# Schedule

## Homework

#### Homework 0 - Description | Submission

#### Homework 1 - Description | Submission

#### Homework 2 - Description | Submission

#### Homework 3 - Description | Submission

#### Homework 4 - Description | Submission

#### Homework 5 - Description | Submission

#### Homework 6 - Description | Submission

#### Homework 7 - Description | Submission

#### Project Proposal - Description | Submission

#### Project Milestone Report - Description | Submission

#### Homework 8 - Description | Submission

#### Final Project Report - Description | Submission

## Lectures

### Lecture 1: Introduction

What is data science? Why is it important? Who are we? Course overview and syllabus.

#### Recommended reading

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

#### Lecture Video: Youtube

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

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

#### Lecture Video: Youtube

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

Data types and operators, conditions, lists, loops.

#### Lecture Video: Youtube

### Lecture 4: Introduction to Descriptive Statistics

Variable types, basic summary statistics and plotting, covariance and correlation, confounders, probability: Bernoulli, Binomial, and Normal distributions.

### Lecture 5: Advanced Data Structures

Sets, dictionaries, pandas series, working with modules.

### Lecture 6: Pandas DataFrames

Reading and writing data from files, pandas data frames, basic plotting.

#### Recommended reading

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

### Lecture 7: Hypothesis Testing and Statistical Inference

Introduction to Hypothesis Testing, Central Limit Theorem, A/B testing.

#### Mandatory reading

### Lecture 8: Temporal Data Analysis and Applications to Stock Analysis

Downloading, cleaning, analyzing, and visualizing stock data.

### Lecture 9: Linear Regression 1

Introduction to simple linear regression, multiple linear regression, exploratory vs. inferential viewpoints

#### Recommended reading

- G. James, D. Witten, T Hastie, and R. Tibshirani, An Introduction to Statistical Learning (ISL) (2015) Ch. 3

### Lecture 10: Linear Regression 2

Model generalizability, cross validation, and using categorical variables in regression

#### Recommended reading

- ISL, Ch. 3

### Lecture 11: Ethics

What are the social impacts of computing technology such as personal privacy, intellectual property, interface usability, accessibility, and reliability. What are scenarios where pervasive use of automated systems can and has disproportionately and negatively impacted some groups more than others? What are solutions to mitigate these effects?

#### Recommended reading

### Lecture 12: Data Visualization 1

Principles of Data Visualization.

### Lecture 13: Data Visualization 2

Data Visualization in Python with Matplotlib, Seaboarn, Altair.

### Lecture 14: Data Visualization 3

### Lecture 15: Web Scraping and APIs

Scrape HTML websites with Beautiful Soup. Data Cleanup with Pandas. Connect to APIs such as Twitter, Reddit. JSON, REST.

#### Recommended reading

### Lecture 16: Classification I: K-Nearest Neighbors, Decision Trees

Introduction to classification, k-nearest neighbors, generalizability, bias-variance, cross validation, discussion of course projects

#### Recommended reading

### Lecture 17: No Class

Spring Break

### Lecture 18: No Class

Spring Break

### Lecture 19: Classification II: Logistic Regression and SVMs

Logistic Regression, Support Vector Machines (SVM), generalizability and cross validation

#### Recommended reading

- ISL, Ch. 8 and 9

### Lecture 20: Natural Language Processing

What are the challenges in understanding natural language? How can we build statistical models of language?

### Lecture 21: Regular Expressions, NLP in Practice

NLP in Python with NLTK. Parsing strings with regular expressions.

### Lecture 22: Clustering I

Introduction to Clustering, supervised vs. unsupervised learning, k-means method

#### Recommended reading

- ISL, Ch. 10.1 and 10.3
- Grus, Ch. 19
- scikit-learn documentation on clustering

### Lecture 23: Project Peer Feedback

Give and receive feedback on your project proposal from a peer group.

### Lecture 24: Clustering II

Hierarchical clustering, dendogram plots, clustering in practice

#### Recommended reading

- ISL, Ch. 10.1 and 10.3
- Grus, Ch. 19
- scikit-learn documentation on clustering

### Lecture 25: Dimensionality Reduction

Principal Component Analysis (PCA), using PCA for visualization

### Lecture 26: Neural Networks, Deep Learning, Tensor Flow

Classification and regression with neural networks. Network architectures. Using Tensor Flow.

#### Recommended reading

- Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow

### Lecture 27: Neural Networks, Deep Learning, Tensor Flow

Classification and regression with neural networks. Network architectures. Using Tensor Flow.

#### Recommended reading

- Aurélien Géron, Hands-On Machine Learning with Scikit-Learn and TensorFlow

### Lecture 28: Databases

Working with relational databases in Python. Introduction to the Structured Query Language.

### Lecture 29: Network Analysis

Basics about Networks. Visualization methods for general graphs and trees. Graph algorithms - path search, centrality, pagerank.

#### Mandatory reading

- Grus Ch. 21

### Lecture 30: Ratings, Rankings, and Elections

### Lecture 31: Best Project Presentations, Recap, Wrap-up, Outlook

What did we learn, what else is out there, what can you learn next?