All Big Picture Concepts

From Chapter 1 - Introduction to Data Science

The importance of Learning on Your Own
The importance of communication

From Chapter 2 - Mathematical Foundations

Functions and relations
Every table represents a relation.

From Chapter 3 - Jupyter

The structure of Jupyter
How to shut down Jupyter

From Chapter 4 - Review of Python and pandas

Writing to a slice of a DataFrame

From Chapter 5 - Before and After

Explanations before and after code

From Chapter 6 - Single-Table Verbs

The relationship between tall and wide data

From Chapter 7 - Abstraction

The value of abstraction in programming

From Chapter 8 - Version Control

Why people use tools like git

From Chapter 9 - Mathematics and Statistics in Python

Vectorization and its benefits
Models vs. fit models

From Chapter 10 - Visualization

Visualizing relations vs. functions

From Chapter 11 - Processing the Rows of a DataFrame

Informally, map is the same as apply
Important phrases: map-reduce and split-apply-combine

From Chapter 12 - Concatenating and Merging DataFrames

Concat adds rows and merge adds columns (usually!)

From Chapter 13 - Miscellaneous Munging Methods (ETL)

Munging/ETL is a large portion of data work
Information = Data + Context
Summary of key points about missing values

From Chapter 14 - Dashboards

Uses for data dashboards

From Chapter 15 - Relations as Graphs - Network Analysis

A graph depicts a binary relation of a set with itself
How pivoting/melting impacts graph data

From Chapter 16 - Relations as Matrices

What is a recommender system?
The SVD and approximation

From Chapter 17 - Introduction to Machine Learning

Supervised vs. unsupervised machine learning
A central issue: overfitting vs. underfitting
Why we split data into train and test sets