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      
Apr 2 Basic Data analysis 3: classification and nearest neighbors slides    
Apr 7 Basic Data analysis 3: logistic regression      
Apr 9 Basic Data analysis 3: Support vector machines, nonlinear models      
Apr 14        
Apr 16 Basic Data Analysis: Data Wrangling      
Apr 21 Basic Statistics      
Apr 23        
Apr 28 Basic Data Analysis III: machine learning libraries      
Apr 30        
May 5        
May 7        
May 13 (Tue) Final Exam 3:30 - 5:30pm