Data Science: Computational Thinking with Python

Course 1 of 3: Professional Certificate® in the Foundations of Data Science 5 Weeks 4–6 hours per week

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About this course

We live in an era of unprecedented access to data. Understanding how to organize and leverage the vast amounts of information at our disposal are critical skills that allow us to infer upon the world and make informed decisions. This course will introduce you to such skills.

To work with large amounts of data, you will need to harness the power of computation through programming. This course teaches you basic programming skills for manipulating data. You will learn how to use Python to organize and manipulate data in tables, and to visualize data effectively. No prior experience with programming or Python is needed, nor is any statistics background necessary.

The examples given in the course involve real world data from diverse settings. Not all data is numerical – you will work with different types of data from a variety of domains. Though the term “data science” is relatively new, the fundamental ideas of data science are not. The course includes powerful examples that span the centuries from the Victorian era to the present day.

This course emphasizes learning through doing: you will work on large real-world data sets through interactive assignments to apply the skills you learn. Throughout, the underlying thread is that data science is a way of thinking, not just an assortment of methods. You will also hone your interpretation and communication skills, which are essential skills for data scientists.

What you’ll learn

  • Basics of the Python programming language, and how to use it as a tool for data analysis
  • Tools widely used by industry and academic data scientists, such as Jupyter Notebooks
  • How to use computation to help your data tell a story
  • Fundamental principles and methods of visualization

Meet Your Instructors

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.

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.

Experience Level


Learning Partner

University of California, Berkeley

Program Type

Professional Certificate


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