Welcome to Pathstream
Welcome to Pathstream! You’re about to get a preview of the Pathstream learning experience through our free mini-course: Transform Data Into Stories: An introduction to best practices for data visualization.
But first, let’s watch a quick video to learn a little bit about Pathstream. After you watch, review the key video takeaways.
We’re excited to have you on board!
- This free online course is designed to resemble the Pathstream learning environment.
- You’ll learn through readings, videos, and graphics.
- You’ll be able to test your knowledge throughout the course.
Estimated Completion Time
None. This course is open to everyone!
Through this course, you will:
- Learn best practices to create charts and visuals that enhance the quality and impact of your work.
- Learn how and when to use specific charts, such as column charts, line charts, and scatter charts.
- Transform poorly made visualizations into impactful data stories.
Now let’s dive into why data visualization matters.
Why is data visualization important?
Data visualization translates complex data into visual graphics, enabling people to understand patterns, trends, and insights more intuitively.
For example, a Contract Specialist at Moody’s might use a bar chart to visualize the allocated budget versus actual expenditure for each contract. This simple yet effective tool could reveal areas of overspend or underspend at a glance while helping communicate that information to people unfamiliar with the data.
In a world where data is constantly produced and collected, the ability to visualize data effectively is crucial! Whether you're a Contract Specialist monitoring financial metrics or a Junior Analyst exploring economical trends, data visualization is an indispensable tool in your toolkit.
5 Key Takeaways: How can visualization skills benefit Moody’s employees?
- Enhanced Decision-making
- Effective Business Communication
- Improved Efficiency
- Risk Identification
- Stronger Cross-functional Collaboration
Moody’s responsibilities that may relate to visualizations / business communication:
- Financial Data Associate:
- Review, analyze and interpret critical legal, contingency or other provisions within deal documents (Credit Agreements, Term Sheets, Amendment documents, etc.)
- Business Analyst:
- Independently own and follow up on projects which may include analysis of large data sets/identification of variances, tracking closure of key deliverables, and drafting communications to senior stakeholders.
- Support the identification of thematic trends and anomalies. Proactively investigate potential failure points, escalate risks, and propose solutions.
- Associate Analyst:
- Serve as the support ratings analyst for transactions, present transactions to rating committees and communicate analysis during internal meetings.
- BD Rep:
- Ability to present high-level information as well as detailed demonstrations of products & services.
- Presenting analytical recommendations to rating committees
- Excellent communication skills; demonstrated ability to convey complex concepts and analytical conclusions succinctly and clearly
- Client Services Specialist:
- Provide in-depth education and assistance on product features, functionality and the product technical architecture.
- Communicate effectively and develop excellent working relationships with colleagues within Client Service and in partner teams such as Engineering, Research, Product, etc.
Best Practices for Data Visualization
Data visualization principles are essential for effectively communicating insights to stakeholders. When selecting a data visualization, it's crucial to optimize it in a way that conveys the information clearly and accurately. In this lesson, you will learn and review visualization guidelines and best practices to enhance the effectiveness of your data visualizations.
But before you jump into the data design principles below, you will read about Edward Tufte, the "Leonardo da Vinci of data," who actually pioneered the data visualization guidelines at the heart of this lesson..
Leonardo da Vinci of Data
Edward Tufte (born in 1942) is a statistician, professor, and artist who pioneered many of the data visualization best practices you will learn in this course.
His work has been so influential that The New York Times described him as the "Leonardo da Vinci of data." He is considered a top expert in data visualization, making his work very effective in the field of business and data analytics.
Below are a few of Tufte's most popular guidelines that you can follow to avoid common errors in visualizing your data and instead focus on clarity, concision, and communication of your insights.
Tufte's Data Visualization Guidelines
1. Remove clutter.
Your visualization should display your insight. If there is so much additional information that takes away from the insights or adds to the preattentive cognition of a viewer, you should remove something. When in doubt, keep it simple.
2. Avoid pie charts.
Consider other options that might more clearly convey the desired information. Pie charts tend to confuse the audience. As humans, we are not great at determining the proportions of pie slices in a pie chart. When you want to show some proportion, Tufte recommends you use a column (bar) chart.
3. Little bit of data? Use a table; not a graphic.
If you only have a little bit of data to work with, Tufte recommends you do not use a graphical data visualization to represent the data, but instead use just the table itself.
4. Communicate insights with clarity, precision, and efficiency.
Make sure that the audience is not spending too much time trying to understand your visualization.
5. Avoid distortion.
Present your data honestly. While you have discretion to choose a visualization that represents the data most clearly, you should not choose one visualization over another simply because it supports a point you want to make. Similarly, avoid changing a visualization just to enhance a feature that may not be robust in the data. Consider carefully what visualizations to make and how to present them to avoid bias.
6. Limit your color palette.
A good rule of thumb is that you should not use more than five to seven colors in a single visualization. Additionally, your viewer should be able to distinguish between the different colors or hues you use to represent various aspects of your visualization. Less is more. Don't paint the rainbow.
7. Guide the viewer to think about the substance.
Your viewer should look at your visualization and think about what it is trying to communicate versus the graphic design of the visualization itself. In order to do this, you can adopt all the guidelines above to remove detractors and use several other data visualization design principles to bring focus.
