assignment 3 storytelling with open data

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Data Storytelling: How to Effectively Tell a Story with Data

• 23 Nov 2021

When you hear the term “data analysis,” what do you think of? Your mind may jump to scouring spreadsheets, implementing algorithms, and making mathematical calculations—all “hard skills” of data analysis. Yet, hard skills are useless without their soft skill counterparts. It’s not enough to just analyze data; you need to know how to communicate the story it tells in a clear, compelling manner—a skill called data storytelling .

According to the Bureau of Labor Statistics , the demand for research analysts is expected to grow 25 percent between 2020 and 2030, much faster than the average across all industries. Many companies have begun including data storytelling as a required skill in analyst job descriptions, while others have opted to hire for data storyteller positions to supplement their existing analytics teams’ abilities. Possessing the skills to both analyze data and communicate its insights can help you stand out as a well-rounded candidate.

Here’s a primer on the key components of data storytelling, why storytelling is an impactful communication tool, and how to craft a compelling narrative of your own.

Access your free e-book today.

What Is Data Storytelling?

Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context for and inspire action from your audience.

There are three key components to data storytelling:

• Data: Thorough analysis of accurate, complete data serves as the foundation of your data story. Analyzing data using descriptive , diagnostic , predictive , and prescriptive analysis can enable you to understand its full picture.
• Narrative: A verbal or written narrative, also called a storyline, is used to communicate insights gleaned from data, the context surrounding it, and actions you recommend and aim to inspire in your audience.
• Visualizations: Visual representations of your data and narrative can be useful for communicating its story clearly and memorably. These can be charts, graphs, diagrams, pictures, or videos.

Data storytelling can be used internally (for instance, to communicate the need for product improvements based on user data) or externally (for instance, to create a compelling case for buying your product to potential customers).

Related: The Advantages of Data-Driven Decision-Making

The Psychological Power of Storytelling

Humans have told stories since the Cro-Magnon era to communicate with others for survival and record accounts of daily life. While storytelling methods have come a long way since the days of cave paintings, its psychological power holds true tens of thousands of years later.

The brain’s preference for stories over pure data stems from the fact that it takes in so much information every day and needs to determine what’s important to process and remember and what can be discarded.

When someone hears a story, multiple parts of the brain are engaged, including:

• Wernicke’s area, which controls language comprehension
• The amygdala, which processes emotional response
• Mirror neurons, which play a role in empathizing with others

When multiple areas of the brain are engaged, the hippocampus—which stores short-term memories—is more likely to convert the experience of hearing a story into a long-term memory.

Rather than presenting your team with a spreadsheet of data and rattling off numbers, consider how you can engage multiple parts of their brains. Using data storytelling, you can evoke an emotional response on a neural level that can help your points be remembered and acted upon.

How to Craft a Compelling Data Narrative

Data storytelling uses the same narrative elements as any story you’ve read or heard before: characters, setting, conflict, and resolution.

To help illustrate this, imagine you’re a data analyst and just discovered your company’s recent decline in sales has been driven by customers of all genders between the ages of 14 and 23. You find that the drop was caused by a viral social media post highlighting your company’s negative impact on the environment, and craft a narrative using the four key story elements:

• Characters: The players and stakeholders include customers between the ages of 14 and 23, environmentally conscious consumers, and your internal team. This doesn’t need to be part of your presentation, but you should define the key players for yourself beforehand.
• Setting: Set the scene by explaining there’s been a recent drop in sales driven by customers of all genders ages 14 to 23. Use a data visualization to show the decline across audience types and highlight the largest drop in young users.
• Conflict: Describe the root issue: A viral social media post highlighted your company’s negative impact on the environment and caused tens of thousands of young customers to stop using your product. Incorporate research (such as this article in the Harvard Business Review ) about how consumers are more environmentally conscious than ever and how sustainably-marketed products can potentially drive more revenue than their unsustainable counterparts. Remind the team of your company’s current unsustainable manufacturing practices to clarify why customers stopped purchasing your product. Use visualizations here, too.
• Resolution: Propose your solution. Based on this data, you present a long-term goal to pivot to sustainable manufacturing practices. You also center marketing and public relations efforts on making this pivot visible across all audience segments. Use visualizations that show the investment required for sustainable manufacturing practices can pay off in the form of earning customers from the growing environmentally conscious market segment.

