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Please select the start dates for your courses below.

1
Data Science: Computational Thinking with Python

Scheduled Start

2
Data Science: Inferential Thinking through Simulations

Scheduled Start

3
Data Science: Machine Learning and Predictions

Scheduled Start

Program overview

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.

You don’t have to download any software – a browser is all you need. Open up a window and prepare to have some fun.

 

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.

Program Class List

1
Data Science: Computational Thinking with Python

Course Details
Learn the basics of computational thinking, an essential skill in today’s data-driven world, using the popular programming language, Python.

2
Data Science: Inferential Thinking through Simulations

Course Details
Learn how to test hypotheses, draw inferences, and make robust conclusions based on data.

3
Data Science: Machine Learning and Predictions

Course Details
Learn how to use machine learning, with a focus on regression and classification, to automatically identify patterns in your data and make better predictions.

Meet Your Instructors

John DeNero

Giancarlo Teaching Fellow in the EECS Department at UC Berkeley John DeNero is the Giancarlo Teaching Fellow in the UC Berkeley EECS Department. He joined the Cal faculty in 2014 to focus on undergraduate education in computer science and data science. He teaches and co-develops two of the largest courses on campus: introductory computer science for majors (3000 students per year) and introductory data science (1500 students per year).

David Wagner

Professor of Computer Science at UC Berkeley David Wagner is Professor of Computer Science at the University of California at Berkeley. He has published over 100 peer-reviewed papers in the scientific literature and has co-authored two books on encryption and computer security. His research has analyzed and contributed to the security of cellular networks, 802.11 wireless networks, electronic voting systems, and other widely deployed systems.

Ani Adhikari

Teaching Professor of Statistics at UC Berkeley Ani Adhikari, Senior Lecturer in Statistics at UC Berkeley, has received the Distinguished Teaching Award at Berkeley and the Dean's Award for Distinguished Teaching at Stanford University. While her research interests are centered on applications of statistics in the natural sciences, her primary focus has always been on teaching and mentoring students. She teaches courses at all levels and has a particular affinity for teaching statistics to students who have little mathematical preparation. She received her undergraduate degree from the Indian Statistical Institute, and her Ph.D. in Statistics from Berkeley.
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Learning Partner

University of California, Berkeley

Program

Data Science Foundations of Data Science

Program Type

Professional Certificate
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