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 Wed, Jan 15

Welcome + Intro to data science

πŸ“š DS From Scratch - Chp 1

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



Introducing your Toolkit

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

⌨️ ae 01


2 Mon, Jan 20

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’» HW 1


3 Mon, Jan 27

Exploratory Data Analysis

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

πŸŽ₯ Week 3 Lectures
⌨️ ae 02


Fri, Jan 31

Data visualization

πŸ“š Python Data Analysis - Chp 9

⌨️ ae 03

HW 1 @ 5pm

4 Mon, Feb 3

Data preprocessing

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

πŸŽ₯ Week 4 Lectures
⌨️ ae 04
πŸ’» HW 2


Fri, Feb 7

Data wrangling

πŸ“š Python Data Analysis - Chp 8

⌨️ ae 05


5 Mon, Feb 10

Web scraping

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

πŸŽ₯ Week 5 Lectures
⌨️ ae 06


Fri, Feb 14

Midterm Review



HW 2 @ 5pm

6 Mon, Feb 17

Midterm Released


πŸŽ₯ Week 6 Lectures
πŸ“ Midterm assignment πŸ’» HW 3



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



7 Mon, Feb 24

Probability

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

πŸŽ₯ Week 7 Lectures
⌨️ ae 07
πŸ““ project description


Fri, Feb 28

Conditional probability

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

⌨️ ae 08
πŸ’» HW 4

HW 3 @ 5pm

8 Mon, Mar 3

Hypothesis testing

πŸ“š DS From Scratch - Chp 5

πŸŽ₯ Week 8 Lectures



Sampling distributions + inference

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

⌨️ ae 09

Midterm assignment due @ 5pm

9 Mon, Mar 10

Spring recess β˜€οΈπŸŒΌ





Spring recess β˜€οΈπŸŒΌ




10 Mon, Mar 17

Linear regression

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

πŸŽ₯ Week 9 Lectures
⌨️ ae 10


Fri, Mar 21

Multiple linear regression

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

⌨️ ae 11


11 Mon, Mar 24

Logistic regression

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

πŸŽ₯ Week 10 Lectures
πŸ’» HW 5

HW 4 @ 5pm

Fri, Mar 28

Prediction + uncertainty

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

⌨️ ae 12


12 Mon, Mar 31

Model validation

πŸ“š Python for DS - Chp 5.3

πŸŽ₯ Week 11 Lectures



Calculus I

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

⌨️ ae 13


13 Mon, Apr 7

Calculus II

πŸ“š Math for DS - Chp 1 (Integrals)

πŸŽ₯ Week 12 Lectures
⌨️ ae 14



Linear Algebra I

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

⌨️ ae 15


Fri, Apr 11

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’» HW 6

HW 5 @ 5pm

14 Mon, Apr 14

Comparing machine learning models

TBD



Fri, Apr 18

πŸ“ Final assignment

AEs final deadline

15 Mon, Apr 21

Communicating data science results effectively

πŸ“š fdv - Chp 29



Fri, Apr 25

Final Review
Final Released



πŸ’» DS experience @ 11:59pm

16 Mon, Apr 28

Work on final project and assignment




Fri, May 2


HW 6 @ 5pm

17 Mon, May 5

Work on final project and assignment




Fri, May 9


Final assignment due @ 5pm

Finals Wed, May 14

Finish final project



Final project due @ 5pm