If there isn’t a conflict in your data story—for instance, if the data showed your current marketing campaign was driving traffic and exceeding your goal—you can skip that element and go straight to recommending that the current course of action be maintained.

Whatever story the data tells, you can communicate it effectively by formatting your narrative with these elements and walking your audience through each piece with the help of visualizations.

Communicating the Need for Action

Data storytelling can help turn data insights into action. Without effective communication, insights can go unnoticed or unremembered by your audience; both hard and soft skills are crucial for leveraging data to its fullest potential.

Harvard Business School Professor Jan Hammond speaks to this in the online course Business Analytics , one of three courses that make up the Credential of Readiness (CORe) program.

“Always remember that applying analytical techniques to managerial problems requires both art and science,” Hammond says. “Over my career, I’ve learned that it’s the soft skills that are the hardest to master, but they’re critically important.”

Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.

About the Author

Your Definitive Guide To Building Valuable Narratives Through Data Storytelling

Table of Contents

1) What Is Data Storytelling?

2) Data Storytelling Importance

3) Data Storytelling Techniques & Best Practices

4) Data Storytelling Examples

Stories inspire, engage, and have the unique ability to transform statistical information into a compelling narrative that can significantly enhance business success.

By gaining centralized access to company data and presenting it in a visual way that follows a logical path and provides invaluable insights on a particular area or subject, you stand to set yourself apart from your competitors and become a leader in your field.

Here, we’ll explore the unrivaled power of data storytelling in the digital age while looking at a mix of powerful data storytelling examples generated with a modern dashboard creator .

What Is Data Storytelling?

Data storytelling is the process of communicating relevant insights in an understandable and widely accessible format. By using a mix of narratives and visualizations, businesses can put their performance into context and make informed strategic decisions.

Beyond this data storytelling definition, the power of a data story lies in our natural affinity for plotlines and narratives that convey information. By leveraging the right tools, it’s possible to take quantitative metrics or information, arrange it into a logical format, and create a narrative that simplifies complex information, presenting it in a way that engages a particular target audience.

That said, data storytelling is composed of three main elements that make them a much more engaging method than traditional statistics. These are narrative, data, and visuals. In order to tell a good and engaging story, it is necessary to build an interesting narrative that keeps the audience interested in the data. This is possible thanks to the smart use of visualizations that transform the data from just numbers into actionable insights. 

We will look at each of these points more in detail later in the post, now let’s dive into the host of business-boosting benefits coming from telling efficient data stories.

Why Is Data Storytelling Important?

Tales help make sense of the world around us, and this very notion is the beating heart of using data to tell a story.

According to a study performed by Skyword, content that features a mix of words and visuals drives 34% more engagement than text-only articles, blog posts, or whitepapers. You have everything to gain by harnessing the power of data visualization, visual analytics , and using a mix of relevant insights to create a compelling narrative.

Here are the key benefits of knowing how to tell stories with data:

• Inclusion: As mentioned, at a fundamental level, stories help us make sense of a complex and occasionally bewildering world. By using the right data storytelling tools to measure, track, and extract relevant data and place it into a visual format that fits into a narrative based on specific company goals, you will make your analytical information accessible to a wider audience. By doing so, you’ll be able to share important messages in a way that inspires, encouraging buy-in from the right parties or stakeholders as a result.
• Decision: By telling a data story through a powerful KPI dashboard software , you'll be able to drive improved decision-making throughout the organization in several critical areas of the organization. If your audience, whether internal or external to the organization, can follow a narrative and extract the right information from your presentation, they'll gain the insight they need to base their strategies on water-tight data, making the organization more efficient, economical, and successful as a whole.
• Organization: In a world dominated by data, knowing which insights to explore can prove daunting. But by working with the right data storytelling tools, not only is it possible to simplify the analytics process, but you'll also gain the ability to arrange your data in a way that's effective, efficient, and ultimately saves you time. As you're no doubt aware—in business, time is money.
• Action: If you tell stories with data and tailor your presentations to your target audience, you'll drive actionable results. If the person is inspired by what you have to show them, and they understand it on a deep, meaningful level, they will act in the desired way. For instance, if your audience is internal, they may formulate an initiative that helps enhance the company's marketing efforts. Or, if you're presenting to external stakeholders through storytelling with data, you might prompt them to increase their investment.

