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YData: An Introduction to Data Science

YData is an introduction to the fundamental ideas and skills of data science. Based on Berkeley’s popular Data 8 course, YData is an introduction to data science that emphasizes computational and programming skills along with inferential thinking.

Calendar Spring 2022

Instructors: Ethan Meyers
Lecture: MWF 10:30-11:20

Note: This class schedule is provisional and will be updated as the course progresses.

Here link to the MyBinder for running class demonstrations

Date Topic Lecture Reading Practice Assignment
Wed 1/26 Introduction Slides Chapter 1.1, 1.2, 1.3   Assignment 00: Not due.
Fri 1/28 Cause and Effect Slides Chapter 2    
Mon 1/31 Intro to Python demos/lec03, MyBinder, Slides Chapter 3 Practice 01: Expressions Assignment 01: due 2/6
Wed 2/2 Data Types demos/lec04, MyBinder, slides Chapters 4, 5    
Fri 2/4 Arrays and Tables demos/lec05, MyBinder, Slides Chapter 6.1, 6.2 Practice 02: Types & Sequences  
Mon 2/7 Census demos/lec06, MyBinder, Slides Chapter 6.3, 6.4   Assignment 02: due 2/13
Wed 2/9 Charts demos/lec07, MyBinder,Slides Chapter 7, 7.1 Practice 03: Arrays & Tables  
Fri 2/11 Histograms demos/lec08, MyBinder, Slides Chapter 7.2, 7.3    
Mon 2/14 Functions demos/lec09, Slides Chapter 8, 8.1   Assignment 03: due 2/20
Wed 2/16 Groups demos/lec10, Slides Chapter 8.2, 8.3 Practice 04: Histograms & Functions  
Fri 2/18 Joins demos/lec11, slides Chapter 8.4, 8.5    
Mon 2/21 Mapping and conditional statements demos/lec12, slides chapter 8.5   Assignment 04: due 2/27
Project 1: due Friday 3/4
Wed 2/23 Iteration demos/lec13, slides Chapter 9, 9.1, 9.2, 9.3    
Fri 2/25 Chance demos/lec14, slides Chapter 9.4, 9.5    
Mon 2/28 Sampling demos/lec15, slides Chapter 9.4 10, 10.1, 10.2   Assignment 05: practice only, not turned in
Wed 3/2 Models demos/lec16, slides Chapter 10.3, 11.1  
Fri 3/4 Comparing Distributions demos/lec17, slides Chapter 11.1, 11.2 Practice 05: Simulations  
Mon 3/7 Decisions and Uncertainty demos/lec18, slides Chapter 11.3   Assignment 06: due 3/13
Wed 3/9 A/B Testing demos/lec19, slides Chapter 12.1, 12.3 Practice 06: Assessing Models  
Fri 3/11 Causality demos/lec20, slides Chapter 12.2    
Mon 3/14 Confidence Intervals demos/lec23, slides chapter 13, 13.1 13.2    
Wed 3/16 Examples demos/lec22, slides  
Fri 3/18 Midterm      
3/19-3/27 Spring Break        
Mon 3/28 Interpreting Confidence demos/lec24, slides Chapter 13.3, 13.4   Assignment 07: practice only, not turned in
Project 2: due Friday 4/8
Wed 3/30 Center and Spread demos/lec25, slides Chapter 14, 14.1, 14.2 Practice 07: Bootstrap  
Fri 4/1 The Normal Distribution demos/lec26, slides Chapter 14.3, 14.4    
Mon 4/4 Sample Means demos/lec27, slides Chapter 14.5   Assignment 08: due 4/10
Wed 4/6 Designing Experiments demos/lec28, slides Chapter 14.6    
Fri 4/8 Correlation demos/lec29, slides Chapter 15, 15.1 Practice 08: Correlation  
Mon 4/11 Linear Regression demos/lec30, slides Chapter 15.2   Assignment 09: due 4/17
Wed 4/13 Least Squares demos/lec31, slides Chapter 15.3, 15.4   Project 3: due Wednesday 4/27
Fri 4/15 Residuals demos/lec32, slides Chapter 15.5, 15.6 Practice 09: Regression  
Mon 4/18 Regression Inference demos/lec33, slides Chapter 16   Assignment 10: due 4/24
Wed 4/20 Classification <demos/lec34, slides Chapter 17, 17.1, 17.2, 17.3 Practice 10: Inference for Regression  
Fri 4/22 Classifiers demos/lec35, slides Chapter 17.4    
Mon 4/25 Classification continued demos/lec36, slides Chapter 17.6   Assignment 11: due 5/1
Wed 4/27 Multiple Regression demos/lec37, slides Chapter 18    
Fri 4/29 pandas demos/lec38, slides  
Mon 5/9 Final exam 2pm