is a problem-solving mindset that involves applying key concepts and strategies to approach complex problems in a logical and systematic manner. This approach is not limited to computer science or programming; it can be applied to various aspects of our lives.

Computational thinking encompasses four cornerstones that form the foundation of this approach: decomposition, pattern recognition, abstraction, and algorithm design.

By understanding and utilizing these cornerstones, individuals can develop a deeper understanding of problem-solving and enhance their ability to analyze and tackle challenging tasks . In this article, we will explore each of these cornerstones in detail and discuss how they contribute to the development of computational thinking skills.

Decomposition

Decomposition is a fundamental concept in computational thinking that involves breaking down complex problems into smaller, more manageable parts. It is a problem-solving approach that allows individuals to tackle intricate tasks by dividing them into simpler subtasks.

By employing decomposition in computational thinking, individuals can better understand complex problems and find efficient solutions. Breaking down a larger problem into smaller parts enables them to focus on addressing each component individually, making it easier to manage and solve the overall problem.

This process also helps in identifying patterns and relationships among the smaller parts, leading to a deeper understanding of the problem as a whole.

Decomposition plays a crucial role in problem-solving as it enhances critical thinking skills and develops effective strategies . When faced with a complex problem, decomposition allows individuals to prioritize and allocate their time effectively. By dividing the problem into smaller parts, they can allocate time to address each subtask based on its importance and urgency.

Another benefit of decomposition is the opportunity it provides for delegation and collaboration. Breaking down a complex problem into smaller parts enables individuals to distribute the workload among a team , improving efficiency and productivity.

It also fosters teamwork and communication skills as team members work together to solve the problem collectively.

Decomposition is a fundamental component of computational thinking and problem-solving. By breaking down complex problems into smaller, more manageable parts, individuals can develop a deeper understanding of the problem and approach it more effectively.

Decomposition enhances critical thinking, time management, delegation, and collaboration skills , making it an essential skill for problem-solving in various domains.

Pattern Recognition

Pattern recognition is a fundamental aspect of computational thinking and plays a crucial role in problem-solving. It involves the ability to identify similarities and differences in the details of a problem, allowing individuals to simplify complex problems by focusing on the underlying patterns.

The ability to recognize patterns is vital because it helps individuals break down a problem into smaller, more manageable parts. By identifying similarities across different components of a problem, individuals can apply a single solution to multiple instances, saving time and effort. Similarly, recognizing differences between components helps individuals understand the unique aspects of each part and tailor specific solutions accordingly.

Practical activities are an effective way to develop pattern recognition skills. Solving puzzles, participating in escape rooms, or even playing strategy games can help individuals practice identifying recurring patterns or unique elements. These activities provide an opportunity to apply pattern recognition skills in a fun and engaging context, honing problem-solving abilities in the process.

Pattern recognition is an essential aspect of computational thinking and problem-solving. By identifying similarities and differences in the details of a problem, individuals can simplify complex problems and find efficient solutions. Engaging in activities that promote pattern recognition can further enhance these skills, making problem-solving a more intuitive and effective process.

Abstraction

Abstraction is a fundamental concept in computational thinking that involves extracting the most relevant information from decomposed problems and generalizing it to solve the problem as a whole. It allows individuals to focus on the essential aspects of a problem and disregard irrelevant details that may distract from finding a solution.

In the context of pattern recognition, abstraction plays a crucial role in identifying relevant details and disregarding extraneous information. For example, in an escape room, participants are often presented with a series of clues, some of which are red herrings meant to mislead.

By practicing pattern generalization and abstraction, players can distinguish between relevant and irrelevant details, allowing them to solve the puzzle more efficiently.

Developing abstraction skills can begin at a young age, and hands-on activities are a great way to foster this cognitive ability in younger students. Building projects, for instance, require students to break down a complex structure into smaller components and then generalize the principles learned from each component to create a complete and functional project.

By engaging in activities that encourage abstraction, such as escape rooms or building projects , younger students can develop this crucial computational thinking skill. Abstraction not only helps students in problem-solving but also in understanding complex concepts across various disciplines.

As an essential skill for students in STEM subjects , abstraction empowers individuals to think critically and approach real-world problems with confidence and clarity.

Algorithmic Thinking

Algorithmic Thinking is a fundamental concept within Computational Thinking that involves defining a step-by-step solution to a problem that can be replicated for a predictable outcome, whether by humans or computers. It is the process of breaking down a complex task into smaller, manageable steps and organizing them in a logical sequence .