“Storytelling is the essential human activity. The harder the situation, the more essential it is.” - Tim O’Brien, author

7 Data Storytelling Techniques & Best Practices

It’s clear that storytelling with data is powerful. To place the notion of knowing how to tell stories with data into a practical perspective, here we look at a mix of data storytelling techniques and best practices backed with actionable advice to get you started. Let’s dive into them! 

1) Turn metrics into actionable concepts

As we’ve explored, knowing how to tell a story with data will empower you to turn metrics into actionable concepts or insights.

One of the most effective ways of transforming quantitative data into a results-driven narrative is by working with key performance indicators (KPIs). By harnessing the power of an interactive business intelligence (BI) dashboard, you’ll be able to select the KPIs that align with your core organizational goals, using the perfect mix of graphs, charts, and visuals to build a narrative that brings your data to life.

To get under the skin of this most priceless concept, read our guide to data-driven dashboard presentation .

2) Build a narrative 

Coming back to the first component of the ones we mentioned above, every solid story, regardless of its theme or format, has a definitive plot: a beginning, a middle, and an end. By using data storytelling templates, tools, and platforms, you can populate your plot with the visualise that will drive the narrative forward while conveying your message in the most effective way possible. Just like with any movie or book, your data stories should begin by setting the current scenario, continue by providing insights that lead to the conflict, and finalize with useful recommendations to move forward. 

To improve your processes with plotting, you should sit down in a collaborative environment and consider the primary aim of your data-driven story while outlining the beginning, middle, and end. With your framework firmly in place, you should start to populate your plot with the KPIs and visualizations that not only represent what you have to say but are also most relevant to the data you’re looking to present. By working through your plot logically and fleshing it out with the right visuals you’ll help streamline processes within your organization, increasing efficiency and productivity as a result. Let’s look at the visualization component next. 

3) Define your data sources 

This is probably one of the briefest points discussed in this post, however, it is of utmost importance in the process. Once you have built the skeleton of what your narrative will look like, you need to find the data that will help you tell it as this is the bases for successful analytical storytelling. Here you should prioritize only the data that is beneficial to your analysis. Just like we will see later with the use of visuals, you don’t want to overwhelm your audience with infinite data points. Think carefully about the variables you will use and keep only critical data as this is an excellent way to reduce cognitive load. 

The cognitive load concept is built on the premise that “since the brain can only do so many things at once, we should be intentional about what we ask it to do”. That said, it makes total sense to keep your data points focused. 

4) Choose the right visualizations 

Visuals are the third and last of the relevant components any successful data story should consider. Once you have built an efficient narrative and selected the data sources and KPIs you will use to tell it, it is also time to select the graphic representations that will bring your entire story to life. 

There are a bunch of visuals out there such as bar charts, line charts, tables, and pie charts, just to name a few. While it might sound exciting to benefit from all of them, you need to be careful in the way you decide to use them and when. Not all visuals will work for every purpose. Here it is important to follow specific data visualization techniques to stay on the right path. For instance, using a pie chart to compare more than 3 elements can end up crowding the visual and making it difficult to grasp. Instead, using a bar chart to show how the different elements compare to each other can be way better. 

