Table of Contents
Motivation
Learning Data Science can be grueling and overwhelming sometimes.
When I feel too overwhelmed it’s time to draw a picture.
This my current overview of what a data scientist has to do:
General tools
Linear Algebra with numpy – Part 1
Data acquisiton
Data Science Datasets: Iris flower data set
Data cleaning
Data exploration
Feature Scaling
Estimator fit
Linear Regression with sklearn – cheat sheet
Lesson 3: Support Vector Machines
Validation
What is Cross-Validation in Data Science?
k-fold crossvalidation with sklearn
Visualization
Introduction to matplotlib – Part 2
Introduction to matplotlib – Part 3
Interpretation
Metrics about the quality of your model
Classification: Precision and Recall
