Introduction to Data Visualization

IN2039: Data Visualization for Decision Making

Alan R. Vazquez

Department of Industrial Engineering

Agenda

1. Introduction to Data Science
2. Three Principles of Data Visualization
3. Storytelling for Data Visualization
4. General Advice for Effective Presentations

Introduction to Data Science

Data science is …

a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from vast amounts of structured and unstructured data.

The scheme of data science

Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.

Business understanding

• Business understanding refers to defining the business problem to be solved.

• The goal is to reframe the business problem as a data science problem.

• Often, reframing the problem and designing a solution is an iterative process.

Data understanding

• If the goal is to solve a business problem, the data that makes up the raw material available from which the solution will be built.

• The available data rarely matches the problem.

• For example, historical data is often collected for purposes unrelated to the current business problem or for no explicit purpose at all.

Our goal is to turn data into information that answers useful questions.

Data types

Text

Images

Video

Audio

Numerical data

Data science methodology is based on numerical data given in tables.

In fact, texts, images, videos or audios are transformed into this format to process them.

In this course, we will assume that the data is in a table.

The Three Principles of Data Visualization

What is data visualization?

“A visualization [of data] is any visual presentation intended to reveal evidence, making the invisible visible.” Alberto Cairo (2015).

https://www.nytimes.com/2019/02/28/learning/whats-going-on-in-this-graph-march-6-2019.html

https://www.nytimes.com/2018/09/18/learning/whats-going-on-in-this-graph-sept-19-2018.html

https://www.nytimes.com/2018/10/16/learning/whats-going-on-in-this-graph-oct-17-2018.html

At its core, data visualization allows you to delve into complex datasets to extract meaningful insights using graphical displays.

Data visualizations are primarily concerned with providing evidence and enabling the audience to explore and draw their own conclusions about what the visualizations reveal about the data.

Data visualization has 3 key principles…

As data scientists, we create data visualizations in order to understand our data and explain our analyses to other people. A plot should have a message, and it’s our job to communicate this message as clearly as possible.

Principle 1: Define the Message or Question

Formulate the question of interest or the message you want to convey.

El mensaje puede ser una pregunta

• What is the key comparison?
• How do we emphasize it?
• Do you have reason to expect that one group/observation might be different?
• Why might your finding about shape matter?
• What additional comparison might add value to the investigation?
• Are there any potentially important features to compare against?

Principle 2: Turn Data into Information

Your graph should use data to convey the message or answer the question. In other words, it should transform data into information.

Enhance your graph with color symbols and text to convey additional information.

Principle 3: Apply Graphic Design Principles

1. Objects are easily identifiable by color.
2. Use direct labels instead of a legend.
3. Elements such as text, lines, and shapes of the same nature should look similar.
4. Balance graphics and text.
5. Be cautious with default settings in visualization software.
6. Use a grid-based layout to organize your visualization.

Don’t limit yourself to simple elements. Enhance your graph with color symbols to convey additional information. If possible, add context with markers and reference labels.

Additionally, include a legend in the graph that describes key features and summarizes its conclusions.

Example

Principle 1: It is cheaper to take Uber than to own a car in four of the five largest cities in the USA.

Weekly cost (in USD) of daily commuting

City	Uber	Car
New York City	142	218
Washington D.C.	96	130
Chicago	77	116
Los Angeles	62	89
Dallas	181	65

The estimated costs are based on a twice-daily 10.4 mile one-way commute at peak times over the course of a week using either the UberPool option (where available) or alternatively UberX vs the cost of using a personal vehicle (including any associated costs of ownership). Study conducted in February 2017.

Principle 2: Turn data into information.

Principle 3: Apply Graphic Design Principles.

“The greatest value of a picture is when it forces us to notice what we never expected to see.” – John W. Tukey.

https://www.storytellingwithdata.com/ is an excellent guide on data visualization.

Activity 1.1 (cooperative mode)

1. Get together with a classmate

2. Aanalyze the five data visualizations in this Google Slides file

3. For each graph, write a short critique (3-4 sentences) using the three principles of data visualization.

4. Upload your document in PDF with your critiques to Canvas.

Storytelling for Data Visualization

Why storytelling matters in data visualization?

