Stat 88 · Probability and Mathematical Statistics for Data Science

Week 1

Getting Started

01/20-01/24

Week 2

Conditioning

01/27-01/31

Week 3

Random Variables

02/03-02/07

Week 4

Random Variables and Expectation

02/10-02/14

Week 5

The Power of Additivity

02/17-02/21

Week 6

Path to Prediction

02/24-02/28

Week 7

Midterm Week

03/02-03/06

Week 8

Variability

03/09-03/13

Week 9

Large Random Samples

03/16-03/20

Week 10

Holiday

03/23-03/27

Week 11

Fundamentals of Inference

03/30-04/03

Week 12

Continuous Distributions

04/06-04/10

Week 13

Bias and Variance

04/13-04/17

Week 14

Introduction to Regression

04/20-04/24

Week 15

Inference in Regression

04/27-05/01

Week	Date	Content	Notes & Assignments
1	Mon 01/20	No school
	Tues 01/21	No school
	Wed 01/22	Lecture 1: Course introduction and fundamentals (section 1.1, 1.2)	Lecture 1 Notes HW 1 (Due Mon 01/27 at 10 PM)
	Thu 01/23	Discussion
	Fri 01/24	Lecture 2: Exact calculations and bounds (section 1.3)	Lecture 2 Notes
2	Mon 01/27	Lecture 3: Intersections of several events (section 2.1, 2.2)	HW 2 (Due Mon 02/03 at 10 PM) Lecture 3 Notes
	Tues 01/28	Discussion
	Wed 01/29	Lecture 4: Updating chances: Bayes' rule (section 2.3, 2.4)	Lecture 4 Notes
	Thu 01/30	Discussion
	Fri 01/31	Lecture 5: A closer look at independence (section 2.4, 2.5)	Lecture 5 Notes
3	Mon 02/03	Lecture 6: Random variables and their distributions: the binomial (section 3.1, 3.2, 3.3)	HW 3 (Due Mon 02/10 at 10 PM) Lecture 6 Notes
	Tues 02/04	Discussion
	Wed 02/05	Lecture 7: Simple random sampling (section 3.4, 3.5)	Lecture 7 Notes
	Thu 02/06	Quiz 1
	Fri 02/07	Lecture 8: Exponential approximations (section 3.5, 4.1)	Lecture 8 Notes
4	Mon 02/10	Lecture 9: Waiting time (section 4.2, 4.3)	HW 4 (Due Tue 02/18 at 10 PM) Lecture 9 Notes
	Tues 02/11	Discussion
	Wed 02/12	Lecture 10: Exponential approximation and Poisson Distribution (section 4.3, 4.4)	Lecture 10 Notes
	Thu 02/13	Discussion
	Fri 02/14	Lecture 11: Poisson and Expectation (section 4.4, 5.1)	Lecture 11 Notes Lecture 11 Video Part1 Lecture 11 Video Part2
5	Mon 02/17	Holiday	HW 5 (Due Mon 02/24 at 10 PM)
	Tues 02/18	Discussion
	Wed 02/19	Lecture 12: Expectation and Joint Distribution; (section 5.1, 5.2, 5.3)	Lecture 12 Notes
	Thu 02/20	Discussion
	Fri 02/21	Lecture 13: The method of indicators and Additivity of Expectation (section 5.3)	Lecture 13 Notes
6	Mon 02/24	Lecture 14: Indicators and Updating revisited: conditional distribution (section 5.3, 5.4)	Lecture 14 Notes HW 6 (Due Mon 03/02 at 10 PM)
	Tues 02/25	Discussion
	Wed 02/26	Lecture 15: Unbiased biased estimator(section 5.4, 5.5)	Lecture 15 Notes
	Thu 02/27	Quiz 2
	Fri 02/28	Lecture 16: Expectation by conditioning (section 5.5)	Lecture 16 Notes
7	Mon 03/02	Lecture 17: examples: putting things together	Midterm Information Lecture 17 Notes
	Tues 03/03	Discussion
	Wed 03/04	Midterm review	Lecture 18 Notes
	Thu 03/05	Discussion
	Fri 03/06	Midterm
8	Mon 03/09	Lecture 20: Measuring variability (section 6.1, 6.2)	Lecture 20 Notes HW 7 (Due Mon 03/16 at 10 PM)
	Tues 03/10	Discussion
	Wed 03/11	Lecture 21: Typical or unusual? Tails of distributions (section 6.3, 6.4)	Lecture 21 Notes Lecture 21 Recording
	Thu 03/12	Discussion
	Fri 03/13	Lecture 22: Variability of a random sample sum (section 7.1)	Lecture 22 Notes Lecture 22 Recording
9	Mon 03/16	Lecture 23: The accuracy of a simple random sample (section 7.2)	HW 8 (Due Mon 03/30 at 10 PM) Lecture 23 Notes Lecture 23 Recording
	Tues 03/17	Discussion
	Wed 03/18	Lecture 24: The law of averages (section 7.3)	Lecture 24 Notes Lecture 24 Recording
	Thu 03/19	Discussion
	Fri 03/20	Lecture 25: Central Limit Theorem (section 8.1, 8.2)	Lecture 25 Notes Lecture 25 Recording
10	Mon 03/23	Holiday
	Tues 03/24	Holiday
	Wed 03/25	Holiday
	Thu 03/26	Holiday
	Fri 03/27	Holiday
11	Mon 03/30	Lecture 26: Normal approximations (section 8.3, 8.4)	HW 9 (Due Mon 04/06 at 10 PM) Lecture 26 Recording Lecture 26 Notes
	Tues 03/31	Discussion
	Wed 04/01	Lecture 27: Inference: testing hypotheses (section 9.1, 9.2)
	Thu 04/02	Quiz 3
	Fri 04/03	Lecture 28: Confidence intervals (section 9.3)
12	Mon 04/06	Lecture 27: Confidence intervals continue (section 9.4)
	Tues 04/07	Discussion
	Wed 04/08	Lecture 28: Probability density (section 10.1, 10.2)
	Thu 04/09	Discussion
	Fri 04/10	Lecture 29: The exponential distribution (section 10.3)
13	Mon 04/13	Lecture 30: Some properties of the normal distribution (section 10.4)
	Tues 04/14	Discussion
	Wed 04/15	Lecture 31: Prediction: bias-varaince (section 11.1)
	Thu 04/16	Discussion
	Fri 04/17	Lecture 32: The bias-variance tradeoff (section 11.2)
14	Mon 04/20	Lecture 33: Correlation (section 11.3, 11.4)
	Tues 04/21	Discussion
	Wed 04/22	Lecture 34: The regression estimates, effect and fallacy (section 11.4, 11.5)
	Thu 04/23	Quiz 4
	Fri 04/24	Lecture 35: The regression model for data (section 12.1)
15	Mon 04/27	Lecture 36: Inference for the true slope in the regression model (section 12.2, 12.3)
	Tues 04/28	Discussion
	Wed 04/29	Lecture 37: Conclusion
	Thu 04/30	Discussion
	Fri 05/01	Lecture 38: Review