Stat 88 · Probability and Mathematical Statistics for Data Science

Week 1

Getting Started

08/24-08/28

Week 2

Conditioning

08/31-09/04

Week 3

Random Variables

09/07-09/11

Week 4

Random Variables and Expectation

09/14-09/18

Week 5

The Power of Additivity

09/21-09/25

Week 6

Path to Prediction

09/28-10/02

Week 7

Midterm Week

10/05-10/09

Week 8

Variability

10/12-10/16

Week 9

Large Random Samples

10/19-10/23

Week 10

Fundamentals of Inference

10/26-10/30

Week 11

Continuous Distributions

11/02-11/06

Week 12

Bias and Variance

11/09-11/13

Week 13

Introduction to Regression

11/16-11/20

Week 14

The Error in a Regression Estimate

11/23-11/27

Week 15

Inference in Regression

11/30-12/04

Week	Date	Content	Notes & Assignments
1	Mon 08/24	No school
	Tues 08/25	No school
	Wed 08/26	Lecture 1: Course introduction; fundamentals (section 1.1, 1.2)	Lecture 1 Pre-lecture Notes Lecture 1 Notes Lecture 1 Video HW 1 (Due Fri 09/04 at 11:59 PM)
	Thu 08/27	Discussion
	Fri 08/28	Lecture 2: Exact calculations, and bounds (section 1.3)	Lecture 2 Pre-lecture Notes Lecture 2 Notes Lecture 2 Video
2	Mon 08/31	Lecture 3: Intersections of several events (section 2.1, 2.2)	Lecture 3 Pre-lecture Notes Lecture 3 Notes Lecture 3 Video HW 2 (Due Wed 09/09 at 11:59 PM)
	Tues 09/01	Discussion
	Wed 09/02	Lecture 4: Updating chances: Bayes' rule (section 2.3, 2.4)	Lecture 4 Pre-lecture Notes Lecture 4 Notes Lecture 4 Video
	Thu 09/03	Discussion
	Fri 09/04	Lecture 5: A closer look at independence (section 2.4, 2.5)	Lecture 5 Pre-lecture Notes Lecture 5 Notes Lecture 5 Video
3	Mon 09/07	Holiday	HW 3 (Due Tue 09/15 at 11:59 PM)
	Tues 09/08	Discussion
	Wed 09/09	Lecture 6: Random variables and their distributions: the binomial (section 3.1, 3.2, 3.3)	Lecture 6 Pre-lecture Notes Lecture 6 Notes Lecture 6 Video
	Thu 09/10	Discussion
	Fri 09/11	Lecture 7: Simple random sampling (section 3.4, 3.5)	Lecture 7 Pre-lecture Notes Lecture 7 Notes Lecture 7 Video Lecture 7 (Part II) Pre-lecture Notes Lecture 7 (Part II) Notes Lecture 7 (Part II) Video
4	Mon 09/14	Lecture 8: Exponential approximations (section 3.5, 4.1)	Lecture 8 Pre-lecture Notes Lecture 8 Notes Lecture 8 Video HW 4 (Due Mon 09/21 at 11:59 PM)
	Tues 09/15	Discussion
	Wed 09/16	Lecture 9: Waiting Time (section 4.2, 4.3)	Lecture 9 Pre-lecture Notes Lecture 9 Notes Lecture 9 Video
	Thu 09/17	Discussion
	Fri 09/18	Lecture 10: Exponential approximation and Poisson Distribution (section 4.3, 4.4)	Lecture 10 Pre-lecture Notes Lecture 10 Notes Lecture 10 Video
5	Mon 09/21	Lecture 11: Poisson and Expectation (section 4.4, 5.1)	Lecture 11 Pre-lecture Notes Lecture 11 Notes Lecture 11 Video HW 5 (Due Mon 09/28 at 11:59 PM)
	Tues 09/22	Discussion
	Wed 09/23	Lecture 12: Expectation and Joint Distribution; (section 5.1, 5.2, 5.3)	Lecture 12 Pre-lecture Notes Lecture 12 Notes Lecture 12 Video
	Thu 09/24	Discussion
	Fri 09/25	Lecture 13: The method of indicators and Additivity of Expectation (section 5.3)	Lecture 13 Pre-lecture Notes Lecture 13 Notes Lecture 13 Video