• Raw data alone is not enough—insights must be communicated effectively.

• Decision-makers rely on clear, engaging, and meaningful presentations of data.

• Good storytelling connects data to real-world problems and solutions.

For example, a simple bar chart can be enhanced by context and narrative to explain a business trend or social issue.

“The most powerful person in the world is the storyteller.” – Steve Jobs

Four Components of a Data Story

1. Introduction (What’s the problem?)

• Define the problem or question you’re addressing.
• Provide relevant background or context.

2. Data (How will you solve it?)

• Collected, cleaned, and arranged analyzed data.
• Provides objective evidence to support claims.

3. Visuals (What’s your solution?)

• Graphs, charts, and maps help translate numbers into meaningful insights (histograms, box plots, line charts, scatter plots, etc.).
• Highlight key trends, comparisons, and relationships.
• Good design guides the audience to the key message.

4. Narrative (Why does it matter?)

• Puts data into context—explains the significance of findings.
• Answers: Why does this matter? Who is affected? What should be done?

Best practices for storytelling

• Know Your Audience – Tailor the message to decision-makers, engineers, or the public.

• Keep it Simple – Avoid unnecessary complexity; clarity is key.

• Use the Right Visuals – Choose the best chart type for your message.

• Emphasize Key Takeaways – Bold the most important insights.

• Create a Narrative Flow – Guide the audience through the story step by step.

General Advice for Effective Presentations

The advice is based on

https://www.amazon.com/Even-Geek-Speak-Joey-Asher/dp/0978577604

1. Avoid jargon overload

Speak in plain, everyday language your audience understands.

For example:

Clear and Engaging

What Is Lean Manufacturing?

Lean Manufacturing is about finding smarter ways to work. The goal is to make products using less time, less waste, and fewer resources, while keeping customers happy.

Jargon Overload

What Is Lean Manufacturing?

Lean Manufacturing is a systematic approach to process optimization that utilizes value stream mapping to identify and eliminate non–value-added activities.

2. State the main objective

Start your speech with “By [insert what you want to accomplish] you will [insert what your listener wants].”

For example, start with

• “In this presentation, I’ll show you how sensors and data can help you detect problems before they cause failures, keeping your production line running smoothly.”

Instead of

• “In this presentation, I will explain how I used a random forest algorithm to predict when a machine will fail. I’ll describe my model, the data preprocessing steps, and my evaluation metrics.”

3. Present the “so what.”

Don’t dive straight into details — explain why it matters first.

For example, is better to start with

• “When a natural disaster disrupts production, companies lose millions of dollars and fail to deliver on time. Supply chain resilience is about designing systems that can recover quickly from these disruptions, keeping customers satisfied.”

than

• “In this project, I analyzed supplier diversification, lead time variability, and network centrality. We used simulation models to measure disruption propagation in the supply chain network.”

4. Limit your slides. One main idea per slide keeps your message clear and focused.

5. Use the 3-points structure. Come up with no more than three points in support of your main objective. Support each of the points with facts or examples.

6. Practice for clarity, not memorization. Rehearse enough to sound natural and confident.

7. You are the presentation. Visuals in PowerPoint reinforce a speaker’s message and help the audience key points. However, the presenter should always be the focus of the presentation, not the visuals.

8 End with a call to action

Tell your audience what you want them to do next.

Example

Let’s Make Our Plant More Efficient

We’ve seen that improving energy efficiency saves costs, reduces emissions, and strengthens competitiveness.

Now it’s your turn:

1. Identify one process in your area that wastes energy.

2. Propose one small improvement this week.

3. Share your idea in next Monday’s team meeting.

Activity 1.2 (Solo mode and take-home)

Prepare a short presentation (up to 3 minutes) about any topic you’re passionate about.

Guidelines:

• Use slides (PowerPoint, Google Slides, or Canva).
  
• Upload your slides to CANVAS before next class.
  
• A few students will be randomly selected to present live.

We won’t judge your topic. We’re looking for clarity, connection, and passion.

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