Another important note when it comes to visuals is to keep them simple. Avoid using any 3D graphics that can make your data harder to understand. While giving dimension to your charts might seem sophisticated, it can also significantly affect the way the visual is perceived. So always keep simplicity as a priority. On that note, keeping a clear color palette throughout the presentation can also make it more cohesive and understandable.

5) Eliminate any clutter 

Another important best practice is to keep your storytelling focused. Think about it as building a presentation. You don’t want to overwhelm your audience with a bunch of text and numbers. This is true for a centralized view of data such as dashboards as well as for a more detailed view on a specific graph. 

On one side, dashboards are extremely useful to build a narrative around your KPIs, however, filling them with too many indicators can also be confusing. Try to only include the ones that will actually provide value to your story and avoid any unnecessary clutter. You can also follow dashboard design best practices to make sure you are on the right path. 

On the other side, charts and graphs can also be cluttered if they are not built correctly. Here you should prioritize only the datasets that you need to make your point. For example, if you want to display the top products by revenue, use only 5 products instead of 10 as too many data points can mislead the actual narrative.  

6) Simplify & make connections

If your organization is informed, well-oiled, and strategic across the board, you will grow, evolve, and boost your profits over time.

By harnessing the power of storytelling through data, you’ll be able to connect the dots, simplifying ideas and making the kind of connections that will give your business a newfound sense of strategic direction.

To squeeze the maximum benefit from your data storytelling efforts, you should focus on creating an interactive dialogue between your insights and your audience, using a mix of historical, real-time, and predictive data to drive your message home, whether for financial reporting processes or strategic development of the company.

Moreover, you should create a balance or harmony between your words and your visuals to make it easier for your audience to make the necessary connections that will result in business-enhancing actions.

The most powerful way of creative data-driven narratives that simplify insights is to take a “ storytelling with data visualization” approach to your efforts. Now, we’re going to explore this invaluable concept in action.

7) Rely on the right data tools 

While it might seem like all the data storytelling techniques and best practices we just mentioned are meant for professionals, that couldn’t be further from the truth. That, if you use the right tools to make it happen. Today, there is a wide range of online data visualization tools that are user-friendly and provide users with the necessary features to generate understandable and interactive stories with their data without the need for any technical skills.

For instance, datapine offers an intuitive dashboard tool with multiple charting options that allow you to create an efficient narrative based on your most important KPIs and data sources. If you are not ready to build your own dashboards, the tool also offers over 80+ templates to help you tell efficient data stories in several industries and functions within minutes. Having access to these kinds of tools makes the storytelling process much more accessible since it empowers users from any level of knowledge to use data for their decision-making process. In time, your entire organization will become data-driven and gain a big competitive advantage. 

Data Storytelling Examples Through Visualizations

As mentioned throughout this post, data visualization storytelling is the best way to share stories with your audience. It’s the glue that binds all of the ideas we’ve mentioned so far. To demonstrate its power, here are two types of strikingly different but equally powerful storytelling with data examples used for building an effective narrative with your insights. 

1. A centralized view with dashboards 

As mentioned earlier in the post, there are two ways in which you can tell your data stories. Dashboards are visual analytical tools that provide users with a centralized view of their most important performance indicators. Each of these KPIs plays a fundamental role in the context of the narrative that is being told. Thanks to these interactive tools, users can navigate and explore the data in an interactive way. Let’s look at two data storytelling examples using dashboards. 

a) Employee Performance Dashboard Example

Primarily used to streamline busy human resources departments, this HR dashboard that focuses on employee performance features a mix of KPIs that build a comprehensive profile around attendance rates, individual productivity, training costs, and overtime hours accrued.

**click to enlarge**

It’s possible to use this dynamic mix of charts, graphs, and graphical information by utilizing HR analytics tools , and build an effective narrative relating to employee performance over a particular time frame, creating a compelling plot that will lead to increased productivity and enhanced economic efficiency as well as and support strategies that will boost staff engagement exponentially.

By looking at this dashboard and related HR KPIs , it’s easy to see how you could build a plot around this perfect storm of insights. Coupled with the data visualizations featured in related HR-based dashboards, the possibilities are seemingly endless; from creating effective HR reports to obtaining a birds-eye view of the whole human resources processes and development.

