## 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* : Interested students should have working knowledge of Python in advance of the class, and also should have completed a fundamental probability or statistics course.**Prerequisite**

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:**

, 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.**Before taking this class**- 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 Materials:

**Lectures Materials: **

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.

**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)