The Dataquest Download

Level up your data and AI skills, one newsletter at a time.

Each week, the Dataquest Download brings the latest behind-the-scenes developments at Dataquest directly to your inbox. Discover our top tutorial of the week to boost your data skills, get the scoop on any course changes, and pick up a useful tip to apply in your projects. We also spotlight standout projects from our students and share their personal learning journeys.

Hello, Dataquesters!

Here’s what’s in store for you in this edition:

Concept of the Editon: Start your data learning journey with our Defining Data lesson. Learn more

From the Community: Solve a fun Python puzzle, and get inspired by Huynh’s project, Clean and Analyze Employee Exit Surveys. Join the challenge

New Resources: Download our latest R programming cheat sheet. Download PDF

Understanding data is a key step toward making better decisions—whether you’re analyzing trends in a spreadsheet, optimizing business processes, or organizing personal information. Data forms the foundation of modern decision-making, guiding everything from corporate strategies to everyday choices.

This article explores what data is, how it can be classified, and why its quality matters. By understanding these concepts, you’ll gain insight into how to work with data effectively. For a deeper dive into these concepts and hands-on practice, explore the Defining Data lesson on Dataquest.

What Is Data?

Let’s start with a simple definition: data is the plural form of “datum,” which refers to a single fact or observation. For example:

  • Your height (e.g., 5 feet, 9 inches).
  • A purchase amount (e.g., $23.50).
  • A survey response (e.g., “Satisfied”).

When these individual facts are recorded, measured, or collected, they become data. Importantly, if something isn’t documented, it isn’t considered data. Your verbal review of a restaurant, for instance, isn’t data unless it’s written down or recorded. Similarly, personal opinions and beliefs aren’t considered data unless they are supported by recorded evidence.

Types of Data

Data comes in many forms, and classifying it helps us understand how to work with it. Broadly, data can be categorized as:

  • Quantitative data: Numerical and measurable, such as income, age, or temperature.
  • Qualitative data: Descriptive and categorical, such as colors, names, or survey responses.

These classifications also intersect with:

  • Structured data: Organized in a clear format, like rows and columns in a spreadsheet.
  • Unstructured data: Freeform, such as images, text, or audio recordings.

For example, demographic information (like age or marital status) in a dataset about employees might be quantitative or qualitative, while satisfaction ratings from a survey would be structured data.

Confidence in Data

Not all data is equally reliable, so evaluating its quality is essential. Here are some questions to consider:

  • Accuracy: Was the data recorded correctly?
  • Completeness: Are there gaps in the dataset?
  • Timeliness: Is the data up-to-date?

Taking a closer look at these factors can help you ensure that your analysis is built on a solid foundation.

Why Does It Matter?

Once data is classified and its quality assessed, it’s easier to determine how to analyze it effectively. For instance:

  • Quantitative data can help us calculate trends or averages.
  • Qualitative data can reveal patterns or preferences.

In the lesson’s case study, a fictional company, DQ Health, uses employee data to explore questions like:

  • What’s the rate of staff attrition over time?
  • How effective are bonus policies at retaining employees?
  • Which employees are most likely to leave?

By combining quantitative measures (like income and years of experience) with qualitative insights (like satisfaction ratings), organizations can uncover actionable insights.

Ready to Explore More?

Data is everywhere, and learning how to define and classify it is the first step toward using it effectively. If you’re ready to deepen your understanding, the Defining Data lesson on Dataquest is free this week. It’s part of the Junior Data Analyst Career Path, where you can build the skills to confidently analyze and work with data every day.

From the Community

Practice Challenge

You have a dataset represented as a list of dictionaries. Each dictionary contains a name and a score. Write a Python function to find the top 3 scores and return them as a formatted string to create a compelling narrative.

This puzzle comes from learner & Community Moderator Neha Jasani—give it a try and share your solution in the Community!

DQ Resources

📌 [New] R Programming Cheat Sheet: Download our R Programming Cheat Sheet for essential commands in data manipulation, visualization, and analysis. Download PDF

What We're Reading

📖 Work Smarter with the 80/20 Principle
Boost productivity in data analysis by focusing on high-impact tasks and automating repetitive workflows using the 80/20 principle. Read more

📖 Training a Neural Network to Play Tetris
Learn how to train a neural network to play Tetris using Q-Learning and TensorFlow, with over 85,000 game experiences. Read more

📖 6 Reasons to Be (Cautiously) Optimistic About Movies in 2025
A data-driven look at positive trends in cinema, countering the pessimistic narrative about the industry’s decline. A refreshing take for movie enthusiasts! Read more

Project Spotlight

Sharing your projects and reviewing projects from other learners are among the best practices to enhance your skills.

This edition, we spotlight Huynh Hang Thanh Sang‘s project, Clean and Analyze Employee Exit Surveys. He thoroughly explored the data from multiple perspectives, uncovered meaningful insights, and presented them with clear graphs in the FiveThirtyEight style.

Want your project in the spotlight? Share it in the Community.

Give 20%, Get $20: Time to Refer a Friend!

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High-fives from Vik, Celeste, Anna P, Anna S, Anishta, Bruno, Elena, Mike, Daniel, and Brayan.

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