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Principles and Techniques of Data Science
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Principles and Techniques of Data Science

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Spring 2025 Frequently Asked Questions

Offerings

  1. Fall 2024
  2. Summer 2024
  3. Spring 2024
  4. Fall 2023
  5. Summer 2023
  6. Spring 2023
  7. Fall 2022
  8. Summer 2022
  9. Spring 2022
  10. Fall 2021
  11. Summer 2021
  12. Spring 2021
  13. Fall 2020
  14. Summer 2020
  15. Spring 2020
  16. Fall 2019
  17. Summer 2019
  18. Spring 2019
  19. Fall 2018
  20. Spring 2018
  21. Fall 2017
  22. Spring 2017

Overview

Combining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. This intermediate-level class bridges between Data 8 and upper-division computer science and statistics courses as well as methods courses in other fields. In this class, we explore key areas of data science, including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision-making.​ Through a strong emphasis on data-centric computing, quantitative critical thinking, and exploratory data analysis, this class covers key principles and techniques of data science. These include languages for transforming, querying, and analyzing data; algorithms for machine learning methods, including regression, classification, and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.

Goals

  • Prepare students for advanced Berkeley courses in data-management (CS 186), machine learning (CS 189), and statistics (Stat 154), by providing the necessary foundation and context

  • Enable students to start careers as data scientists by providing experience working with real-world data, tools, and techniques

  • Empower students to apply computational and inferential thinking to tackle real-world problems

Prerequisites

While we are working to make this class widely accessible, we currently require the following (or equivalent) prerequisites:

  1. Foundations of Data Science: Data 8 covers much of the material in Data 100 but at an introductory level. Data8 provides basic exposure to Python programming and working with tabular data, as well as visualization, statistics, and machine learning.

  2. Computing: The Structure and Interpretation of Computer Programs CS 61A or Computational Structures in Data Science CS 88. These courses provide additional background in Python programming (e.g., for loops, lambdas, debugging, and complexity) that will enable Data 100 to focus more on the concepts in Data Science and less on the details of programming in Python.

  3. Math: Linear Algebra (Math 54, EE 16A, or Stat 89A): We will need some basic concepts like linear operators and derivatives to enable statistical inference and derive new prediction algorithms. This may be satisfied concurrently with Data 100.