In Algorithmic Thinking, emphasis is placed on the design and structure of algorithms. An algorithm is a set of instructions that helps solve a specific problem or accomplish a particular task. These instructions are typically presented in a clear and unambiguous manner, allowing individuals or computers to follow them precisely.

The ability to think algorithmically is vital in the problem-solving process. It enables individuals to approach challenges systematically and methodically. By breaking down a problem into smaller steps, identifying patterns, and identifying the appropriate sequence of actions, algorithmic thinking helps to simplify complex problems. This structured approach enhances efficiency, accuracy, and effectiveness in finding solutions.

Furthermore, algorithm design is crucial in ensuring that the steps of the solution are well-defined, comprehensive, and optimized. A properly designed algorithm accounts for various scenarios, considering potential errors or exceptions and providing contingency plans. This systematic approach to algorithm design guarantees a more reliable and robust problem-solving process.

Algorithmic Thinking is a key aspect of Computational Thinking that involves creating step-by-step solutions with predictable outcomes. It incorporates careful algorithm design to enhance problem-solving efficiency and accuracy, whether executed by humans or computers.

By developing algorithmic thinking skills , individuals can approach challenges in a structured and systematic manner, ultimately leading to more effective problem-solving.

Computational Thinking and Its Role in Problem-Solving

Computational thinking is a powerful tool that can be applied to a variety of problem-solving scenarios, particularly in the workplace. Here are five fictional examples of how computational thinking has been used to solve complex problems:

• Automating Repetitive Tasks : A data analyst at a tech company used computational thinking to automate a repetitive task of cleaning and organizing large datasets. By breaking down the task into simple steps and writing a script in a programming language , the analyst was able to save hours of manual work each week.
• Optimizing Resource Allocation : A logistics manager at a shipping company used computational thinking to optimize the allocation of trucks for deliveries. By abstracting the problem and using computational tools, the manager was able to find the most efficient routes, reducing fuel costs and delivery times.
• Improving Customer Service : A customer service manager at a retail company used computational thinking to improve the company's response time to customer inquiries. By analyzing patterns in customer complaints and creating an algorithm to prioritize responses, the company was able to improve its customer satisfaction ratings.
• Enhancing Product Design : A product designer at a software company used computational thinking to enhance the design of a new app. By using logical reasoning to understand user needs and preferences, the designer was able to create a more user-friendly interface.
• Predicting Market Trends : A financial analyst at an investment firm used computational thinking to predict market trends. By using computational tools to analyze historical data and identify patterns, the analyst was able to make more accurate predictions about future market movements.

These examples demonstrate the power of computational thinking in solving real-world problems. As Wing (2006) notes, "Computational thinking involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science."

This echoes the sentiment of an expert in the field, who states, "Computational thinking is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability " (Jeannette Wing).

According to a report by the Royal Society, over 60% of new jobs in STEM fields require computational thinking skills and programming experience. This statistic underscores the importance of computational thinking in today's digital age.

• Computational thinking can be used to automate repetitive tasks, optimize resource allocation, improve customer service, enhance product design, and predict market trends.
• Computational thinking involves solving problems, designing systems, and understanding human behavior.
• Over 60% of new jobs in STEM fields require computational thinking skills and programming experience.
• Computational thinking is a fundamental skill for everyone, not just for computer scientists.

Computational Thinking in The Classroom

Computational thinking has become an integral part of the modern classroom, providing a framework for problem-solving that is applicable across a variety of subjects. Here are seven fictional examples of how computational thinking has been used to enhance learning outcomes in classrooms:

