Data Science Notes

I have summarized some useful notes covering several machine learning and data mining subjects. These materials are written in a way that is easy to pick up if you have experience in these areas. Some data analysis coding pieces are also provided for quick coding practice.

My Data Science Projects

• Driver Telematics Analysis from Kaggle

Four Steps of Data Analysis

1. Collecting Data

• Data Manipulation using numpy
• SQL Quick Reference
• Data Cleaning using pandas/numpy?
• How to Read Data using Python?

2. Feature Engineering

• Data Splitting and Selection
• Model Selection

3. Modeling

• Statistical Learning Theory
• Brief Introduction to scikit-learn Classifiers in Python
• Brief Introduction to caret Classifiers in R
• SVM
• Outlier Detection Methods
• Kernel Method in Logistic Regression, Linear Regression, and SVM
• Neural Network
• EM Algorithm
• Bayesian Network
• Hidden Markov Model
• Clustering Methods
• Dimension Reduction

4. Data Presentation

• Data Visualization using matplotlib

Math

• Statistics Basics

Programming Techniques

• Python VS R
• Python built-in Data Structure
• SAS Basics
• ggplot2 in R
• C++ Quick Reference
• Implementing Data Structures and Algorithms in C++
• Data Container Comparison between C++ and Python
• Quote Conventions