Featured KPIs:

• Absenteeism Rate
• Overtime Hours
• Training Costs
• Employee Productivity

b) Sales Performance Dashboard 

Our next data story example focuses on the development of a sales department over time. This sales dashboard provides a comprehensive picture of the progress of the department focusing on sales growth, sales targets, ARPU, CAC, and CLV. Sales is one of the most important areas for any business offering a product or service. Therefore, being able to build an understandable story of its performance is critical to ensuring growth and profitability.

By using a mix of current and historical data, this dashboard allows the sales team to clearly understand how critical metrics developed in time and find improvement opportunities to reach their end goals. 

The level of interactivity provided by this data storytelling tool allows for a more complete and intuitive analysis process as the graphs and charts can be easily filtered and explored to discover new insights and support any discussion that might arise in the process. 

Featured KPIs 

• Sales Growth
• Sales Target
• Acquisition Cost

2. KPIs for a detailed view 

As mentioned, dashboards provide a centralized view of the most important KPIs for a business. These KPIs are portrayed with the help of interactive charts that make the data more understandable. We already mentioned a few best practices when it comes to building charts to tell your data stories. Now let’s look at some examples to put their value into perspective. 

a) Compliance Rate KPI

A valued fulfillment-based KPI across industries, this dynamic mix of graphs offers a panoramic snapshot of supplier compliance rates over a particular time frame.

A key component of our procurement dashboard , the compliance rate KPI is a prime example of how powerful an individual visualization can be in communicating vital information and how it can fit into a broader narrative.

It’s possible to place this KPI into the heart of a story surrounding procurement structures, success, and processes, offering a breakdown of compliance per supplier in addition to the company’s overall compliance success rate.

Connected with a tailored mix of our additional top 10 procurement KPIs , it’s possible to develop a story that helps to convey key trends, connect organizational dots, and share actionable insights that drive real change. A prime business report example of data storytelling in action. 

b) Current Ratio

Arguably one of the most important metrics when it comes to financial analytics , the current ratio measures the ability of a company to pay its financial obligations in a short period of time, often within 12 months. 

Now, the current ratio alone would not tell us enough information to build an accurate story or extract any valuable conclusions. This is why this graph template was built with complementary data that provides context to it. The current ratio is calculated by diving the current assets with the current liabilities. Therefore, the chart provides a breakdown of how each of these indicators changed over the course of the last 12 months. This information allows the audience to understand more clearly how the data fluctuated and how it reach its current value. 

Complementing this chart with others in an intuitive financial dashboard will provide a complete picture of a business's financial health and provide the necessary tools to ensure continuous improvement. 

Key Takeaways From Efficient Data Stories 

"You’re never going to kill storytelling because it’s built into the human plan. We come with it.” - Margaret Atwood, author of The Handmaid's Tale

As we reach the end of this practical guide we hope you understand the main principles behind efficient storytelling of data. We covered a list of benefits, best practices, and examples to put the power of this practice into perspective. The key takeaway should be to always keep in mind how narrative, data, and visuals intertwine to build efficient stories that can drive a business forward. Using these three elements correctly will allow you to support your ideas with facts while keeping your audience engaged in the process! 

Industry or niche aside, storytelling with data will propel your business to new and exciting heights. With the help of datapine's 14-day trial , you can start your own data story and embrace the power of data storytelling today!

Data Storytelling

Course syllabus, course syllabus #, course info #.

Learning objectives #

This course teaches you the fundamentals of data visualization and how to communicate effectively with data using Python. With visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

By the end of the semester, you will be able to…

Understand the importance of context and audience

Determine the appropriate type of graph for your situation

Recognize and eliminate the clutter clouding your information

Direct your audience’s attention to the most important parts of your data

Think like a designer and utilize concepts of design in data visualization

Leverage the power of storytelling to help your message resonate with your audience

use Python to analyze, prepare and visualize data

create dashboards with Streamlit and reports in quarto

Where to get help #

If you have a question during lecture, feel free to ask it!