6	Mon 09/28	Lecture 14: Indicators and Updating revisited: conditional distribution (section 5.3, 5.4)	Lecture 14 Pre-lecture Notes Lecture 14 Notes Lecture 14 Video HW 6 (Due Wed 10/07 at 11:59 PM)
	Tues 09/29	Discussion
	Wed 09/30	Lecture 15: Unbiased biased estimator(section 5.4, 5.5)	Lecture 15 Pre-lecture Notes Lecture 15 Notes Lecture 15 Video
	Thu 10/01	Discussion
	Fri 10/02	Lecture 16: Expectation by conditioning (section 5.5)	Lecture 16 Pre-lecture Notes Lecture 16 Notes Lecture 16 Video
7	Mon 10/05	Lecture 17: Examples: putting things together	Lecture 17 Pre-lecture Notes Lecture 17 Notes Lecture 17 Video
	Tues 10/06	Discussion
	Wed 10/07	Midterm review	Lecture 18 Pre-lecture Notes Lecture 18 Notes Lecture 18 Video
	Thu 10/08	Discussion
	Fri 10/09	Midterm
8	Mon 10/12	Lecture 20: Measuring variability (section 6.1, 6.2)	Lecture 20 Pre-lecture Notes Lecture 20 Notes Lecture 20 Video HW 7 (Due Mon 10/19 at 11:59 PM)
	Tues 10/13	Discussion
	Wed 10/14	Lecture 21: Typical or unusual? Tails of distributions (section 6.3, 6.4)	Lecture 21 Pre-lecture Notes Lecture 21 Notes Lecture 21 Video
	Thu 10/15	Discussion
	Fri 10/16	Lecture 22: Variability of a random sample sum (section 7.1)	Lecture 22 Pre-lecture Notes Lecture 22 Notes Lecture 22 Video
9	Mon 10/19	Lecture 23: The accuracy of a simple random sample (section 7.2)	Lecture 23 Pre-lecture Notes Lecture 23 Notes Lecture 23 Video HW 8 (Due Mon 10/26 at 11:59 PM)
	Tues 10/20	Discussion
	Wed 10/21	Lecture 24: The law of averages (section 7.3)	Lecture 24 Pre-lecture Notes Lecture 24 Notes Lecture 24 Video
	Thu 10/22	Discussion
	Fri 10/23	Lecture 25: Central Limit Theorem (section 8.1, 8.2)	Lecture 25 Pre-lecture Notes Lecture 25 Notes Lecture 25 Video
10	Mon 10/26	Lecture 26: Normal approximations (section 8.3, 8.4)	Lecture 26 Pre-lecture Notes Lecture 26 Notes Lecture 26 Video HW 9 (Due Mon 11/02 at 11:59 PM)
	Tues 10/27	Discussion
	Wed 10/28	Lecture 27: Inference: testing hypotheses (section 9.1)
	Thu 10/29	Discussion
	Fri 10/30	Lecture 28: Inference: testing hypotheses (section 9.2) Continued
11	Mon 11/02	Lecture 29: Confidence intervals continue (section 9.3, 9.4)
	Tues 11/03	Discussion
	Wed 11/04	Lecture 30: Probability density (section 10.1, 10.2)
	Thu 11/05	Discussion
	Fri 11/06	Lecture 31: The exponential distribution (section 10.3)
12	Mon 11/09	Lecture 32: Some properties of the normal distribution (section 10.4)
	Tues 11/10	Discussion
	Wed 11/11	Holiday
	Thu 11/12	Discussion
	Fri 11/13	Lecture 33: Prediction: bias-variance (section 11.1)
13	Mon 11/16	Lecture 34: The bias-variance tradeoff (section 11.2)
	Tues 11/17	Discussion
	Wed 11/18	Lecture 35: Correlation (section 11.3, 11.4)
	Thu 11/19	Discussion
	Fri 11/20	Lecture 36: The regression estimates, effect and fallacy (section 11.4, 11.5)
14	Mon 11/23	Lecture 37: The regression model for data (section 12.1)
	Tues 11/24	Discussion
	Wed 11/25	Holiday
	Thu 11/26	Holiday
	Fri 11/27	Holiday
15	Mon 11/30	Lecture 38: Inference for the true slope in the regression model (section 12.2, section 12.3)
	Tues 12/01	Discussion
	Wed 12/02	Lecture 39: Conclusion
	Thu 12/03	Discussion
	Fri 12/04	Lecture 40: Review