• Mathematics: A Year 6 teacher incorporated computational thinking into her lesson on fractions. She encouraged students to break down the problem (decomposition), identify patterns (pattern recognition), and develop a step-by-step solution (algorithmic thinking). This approach helped students understand the concept more deeply and apply it in different contexts.
• Science: In a Year 5 science class studying the water cycle, the teacher used computational thinking to help students understand the process. Students were asked to decompose the cycle into stages, identify the sequence of these stages (algorithmic thinking), and understand the conditions that lead to each stage (abstraction).
• English: A Year 4 English teacher used computational thinking to teach story structure. Students decomposed a story into its basic elements, identified patterns in story structures , and created an algorithm for writing their own stories.
• Geography: In a Year 3 geography lesson on climate zones, the teacher used computational thinking to help students understand the factors that determine a region's climate. Students decomposed the problem by considering each factor individually, identified patterns in how these factors interact, and used this understanding to predict the climate of different regions.
• History: A Year 7 history teacher used computational thinking to help students understand the causes of World War I. Students decomposed the problem by examining each cause individually, identified patterns in how these causes led to the war, and used this understanding to discuss the likelihood of similar events happening in the future.
• Art: In a Year 2 art class, the teacher used computational thinking to teach students about patterns in art. Students decomposed artworks into individual elements, identified patterns in these elements, and used this understanding to create their own patterned artworks .
• Physical Education: A Year 8 PE teacher used computational thinking to help students improve their basketball skills. Students decomposed the skill of shooting a basket into individual movements, identified patterns in successful shots, and used this understanding to improve their own technique.

These examples demonstrate the versatility of computational thinking as a teaching tool . It can be applied across a range of subjects to enhance students' understanding and problem-solving skills.

Relevant Statistic: Although specific statistics on computational thinking in classrooms are limited, a report by Google and Gallup (2016) found that 60% of U.S. K-12 schools have incorporated some form of computer science into their curriculum, indicating a growing emphasis on skills like computational thinking.

Other Practical Applications of Computational Thinking

As we have seen, computational thinking is not limited to computer science or STEM subjects; it has practical applications in everyday life. By using computational thinking skills, individuals can approach problems and make decisions in a more systematic and logical way .

In work settings, computational thinking can enhance problem-solving skills. For instance, when faced with a complex task, breaking it down into smaller, manageable parts allows for a step-by-step solution. This approach helps to identify patterns, recognize relevant information, and design algorithms to achieve efficient results.

In personal life, computational thinking can be applied in various ways. For example, when organizing daily schedules or planning events, breaking down tasks into smaller steps can ensure smooth execution. Computational thinking also aids in decision-making processes by considering various factors, analyzing pros and cons, and making informed choices.

Furthermore, computational thinking can be used in everyday problem-solving scenarios. When confronted with a household issue, such as troubleshooting a malfunctioning appliance, individuals can apply computational thinking principles to identify the problem's root cause, isolate relevant details, and devise a solution.

The real-life applications of computational thinking are vast and diverse. By utilizing problem-solving skills and applying computational thinking, individuals can enhance their everyday lives and make more logical and informed decisions.

How will Computational Thinking Change the Future Workforce?

Computational thinking is not just a skill for computer scientists; it's a skill that every member of the future workforce will need to have. Here are seven ways computational thinking might change the way we work in the future:

• Legal Profession : Lawyers could use computational thinking to analyze large amounts of data in legal cases, identifying patterns and making predictions about outcomes. This could lead to more efficient and effective legal strategies.
• Healthcare : In the healthcare sector, computational thinking could help professionals analyze patient data to predict health outcomes and develop personalized treatment plans . This could lead to improved patient care and outcomes.
• Education : Teachers could use computational thinking to analyze student performance data, identifying patterns and making predictions about student learning outcomes. This could lead to more effective teaching strategies and improved student learning.
• Finance : In the finance sector, computational thinking could help professionals analyze financial data to make predictions about market trends. This could lead to more effective investment strategies and improved financial outcomes.
• Marketing : Marketers could use computational thinking to analyze consumer data, identifying patterns and making predictions about consumer behavior. This could lead to more effective marketing strategies and improved business outcomes.
• Manufacturing : In the manufacturing sector, computational thinking could help professionals analyze production data to optimize manufacturing processes. This could lead to increased efficiency and productivity.
• Transportation : In the transportation sector, computational thinking could help professionals analyze traffic data to optimize routes and schedules. This could lead to improved efficiency and reduced congestion.

According to a study on Education 4.0, the development of computational thinking skills is a key component of preparing students for the 21st-century workforce. As technology continues to advance , the demand for individuals with computational thinking skills is growing in various industries . 

Enhance Learner Outcomes Across Your School

Download an Overview of our Support and Resources

We'll send it over now.

Please fill in the details so we can send over the resources.

What type of school are you?

We'll get you the right resource

Is your school involved in any staff development projects?

Are your colleagues running any research projects or courses?

Do you have any immediate school priorities?

Please check the ones that apply.

Download your resource

Thanks for taking the time to complete this form, submit the form to get the tool.