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
- See this link for Install Instructions for all necessary tools: Slides for Pre-Install-OSX-v5 (Mac) and Slides for Pre-Install-with-Windows-Annotation-v3 (Windows).
- To get started, first, install the Anaconda Environment which includes a Jupyter Notebook for interactive Python.
- Python Install Examples in Notebook format: Download from Module 0 Folder
Cookbook Code Samples:
Follow this link
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)