due by 01/13/2022,03:39pm

Homework 0 - Description | Submission

due by 01/21/2022,11:59pm

Homework 1 - Description | Submission

due by 01/28/2022,11:59pm

Homework 2 - Description | Submission

due by 02/04/2022,11:59pm

Homework 3 - Description | Submission

due by 02/11/2022,11:59pm

Homework 4 - Description | Submission

due by 02/25/2022,11:59pm

Homework 5 - Description | Submission

due by 03/11/2022,11:59pm

Homework 6 - Description | Submission

due by 03/18/2022,11:59pm

Homework 7 - Description | Submission

due by 03/25/2022,11:59pm

Project Proposal - Description | Submission

due by 04/05/2022,11:59pm

Project Milestone Report - Description | Submission

due by 04/15/2022,11:59pm

Homework 8 - Description | Submission

due by 04/26/2022,11:59pm

Final Project Report - Description | Submission



Lecture 1: Introduction

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

Recommended reading

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

WIRED article on A/B testing


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


Lecture 10: Linear Regression 2

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

Recommended reading


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?


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


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


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


Lecture 27: Neural Networks, Deep Learning, Tensor Flow

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

Recommended reading


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?