About

Data-X – Public Course Page

Instructor: Ikhlaq Sidhu, IEOR, UC Berkeley (contact)

You can find all the resources and code samples for Data-X on this page.  This content for this course is drawn from open source tools and publicly available materials.

At UC Berkeley, this course is 3 units, limited to 55 students in Spring 2017
Thursdays: 5:00 to 7:59 pm in 3108 Etcheverry Hall

  • Undergrad: 190D, Class Number 33036
  • Grad Section: Class Number INDENG  290 – 003, 33258
  • Location: 3108 Etcheverry Hall
  • Prerequisite: Interested students should have working knowledge of Python in advance of the class, and also should have completed a fundamental probability or statistics course.

In Spring, 2017, the course is run as an experimental section.

Suggestions for Data-X project may be submitted here:
https://goo.gl/forms/h6cAxZS3Il2F0k4F2

Data-X Breadth Perspectives:
Ref B01: Why you’re not getting value from your data science

Resources:

Syllabus: Click Here

Getting Started:


Course Materials:

Lectures: 
Course Introduction (download)
Remaining Lectures, Homework, and Notebooks to be posted here

Cookbook Code Samples:
Follow this link

Coding Questions: Try Stack Overflow and/or simply ask Google

CS Tools Reference Materials:
Ref CS01: Python Quick Reference Guide, Python Review from Data 8
and Python Data Structures for 2.7.
Ref CS02: NumPy Getting Started v1-12
Ref CS03: Pandas in 10 Min
Ref CS04: Pandas-SciPy-Numpy-Cheatsheet
Ref CS05: TensorFlow Getting Started
Ref CS06: SciKitLearn Reference Guide, Algorithm Cheat Sheet
Ref CS07: MatPlotLib Guide
Ref CS08: JSON File Format, JSON Examples

Math Reference List:
Ref M01: Covariance and Correlation
Ref M02: Basic Matrix Math
Ref M03: Gradient Descent
Ref M04: Linear-vs-Logit
Ref M05: Regression Analysis
Ref M06: Markov Chains (simplified)(complete) (Wikipedia)