This course introduces students to the principles and tools of data science. This course will provide a foundation for properly collecting and analyzing data to draw insights and to answer datadriven questions. The course has three main components: applied probability and statistics, data analysis and visualization, and machine learning. In the first component students will be introduced to the fundamentals of applied probability and statistics, learn how to interpret randomness, and how to assess predictive uncertainty. Students will then learn how to handle, clean, process, and visualize data of varying types using Python. Finally, the students will be introduced to the basics of machine learning to build predictive models. Students will further learn how to assess model validity and how to interpret the quality of model predictions.
Instructor: Jason Pacheco, GS 724, Email: pachecoj@cs.arizona.edu
TA: Enfa Rose George: enfageorge@email.arizona.edu
TA: Saiful Islam Salim saifulislam@email.arizona.edu
Office Hours:
Enfa, Mondays, 10:30  11:30, GouldSimpson Rm 934, Desk #6 (Hybrid)
Saiful, Tuesdays, 10:00  11:00, GouldSimpson Rm 942 (Hybrid)
Jason, Wednesdays, 10:00  11:00, (Zoom)
D2L: https://d2l.arizona.edu/d2l/home/1072117
Piazza: https://piazza.com/arizona/fall2021/csc380
Instructor Homepage: http://www.pachecoj.com
Date  Topic  Readings  Assignment 

8/24  Introduction + Course Overview (slides) 
What is Data Science? Robinson, E. and Nolis, J. 

8/26  Random Events and Probability (slides)  WL : CH1  
8/31  Discrete Probability Distributions + numpy.random (slides)  WL : CH2  HW1 (Due: 9/9) 
9/2  Continuous Probability, PDFs (slides)  
9/7  Moments and Dependence (slides)  WL : CH3  
9/9  Statistics and Estimation (slides)  WL : Sec. 9.1  9.7  HW2 (Due: 9/16) 
9/14  Bayesian Statistics (slides) 
WL : Sec. 6.3, Sec. 10.2, Sec. 11.111.4 Scribbr: A Stepbystep Guide to Statistical Analysis 

9/16  Data Collection 
Scribbr: 
HW3 (Due: 9/23) 
9/21  Exploratory Data Analysis  
9/23  Data Preprocessing  
9/28  Introduction to Data Visualization  
9/30  Data Visualization  
10/5  Review + Midterm  
10/7  
10/12  
10/14  
10/19  
10/21  
10/26  
10/28  
11/2  
11/4  
11/9  
11/11  Veteran's Day / NO CLASS  
11/16  
11/18  
11/23  
11/25  Thanksgiving Recess / NO CLASS  
11/30  
12/2  
12/7  
12/15  Final Exam 