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.
Data-X Breadth Perspectives:
Ref B01: Why you’re not getting value from your data science
Syllabus: Click Here
- Before taking this class, if you don’t have working knowledge of Python, you can update your skills with the on-line Python Bootcamp offered by UC Berkeley and the Institute of Data Science. We recommend that you understand the content of at least the first 6 video lectures.
- To get started, first, install the Anaconda Environment which includes a Jupyter Notebook for interactive Python.
- See this Github link for install instructions for all necessary tools including install examples in Jupyter Notebook format
Course Introduction: download from Github here
Starting Fall 2017, all lectures and code samples will be available at this Github Repository
For Spring 2017, Lectures, Homework, and Notebooks are still posted here.
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)