Outside of class, any general questions about course content or assignments should be posted on the Moodle course forum.

Emails should be reserved for questions not appropriate for the public forum. If you email me, please include the name of our course in the subject line.

Check out the Support page for more resources.

Textbooks #

This course is mainly based on the following resources:

Available as E-Books in the HdM library :

“Storytelling with Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic.

“Storytelling mit Daten: Die Grundlagen der effektiven Kommunikation und Visualisierung mit Daten” von Cole Nussbaumer Knaflic.

“Storytelling with Data : Let’s Practice!” by Cole Nussbaumer Knaflic.

Available as free online books:

Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin

Python for Data Analysis, 3E by Wes McKinney

A lot of what you do in this course will involve writing code, and coding is a skill that is best learned by doing. Therefore, as much as possible, you will be working on a variety of tasks and activities throughout each lecture.

Additionally, some lectures will feature application exercises that will be graded.

You are expected to bring a laptop to each class so that you can take part in the in-class exercises.

You will be assigned to a team at the beginning of the semester.

All team members are expected to contribute equally to the completion of the labs and project and you will be asked to evaluate your team members throughout the semester. Failure to adequately contribute to an assignment will result in a penalty to your mark relative to the team’s overall mark.

You are expected to make use of the provided GitHub repository as the central collaborative platform. Commits to this repository will be used as a metric (one of several) of each team member’s relative contribution for each project.

Assessment #

Assessment for the course is comprised of three components:

application exercises

homework assignments

Application exercises #

Parts of some lectures will be dedicated to working on “Application Exercises” (AE). These small exercises will give you an opportunity to practice apply the concepts and code introduced in the readings and lectures.

AEs should be completed and submitted individually .

AEs are due within six days after lecture

The AEs are due within six days after the corresponding lecture. For example, AEs from a Monday lecture would be due Sunday by 11:59 pm.

Because these AEs are for practice, they will be graded based on completion. Successful on-time completion of at least 80% of every AE will result in full credit in the final course grade.

In homeworks (HW), you will apply what you’ve learned during lectures to complete data analysis and visualization‚ tasks using data not covered during lectures.

You may discuss homework assignments with other students; however, homework should be completed and submitted individually .

Homework must be completed in the provided Jupyter Notebooks in your course GitHub-repo and also submitted in Moodle.

The purpose of the project is to apply what you’ve learned throughout the semester to analyze an interesting, data-driven research question. The project will be completed in teams .

More information about the project will be provided during the semester.

The final course grade will be calculated as follows:

The final grade will be determined based on the following thresholds:

Course policies #

Academic integrity #.

TL;DR: Don’t cheat!

All students must adhere to the academic integrity standard. Students affirm their commitment to uphold the values by signing a pledge that states:

I will not lie, cheat, or steal in my academic endeavors;

I will conduct myself honorably in all my endeavors;

I will act if the standard is compromised

Regardless of the course delivery format, it is your responsibility to understand and follow HdM policies regarding academic integrity, including doing one’s own work, following proper citation of sources, and adhering to guidance around group work projects.

Collaboration policy #

Only work that is clearly assigned as team work should be completed collaboratively.

The homework assignments must be completed individually and you are welcomed to discuss the assignment with classmates at a high level (e.g., discuss what’s the best way for approaching a problem, what functions are useful for accomplishing a particular task, etc.). However you may not directly share answers to homework questions (including any code) with anyone other than myself and the teaching assistants.

For the projects , collaboration within teams is not only allowed, but expected. Communication between teams at a high level is also allowed however you may not share code or components of the project across teams.

Policy on sharing and reusing code #

I am well aware that a huge volume of code is available on the web to solve any number of problems.

Unless I explicitly tell you not to use something, the course’s policy is that you may make use of any online resources (e.g. StackOverflow ) but you must explicitly cite where you obtained any code you directly use (or use as inspiration).

Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.

On individual assignments you may not directly share code with another student in this class, and on team assignments you may not directly share code with another team in this class.

Late work policy #

The due dates for assignments are there to help you keep up with the course material. However, I understand that things come up periodically that could make it difficult to submit an assignment by the deadline. Here are the rules for late submissions:

Homeworks may be submitted up to 3 days late. There will be a 25% deduction for each 24-hour period the assignment is late.

There is no late work accepted for application exercises , since these are designed to help you prepare for homeworks.

Project taks may be submitted up to 2 days late. There will be a 25% deduction for each 24-hour period the project task is late.

If there are important circumstances that prevent you from completing a lab or homework assignment by the stated due date, you may email me at kirenz @ hdm-stuttgart . de before the deadline.

6.894 : Interactive Data Visualization

Assignment 2: exploratory data analysis.

In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis.

Step 1: Data Selection

First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. To streamline the assignment, we've pre-selected a number of datasets for you to choose from.

However, if you would like to investigate a different topic and dataset, you are free to do so. If working with a self-selected dataset, please check with the course staff to ensure it is appropriate for the course. Be advised that data collection and preparation (also known as data wrangling ) can be a very tedious and time-consuming process. Be sure you have sufficient time to conduct exploratory analysis, after preparing the data.

After selecting a topic and dataset – but prior to analysis – you should write down an initial set of at least three questions you'd like to investigate.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? Be sure to also perform "sanity checks" for patterns you expect to see!

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc. ) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.

• Final Deliverable

Your final submission should take the form of a Google Docs report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures . We've annotated and graded this example to help you calibrate for the breadth and depth of exploration we're looking for.

Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. You may perform highlighting within the visualization tool itself, or draw annotations on the exported image. To easily export images from Tableau, use the Worksheet > Export > Image... menu item.

The end of your report should include a brief summary of main lessons learned.

Recommended Data Sources

To get up and running quickly with this assignment, we recommend exploring one of the following provided datasets:

World Bank Indicators, 1960–2017 . The World Bank has tracked global human developed by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been formatted to facilitate use with Tableau and other data visualization tools. However, you're also welcome to browse and use the original data by indicator or by country . Click on an indicator category or country to download the CSV file.

Chicago Crimes, 2001–present (click Export to download a CSV file). This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system.

Daily Weather in the U.S., 2017 . This dataset contains daily U.S. weather measurements in 2017, provided by the NOAA Daily Global Historical Climatology Network . This data has been transformed: some weather stations with only sparse measurements have been filtered out. See the accompanying weather.txt for descriptions of each column .

Social mobility in the U.S. . Raj Chetty's group at Harvard studies the factors that contribute to (or hinder) upward mobility in the United States (i.e., will our children earn more than we will). Their work has been extensively featured in The New York Times. This page lists data from all of their papers, broken down by geographic level or by topic. We recommend downloading data in the CSV/Excel format, and encourage you to consider joining multiple datasets from the same paper (under the same heading on the page) for a sufficiently rich exploratory process.

The Yelp Open Dataset provides information about businesses, user reviews, and more from Yelp's database. The data is split into separate files ( business , checkin , photos , review , tip , and user ), and is available in either JSON or SQL format. You might use this to investigate the distributions of scores on Yelp, look at how many reviews users typically leave, or look for regional trends about restaurants. Note that this is a large, structured dataset and you don't need to look at all of the data to answer interesting questions. In order to download the data you will need to enter your email and agree to Yelp's Dataset License .

Additional Data Sources

If you want to investigate datasets other than those recommended above, here are some possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!

• data.boston.gov - City of Boston Open Data
• MassData - State of Masachussets Open Data
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News
• Socrata Open Data
• 17 places to find datasets for data science projects

Visualization Tools

You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau . Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.

