Getting Started with Applied Data Science with Venture Applications:
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Before Applied Data Science at Berkeley, if you don’t have a 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.
- Bootcamp Website
- BIDS Website with training videos
- Python Bootcamp Videos on YouTube
- Code and notebooks on Github
- 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
- Suggestions for Data-X project may be submitted here:
https://goo.gl/forms/h6cAxZS3Il2F0k4F2
Course Materials:
Visit Github for Lectures Materials (Click Here)
- All lectures and code samples will be available at this Github Repository
Coding Questions: Try Stack Overflow and/or simply ask Google
CS Tools Reference Materials:
Ref CS01: Python 3 Quick Reference (download) (weblink), Python 2.7 Quick Reference
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)
Extra Reading & Online Courses:
Ref EROC01 (free): Book: Introduction to Statistical Learning
Ref EROC02: Book: Hands-On Machine Learning with Scikit-Learn and TensorFlow
Ref EROC03: MOOC: Machine Learning, Coursera
Ref EROC04: DataCamp —- Sign up here for free classes (Open until Oct25th)