Read the following slide deck on design principles for focus. These principles are used in the industry today to create clear visualizations.
Visualization Best Practices
The final guideline from Tufte's list above references guiding the viewer to appreciate the substance of your visualization. Visualizations should make data easier to understand so that your focus can be on telling a clear story. In the video below, you can review best practices for designing data visualizations.
- Preattentive processing is how the brain notices things subconsciously, and this should be considered to draw attention to the most important parts of a visualization.
- A chart should be able to stand alone and allow a viewer to identify insights without additional information.
- Data in a chart should be displayed accurately to prevent misrepresented numbers.
- Charts should be well-designed to be aesthetically pleasing and maintain simplicity so the data is not overwhelming.
Selecting the Best Data Visualization
Choosing the best data visualization to communicate your data insight is very important. There are many ways you can choose to visualize data, but not all those ways are the optimal way to demonstrate the action or clearly share the information from your data.
Thankfully, in business and data analytics, specifically created visualizations can convey the different actions of your insights.
Continue reading below to see these examples of how each data visualization can be used, as well as an example of each type of visualization.
If you need to compare data, use column charts. Column charts (or bar charts) are best for comparisons of values. In the visualization below, you compare the total revenue and total profit between stores.
Total Revenue and Total Profit for All Stores
SHOW CHANGES (OVER TIME)
Line charts are ideal for showing changes. They are best used to visualize how something happens over time. The visualization below shows the change in revenue and profit at the end of each month in 2019.
Total Revenue and Total Profit for 2019
Scatter charts can be used to show the relationship between two values. The scatter chart below shows the relationship between the number of cell phones sold and the revenue for each day.
Daily Revenue and Daily Cell Phone Sales
Tables are great for including detail. Sometimes you need more than a simple visualization. The following table includes the details of the orders of each customer.
| OrderID|| Date||CustomerName|
| 12345|| 12/16/18||Franco|
| 12346|| 12/17/18||Lauren|
| 12347|| 12/18/18||Jorge|
| 12348|| 12/19/18||Esmeralda|
Once you've selected the best data visualization to communicate your insight, you can apply the design principles you have learned to be able to create simple yet effective data visualizations.
Recap of Best Data Visualization Practices
- Visualizations should make data easier to understand, so keep it simple and focus on the one or two messages you are trying to convey to your audience.
- Communicate complex insights by thinking like a visual designer and applying data visualization guidelines:
- Remove clutter.
- Avoid pie charts.
- For a small amount of data, use a table; not a graphic.
- Communicate insights with clarity, precision, and efficiency.
- Avoid distortion.
- Limit your color palette.
- Guide the viewer to think about the substance.
- To build the most impactful visualizations, designers should consider two key factors among others: preattentive cognition (or preattentive processing) and accuracy.
- Choosing the best data visualization to communicate your data insight is very important, because data can easily be miscommunicated. The following table suggests options for choosing an appropriate data visualization according to its intended purpose:
| If the action you want to convey is to:||Consider using a:|
| Compare data||Column/bar chart|
| Show changes (over time)||Line chart|
| Show relationships||Scatter chart|
| Include detail||Table|
While these practices can help guide you, there is no perfect way to create a chart. Don't get overwhelmed with the idea of generating the perfect chart, as there are many variations that can result in the same insight. Remember to keep it simple and focus on the one or two messages you are trying to convey to your audience.
If you'd like to read more about best practices for data visualizations, the articles below contain additional guidelines and examples.
OPTIONAL RESOURCES: MORE BEST PRACTICES
Unlock Your Potential With Pathstream
Did you enjoy our free “Transform Data Into Stories: An introduction to best practices for data visualization” course?
To enroll in our program and continue learning, click the button below.
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Initiate the conversation with your manager
- Discuss and obtain approval from your manager; AND
- Obtain any “pre-approvals” from the Business and People team.
To make things easier, we wrote an email template you can send to your manager. It’s designed to help you communicate the program's value and gain support for your learning journey. We hope it’s helpful!
"ASK YOUR MANAGER" KIT
Subject Line: I’d Like to Enroll In The NYU Data Quality and Data Governance Certificate Program
Recently, I completed a free course called “Transform Data Into Stories: An introduction to best practices for data visualization” offered through Pathstream, a Moody’s educational partner. I learned some valuable data visualization best practices, and I’m excited to learn more e about data analysis and visualization.
Following this experience, I am excited to propose enrolling in the full NYU Data Quality and Data Governance Certificate Program. This 27-week program will equip with me with the skills and knowledge to:
- Help drive better business decisions based on actionable insights pulled from company data and optimize business performance
- Identify more efficient ways of organizing and storing data
- Communicate data, insights, and recommendations more clearly
The program is also self-paced and can be done without interrupting work. I’ve attached a syllabus that breaks down what is taught in each course, the projects I will work on, and the skills I will be equipped with after completing the certificate.
The next cohort starts July 20th. If I enroll now, I can take advantage of the tuition reimbursement special, which reduces my initial upfront costs by 60%.
I believe this program can improve my confidence and contributions to our team. After you have a chance to review the syllabus, let me know what you think! I can set up a time to discuss the program and the possibility of tuition reimbursement as per Moody’s policy.