CSC 380: Principles of Data Science (Spring 2025)
Tentative schedule
(Thanks to Profs. Colin Dawson, Cesim Erten, Kwang-Sung Jun, Jason Pacheco, Mihai Surdeanu, Xinchen Yu, whose slides I build mine upon.)
| Date | Topics | Slides | Additional readings | Homework |
|---|---|---|---|---|
| Jan 15 | Course logistics, Intro to data science | intro slides | Robinson and Nolis, “What is Data Science?”; Download Probability and Statisics Cookbook | |
| Jan 20 | Martin Luther King Jr Day – no class | |||
| Jan 22 | Basic Data Analysis 1: Pandas and descriptive statistics | slides | WJ Chap. 1 | |
| Jan 27 | Basic Probability 1 | slides | WJ Chap. 2 | |
| Jan 29 | Basic Probability 1 | WJ Chap. 5 | ||
| Feb 3 | Basic Probability 2: conditional probability | slides | WJ Chap. 6 | HW2 |
| Feb 5 | Basic Probability 2: conditional probability | Peter Donnelly: How stats fool juries, Interactive Fagan Nomogram by Dr. Carlos Scheidegger | ||
| Feb 10 | Basic Probability 2: independence, connections to combinatorics | WJ Chap 7 | ||
| Feb 12 | Basic Probability 3: discrete random variables | slides | WJ Chap 8 | |
| Feb 17 | Basic Probability 3: discrete random variables, continuous random variables | WJ Chap 9 | ||
| Feb 19 | Basic Probability 3: continuous random variables and PDFs | WJ Chap 7.5 | ||
| Feb 24 | Basic Probability 3: continuous random variables transformations, summary, examples | WJ Chap 8.4-8.6 | ||
| Feb 26 | Basic Probability 4: multivariate random variables | slides | WJ Chap 7.7 | |
| Mar 3 | Midterm review | slides | WJ Chap 10 | |
| Mar 5 | Midterm | |||
| Mar 10 | Spring Recess | |||
| Mar 12 | Spring Recess | |||
| Mar 17 | Basic Probability 4: multivariate random variables: conditional distribution and independence | WJ Chap. 7.7, 7.8, 8.7 | ||
| Mar 19 | Basic Probability 4: multivariate random variables: expectation and variance | WJ Chapt 3.1 and 8.8 | ||
| Mar 24 | Basic Probability 4: multivariate random variables: law of large numbers and central limit theorem | WJ Chap 10, 11 | ||
| Mar 26 | Basic Data analysis 2: machine learning and linear regression | slides | WJ Chap 3.2 | |
| Mar 31 | Basic Data analysis 2: overfitting and underfitting; cross validation; ridge and Lasso | ISL 6.1, 6.2 | ||
| Apr 2 | Basic Data analysis 3: classification and nearest neighbors | slides | ISL 2.2 | |
| Apr 7 | Basic Data analysis 3: logistic regression | ISL 4.3 | ||
| Apr 9 | Basic Data analysis 3: classification metrics beyond accuracy; multiclass classification | slides | ISL 4.4.2 (start from page 161) | |
| Apr 14 | Basic Data analysis 4: support vector machines; nonlinear models; beginning neural networks | ISL 9.1, 9.2, 10.1, 10.2 | ||
| Apr 16 | Basic Data Analysis 4: neural networks; clustering | ISL 10.7, 12.1, 12.4.1 | ||
| Apr 21 | Basic Statistics 1: point estimation basics; maximum likelihood estimation | slides | WJ 12.1, 12.2 | |
| Apr 23 | WJ 14.1, 14.2, 15.1, 15.2 | |||
| Apr 28 | Basic Statistics 2: interval estimation | WJ 16.1 | ||
| Apr 30 | Basic Statistics 3: hypothesis testing | WJ 17.1, 20.1-20.6 | ||
| May 5 | ||||
| May 7 | Final review | |||
| May 13 (Tue) | Final Exam 3:30 - 5:30pm |