Data-X: As a Platform
Data-X is an open platform 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 a common, open platform
- 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.
Unlike Other Courses
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:
"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. AnChain.ai 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 AnChain.ai
|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|
|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|