INFO 511: Fundamentals of Data Science

Dr. Greg Chism

This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses and the timeline of topics and assignments might be updated throughout the semester.

WEEK DATE TOPIC PREPARE MATERIALS DUE
1 Mon, Aug 26

Welcome + Intro to data science

πŸ“š DS From Scratch - Chp 1

πŸŽ₯ Week 1 Lectures
⌨️ ae 00
πŸ’» lab 0



Introducing your Toolkit

πŸ“ƒ Jupyter + VSCode
OR
πŸŽ₯ Jupyter + VSCode (up to 4:50)

⌨️ ae 01


2 Mon, Sep 2

Intro to Python + Git/GitHub

πŸ“š DS From Scratch - Chp 2
πŸŽ₯ Git + VSCode

πŸŽ₯ Week 2 Lectures
πŸ’» DS experience



Intro to Numpy + Pandas

πŸ“š Python Data Analysis - Chp 4
πŸ“š Python Data Analysis - Chp 5

πŸ’» lab 1


3 Mon, Sep 9

Exploratory Data Analysis

πŸ“š Prac Stats for DS - Chp 1
πŸ“š Math for DS - Chp 3

πŸŽ₯ Week 3 Lectures
⌨️ ae 02
βœ… ae 02


Fri, Sep 13

Data visualization

πŸ“š Python Data Analysis - Chp 9

⌨️ ae 03
βœ… ae 03

Lab 1 @ 5pm

4 Mon, Sep 16

Data preprocessing

πŸ“š Python Data Analysis - Chp 7
πŸ“ƒ Data Preprocessing in Python

πŸŽ₯ Week 4 Lectures
⌨️ ae 04
βœ… ae 04
πŸ’» lab 2


Fri, Sep 20

Data wrangling

πŸ“š Python Data Analysis - Chp 8

⌨️ ae 05
βœ… ae 05


5 Mon, Sep 23

Web scraping

πŸ“š DS From Scratch - Chp 9
πŸ“ƒ Web Scraping in Python

πŸŽ₯ Week 5 Lectures
⌨️ ae 06
βœ… ae 06


Fri, Sep 27

Midterm Review



Lab 2 @ 5pm

6 Mon, Sep 30

Midterm Released


πŸŽ₯ Week 6 Lectures
πŸ“ Midterm assignment



Data science ethics

πŸ“š DS From Scratch - Chp 26
πŸŽ₯ Misrepresentation
πŸŽ₯ Data privacy
πŸŽ₯ Algorithmic bias
πŸŽ₯ Alberto Cairo - How charts lie
πŸŽ₯ Joy Buolamwini - How I’m fighting bias in algorithms

πŸ’» lab 3


7 Mon, Oct 7

Probability

πŸ“š DS From Scratch - Chp 6 (up to conditional probability)

πŸŽ₯ Week 7 Lectures
⌨️ ae 07
βœ… ae 07
πŸ““ project milestone 1


Fri, Oct 11

Conditional probability

πŸ“š DS From Scratch - Chp 6 (Conditional probability)

⌨️ ae 08
βœ… ae 08


8 Mon, Oct 14

Hypothesis testing

πŸ“š DS From Scratch - Chp 5

πŸŽ₯ Week 8 Lectures

Midterm assignment due @ 5pm
project milestone 1 @ 5pm


Sampling distributions + inference

πŸ“š Review DS From Scratch - Chp 6 (Central Limit Theorem)
πŸ“š IMS - Chp 13

⌨️ ae 09
βœ… ae 09


9 Mon, Oct 21

Linear regression

πŸ“š DS From Scratch - Chp 14 (up to Gradient Descent)

πŸŽ₯ Week 9 Lectures
⌨️ ae 10
βœ… ae 10



Multiple linear regression

πŸ“š DS From Scratch - Chp 15 (up to Regularization)

⌨️ ae 11
βœ… ae 11


10 Mon, Oct 28

Logistic regression

πŸ“š DS From Scratch - Chp 16 (up to SVMs)

πŸŽ₯ Week 10 Lectures

Lab 3 @ 5pm


Prediction + uncertainty

πŸ“š DS From Scratch - Chp 11
πŸ“š Python Data Analysis - Chp 12.1 - 12.2

⌨️ ae 12
βœ… ae 12
πŸ’» lab 4


11 Mon, Nov 4

Model validation

πŸ“š Python for DS - Chp 5.3

πŸŽ₯ Week 11 Lectures
πŸ““ project milestone 2


Fri, Nov 8

Calculus I

πŸ“š Math for DS - Chp 1 (up to Integrals)

⌨️ ae 13
βœ… ae 13


12 Mon, Nov 11

Calculus II

πŸ“š Math for DS - Chp 1 (Integrals)

πŸŽ₯ Week 12 Lectures
⌨️ ae 14
βœ… ae 14

project milestone 2 @ 5pm


Linear Algebra I

πŸ“š Math for DS - Chp 4 (up to Eigenvalues)

⌨️ ae 15
βœ… ae 15

ae-15-linear-algebra

13 Mon, Nov 18

Linear Algebra II

πŸ“š Math for DS - Chp 4 (from Eigenvalues)
πŸ“ƒ Dr. Bernstein’s Posts on Linear Algebra

πŸŽ₯ Week 13 Lectures ⌨️ ae 16
βœ… ae 16
πŸ’» lab 5

Lab 4 @ 5pm


Communicating data science results effectively

πŸ“š fdv - Chp 29



Fri, Nov 22

Final Review
Final Released


πŸ“ Final assignment


14 Mon, Nov 25

πŸ¦ƒ Happy Thanksgiving




Thu, Nov 28

πŸ¦ƒ Happy Thanksgiving




15 Mon, Dec 2

Project milestone 3 - Peer review


πŸ““ project milestone 3
πŸŽ₯ peer-review walkthrough

Lab 5 @ 5pm

Fri, Dec 6


project milestone 3 @ 5pm
πŸ’» DS experience @ 11:59pm

16 Mon, Dec 9

Work on final project & assignment




Fri, Dec 13

πŸ““ project milestone 4

Final assignment due @ 5pm

Finals Wed, Dec 18

Project milestone 4 - Project presentations



project presentations due @ 5pm

Fri, Dec 20


project write-up due @ 5pm