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Foundations of Data Science

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Foundations of Data Science
Berkeley Online Course Highlights
  • 2-4 months long
  • 64 – 96 hours of effort
  • Learn for FREE, Up-gradable
  • Self-Paced
  • Taught by: John DeNero, David Wagner, Ani Adhikari
  • View Course Syllabus

Online Course Details:

This Professional Certificate gives you a new lens to explore the issues and problems that you care about. You’ll learn how to combine data with Python programming skills to ask questions and explore problems that you encounter in any field of study, in a future job, and even in everyday life.

This program will help you become a data scientist by teaching you how to analyze a diverse array of real data sets including economic data, geographic data and social networks. Typically, the information will be incomplete and there will be some uncertainty involved. You will then study inference, which will help you quantify uncertainty and measure the accuracy of your estimates. Finally, you will put all of your knowledge together and learn about prediction using machine learning.

The program focuses on a set of core concepts and techniques that have broad applicability. Unlike “bootcamps” for programmers, it presents data science as a way of thinking, in which interpretation and communication are as important as computation and statistical methods.

We all have to be able to think critically and make decisions based on data. Thus, the program aims to make data science accessible to everyone.

It is designed specifically for students who have not previously taken statistics or computer science courses. No prior programming experience is needed. The program is based on Data 8, Berkeley’s fastest-growing class, taken by 1,000+ students each semester.

Courses:

Foundations of Data Science: Computational Thinking with Python
  • 4–6 hours per week, for 5 weeks
  • Learn the basics of computational thinking, an essential skill in today’s data-driven world, using the popular programming language, Python.
Foundations of Data Science: Inferential Thinking by Re-sampling
  • 4–6 hours per week, for 5 weeks
  • Learn how to use inferential thinking to make conclusions about unknowns based on data in random samples.
Foundations of Data Science: Prediction and Machine Learning
  • 4–6 hours per week, for 6 weeks
  • Learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions.

What you will learn:

  • How to think critically about data and draw robust conclusions based on incomplete information.
  • Computational thinking and skills, including the Python 3 programming language for visualizing and analyzing data.
  • How to make predictions based on machine learning.
  • How to interpret and communicate data and results using a vast array of real-world examples.

Take This Online Course