# Schedule

## Week 1

### Lecture 1: Introduction

Tuesday, Jan. 9What is data science? Why is it important? Who are we? Course overview.

#### Recommended reading

- Cathy O’Neil and Rachel Schutt, Doing Data Science. (2014) Chapter 1.
- David Donoho, 50 years of Data Science. (2015).

### Lecture 2: Introduction to Programming in Python, Version Control

Thursday, Jan. 11Running a Python program, IPython, Jupyter notebooks, variables and data types, operations, functions, scope. Version Control with GIT

## Week 2

### Lecture 3: Introduction to Programming in Python II

Tuesday, Jan. 16Data types and operators, conditions, lists, loops.

### Lecture 4: Introduction to Descriptive Statistics

Thursday, Jan. 18Variable types, basic summary statistics and plotting, covariance and correlation, and confounders.

## Week 3

### Lecture 5: Advanced Data Structures

Tuesday, Jan. 23Sets, dictionaries, pandas series, working with modules.

### Lecture 6: Pandas DataFrames

Thursday, Jan. 25Reading and writing data, pandas data frames, basic plotting.

#### Recommended reading

- Learning the Pandas Library: Python Tools for Data Munging, Analysis, and Visualization. Matt Harrison

## Week 4

### Lecture 7: Temporal Data Analysis and Applications to Stock Analysis

Tuesday, Jan. 30Downloading, cleaning, analyzing, and visualizing stock data

Guest lecturer: Curtis Miller### Lecture 8: Hypothesis Testing and Statistical Inference

Thursday, Feb. 1Bernoulli, Binomial, and Normal distributions, Central Limit Theorem, and Introduction to Hypothesis Testing.

#### Mandatory reading

- Grus, Ch.7

## Week 5

### Lecture 9: Hypothesis Testing and Statistical Inference

Tuesday, Feb. 6Bernoulli, Binomial, and Normal distributions, Central Limit Theorem, and Introduction to Hypothesis Testing.

#### Mandatory reading

- Grus, Ch.7

### Lecture 10: Data Visualization

Thursday, Feb. 8Principles of Data Visualization; Visualization in Python

## Week 6

### Lecture 11: Linear Regression 1

Tuesday, Feb. 13Introduction to ordinary linear regression

#### Recommended reading

- ISLR, Ch. 3