As a Course

Today, Data, AI, and digital systems are increasingly important in today's applications. However, 'learning the theory' and 'making it work' are not the same. It is essential to actually implement useful systems in real life.

Data-X bridges the gap between theory and practice, by combining state-of-the-art tools, innovation processes. Data-X is powered by the Innovation Engineering framework.

As a Lab

Data-X is a lab for students, researchers, companies, and leading global institutions:

  • For everyone: work collaboratively real-world, applied data, AI, and digital applications
  • For students: develop, applied data science and connect with global firms and organizations
  • For companies: win the war for talent in data, AI, and digital acceleration
  • For global universities: collaborative projects with global students and researchers
  • Bring your data, skills, application ideas to Data-X

Data-X Skills:

  • Implementation: Amazing AI and Data-related projects
  • Tools: Covers the open-source software tools needed for these types of projects
  • Mindset and Process: Develops the necessary mindset and behaviors to deliver innovative projects within a real-life development process
  • Network: Brings together students, faculty, new ventures, and large firms so they can learn from each other in a manner that is both technically deep and yet broad
  • Project Resources: Lectures, code samples, slides, references and everything needed for real-life projects and education all in one place

Who is it for?

Data-X is for anyone interested in careers, new ventures, and innovative projects in areas related to data science and information technology systems.

How is it different?

Taking a purely theoretical course is not enough.  Students often take course after course in technical subject areas without being able to implement, apply, and/or make an innovative impact. Data-X places a real life innovative emerging technology project at the center of a learning experience that includes powerful tools, theory, and innovation behaviors and mindset.  Data-X also builds on Innovation Engineering, a powerful framework for guiding innovation projects.

Contribute to Data-X

Bring your own ideas into an integrative project that can help students pursue new opportunities ranging from starting a new venture to interviewing for industry positions.

Learn more from these articles:

To work with and contribute to Data-X, check out our Collaborate page.

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Data-X was created in 2015 by Sutardja Center faculty director and chief scientist, Ikhlaq Sidhu. Ikhlaq wanted to create a data science course that equipped students with the most relevant skills and approaches that would help them get started with creating innovative data science projects. His idea was to teach students the most relevant industry programming skills, the most commonly used algorithms, and pair this with an experiential data science project so that students would learn to how to execute data science projects in real life.

Since 2015, more than 1,500 students have taken Applied Data Science with Venture Applications, or Data-X for short.


"The opportunity to dive into extensive projects with diverse teams, getting involved with industry mentors, the openness and flexibility of the Profs and GSIs makes the course a must have for everyone interested in data analytics. My two-semester long involvement with the class and the Profs was a significant contributing factor to me being a Data Scientist today."

"I think this class is so awesome because it teaches the tools and concepts that are most commonly used in workplace teams that are involved with data science and applied machine learning."

"135 has to be my favorite of the ML classes at Berkeley. It covers A TON of content.  The course is very application-focused and yet explains the general idea behind concepts."

"DataX is a very rare data science course that prepares students ready to be real world data scientists. has hired great talents from the DataX course, and we are excited to see they are applying the DataX philosophy to challenging machine learning problems, not just from how to code up  deep learning SGD solver,  but also from the business and product perspective, "

-- Victor Fang, Ph.D. , CEO of
Lecture Materials Homework Resources
00: Getting Started
010 Introduction Basics Video Slides Code Code Video References Page
020 Project Guidance Video Slides Code Code Video Project Guidelines
030 Install Instructions Video Slides Code Code Video References Page
01: Fundamentals
100A Predication and Linear Regression Part I Video Slides Code Code Video HW NA
100B Predication and Linear Regression Part II Video Slides Code Code Video HW NA
110 NumPy Video Slides Code Code Video HW-NumPy Introduction to NumPy 101 NumPy Exercises NumPy Cheatsheet
120 Pandas Video Slides Code Code Video HW-Pandas Introduction to Pandas 10 Minutes to Pandas Pandas Cheatsheet
130 X Data Visualization Video Slides Code Code Video HW NA
140 X Logistic Regression and SKlearn (Empty) Video Slides Code Code Video HW List of Resources
160 X Predictive Model (Titanic): Putting it together Video Slides Code Code Video HW NA
170 X ML Algorithm Overview Video Slides Code Code Video HW NA
180 X Cross-Validation and Regularization Video Slides Code Code Video HW NA
X X Video Slides Code Code Video HW NA
02: Data Signals
200 X Correlation Video Slides Code Code Video HW NA
215A Time Series Video Slides Code Video HW NA
215B Time Series Video Code Code Video HW-TS NA
220 X Decision Trees, Information Theory Video Slides Code Code Video HW NA
250 X Spectral Signals Video Slides Code Code Video HW NA
03: Data Handling
310 Web Scraping Video Slides Code Code Video HW NA
320 X Flask Video Slides Code Code Video HW NA
04: Deep Learning
410 Intro to Tensor Flow Video Slides Code Code Video HW NA
420 X Neural Networks Video Slides Code Code Video HW NA
430 X Convolution Neural Networks Video Slides Code Code Video HW NA
05: Natural Language Processing
500 Text Processing Video Slides CodeX Code Video HW NA
510 X Feature Engineering & Text Representation Video Slides Code Code Video HW NA
520 X Learning Models Video Slides Code Code Video HW NA
06: Data-X Library
610 Stock Market Data and Quotes Video Slides Code Code Video HW NA