Intro to Data for Data Science

Learn the basics of data and how data are used in data science.
Free tutorial
Rating: 4.4 out of 5 (909 ratings)
16,453 students
1hr 1min of on-demand video

Learn what data are and why they are important
Learn how data are used in data science
Learn the types of data that exist in data science
Learn how data are represented in a computer
Learn how to extract information from a table of data
Learn about the data life-cycle from data collection to action


  • None - this is an introductory course for beginners


In an information economy, data is the new oil. However, most people lack even a basic understanding of what data are and how they are used in data science. As a result, many of us will be left behind during the next industrial revolution, an information revolution.

In this course, we will learn about data as a foundation for data science. We’ll learn what data are and why they are important. In addition, we’ll learn about data types, data structures, tabular data, and the data life cycle.

By the end of this course, you’ll understand data and how data are used in data science.

Who this course is for:

  • Beginners
  • IT Professionals


Data Science Consultant
Matthew Renze
  • 4.4 Instructor Rating
  • 1,542 Reviews
  • 33,061 Students
  • 2 Courses

Matthew Renze is a data science consultant, author, and public speaker.

Over the past two decades, he’s taught over 400,000 software developers and IT professionals. He’s delivered over 100 keynotes, presentations, and workshops at conferences on every continent in the world (including Antarctica). His clients range from Fortune 500 companies to small tech startups around the globe.

Matthew is a Microsoft MVP in AI, an ASPInsider, and an author for Pluralsight, Udemy, and Skillshare. He’s also the president of Serenze Global, a 501(c)(3) nonprofit organization whose mission is to improve access to technology education for under-represented individuals in the IT industry.

His focus includes artificial intelligence, data science, and machine learning.

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