• Tableau - Desktop visual analysis software . Available for both Windows and MacOS; register for a free student license.
• Data Transforms in Vega-Lite . A tutorial on the various built-in data transformation operators available in Vega-Lite.
• Data Voyager , a research prototype from the UW Interactive Data Lab, combines a Tableau-style interface with visualization recommendations. Use at your own risk!
• R , using the ggplot2 library or with R's built-in plotting functions.
• Jupyter Notebooks (Python) , using libraries such as Altair or Matplotlib .

Data Wrangling Tools

The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!

Graphical Tools

• Tableau Prep - Tableau provides basic facilities for data import, transformation & blending. Tableau prep is a more sophisticated data preparation tool
• Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
• OpenRefine - A free, open source tool for working with messy data.

Programming Tools

• JavaScript data utilities and/or the Datalib JS library .
• Pandas - Data table and manipulation utilites for Python.
• dplyr - A library for data manipulation in R.
• Or, the programming language and tools of your choice...

The assignment score is out of a maximum of 10 points. Submissions that squarely meet the requirements will receive a score of 8. We will determine scores by judging the breadth and depth of your analysis, whether visualizations meet the expressivenes and effectiveness principles, and how well-written and synthesized your insights are.

We will use the following rubric to grade your assignment. Note, rubric cells may not map exactly to specific point scores.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed exploratory analysis report is due by noon on Wednesday 2/19 . Submit a link to your Google Doc report using this submission form . Please double check your link to ensure it is viewable by others (e.g., try it in an incognito window).

Resubmissions. Resubmissions will be regraded by teaching staff, and you may earn back up to 50% of the points lost in the original submission. To resubmit this assignment, please use this form and follow the same submission process described above. Include a short 1 paragraph description summarizing the changes from the initial submission. Resubmissions without this summary will not be regraded. Resubmissions will be due by 11:59pm on Saturday, 3/14. Slack days may not be applied to extend the resubmission deadline. The teaching staff will only begin to regrade assignments once the Final Project phase begins, so please be patient.

• Due: 12pm, Wed 2/19
• Recommended Datasets
• Example Report
• Visualization & Data Wrangling Tools
• Submission form

• RMIT MATH2404, Assignment 3, Storytelling with open data
• by Isaac Jennings
• Last updated over 1 year ago
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Assignment 3 - Storytelling with Open Data

Road fatalities in australia.

Krishnakanth Srikanth (s3959200)

Introduction

• Road fatalities are the most recent problem raising in recent times. This way, it is being the most impacting factor of the families and individuals.

Total Number of Fatal Accidents in each state of Australia

Total number of fatal accidents in each state of australia based on gender, total number of fatal accidents in australia based on age groups, total number of fatal accidents based on year.

• The data is fetched from (Bureau of Infrastructure and Transport Research Economics, 2023)
• In here, we plot a graph to see the total number of fatalities on road in each state of Australia.
• From the plot, we can see clearly that, New South Wales has recorded the most number of fatalities ( 16771 ) considering the whole continent.
• Victoria stands the second with 11968 number of accidents.
• ACT with 500 recorded the least accident count in Australia.
• Here, we try to find out the number of accidents in each state of Australia based on Gender.
• We could clearly see from the plot that, in every state, Male tops with most fatalities on road.
• Furthermore, we try to observe the age groups of people who are involved in the road accidents.
• The graph depicts that the people in age group 17 to 25 (i.e. the youths) tend to be involved highly on such road fatalities. These can be the result of over speeding and thrills.
• The final plot is to show the variations in the fatalities in Australia over the years.
• Observing the plot, we can see that the count has decreased drastically between 1989 to 1995, then has been uneven with many ups and downs till current date.
• Australian Road Deaths Database (ARDD) (no date) Dataset. Available at: https://data.gov.au/data/dataset/australian-road-deaths-database (Accessed: 29 May 2023).
• Bureau of Infrastructure and Transport Research Economics (2023) Australian Road Deaths Database - Ardd, Bureau of Infrastructure and Transport Research Economics. Available at: https://www.bitre.gov.au/statistics/safety/fatal_road_crash_database (Accessed: 29 May 2023).