Stat 88 · Probability and Mathematical Statistics for Data Science

Week 1

Getting Started

01/18-01/22

Week 2

Conditioning

01/25-01/29

Week 3

Random Variables

02/01-02/05

Week 4

Random Variables and Expectation

02/08-02/12

Week 5

The Power of Additivity

02/15-02/19

Week 6

Path to Prediction

02/22-02/26

Week 7

Midterm Week

03/01-03/05

Week 8

Variability

03/08-03/12

Week 9

Large Random Samples

03/15-03/19

Week 10

Spring Recess

03/22-03/26

Week 11

Fundamentals of Inference

03/29-04/02

Week 12

Continuous Distributions

04/05-04/09

Week 13

Bias and Variance

04/12-04/16

Week 14

The Error in a Regression Estimate

04/19-04/23

Week 15

Inference in Regression

04/26-04/30

Week	Date	Content	Notes & Assignments
1	Mon 01/18	No school
	Tues 01/19	No school
	Wed 01/20	Lecture 1: Course introduction and the basics (Sections 1.1, 1.2)	Lecture 1 Pre-lecture Notes Lecture 1 Notes Lecture 1 Video HW 1 (Due Fri 01/29 at 11:59 PM)
	Thu 01/21	Discussion
	Fri 01/22	Lecture 2: Axioms of Probability (Section 1.3)	Lecture 2 Pre-lecture Notes Lecture 2 Notes Lecture 2 Video
2	Mon 01/25	Lecture 3: Axioms of Probability, Intersections (section 1.3, 2.1)	Lecture 3 Pre-lecture Notes Lecture 3 Notes Lecture 3 Video HW 2 (Due Mon 02/01 at 11:59 PM)
	Tues 01/26	Discussion
	Wed 01/27	Lecture 4: Symmetry in Sampling, Bayes’ Rule (section 2.2, 2.3)	Lecture 4 Pre-lecture Notes Lecture 4 Notes Lecture 4 Kahoot Lecture 4 Video
	Thu 01/28	Discussion
	Fri 01/29	Lecture 5: Bayes’ Rule: definition, use, and interpretation, Independence (section 2.2, 2.3, 2.5)	Lecture 5 Pre-lecture Notes Lecture 5 Notes Lecture 5 Video Exam-prep Worksheet 1
3	Mon 02/01	Lecture 6: Finish up chapter 2, Random variables & their distributions (section 2.4, 3.1, 3.2)	Lecture 6 Pre-lecture Notes Lecture 6 Notes Lecture 6 Video HW 3 (Due Mon 02/08 at 11:59 PM)
	Tues 02/02	Discussion
	Wed 02/03	Lecture 7: Random variables & their distributions + 2 special distributions (section 3.1, 3.2, 3.3, 3.4)	Lecture 7 Pre-lecture Notes Lecture 7 Notes Lecture 7 Jupyter Notebook Lecture 7 Video
	Thu 02/04	Discussion
	Fri 02/05	Lecture 8: The hypergeometric distribution, examples, CDF (section 3.4, 3.5, 4.1)	Lecture 8 Pre-lecture Notes Lecture 8 Notes Lecture 8 Video
4	Mon 02/08	Lecture 9: CDF, Waiting Times (section 3.5, 4.1, 4.2)	Lecture 9 Pre-lecture Notes Lecture 9 Notes Lecture 9 Video HW 4 (Due Wed 02/17 at 11:59 PM)
	Tues 02/09	Discussion
	Wed 02/10	Lecture 10: Waiting Times, Exponential Approximations (section 4.2, 4.3)	Lecture 10 Pre-lecture Notes Lecture 10 Notes Lecture 10 Video
	Thu 02/11	Discussion
	Fri 02/12	Lecture 11: Waiting times, exponential approximations, the Poisson distribution (section 4.3, 4.4)	Lecture 11 Pre-lecture Notes Lecture 11 Notes Lecture 11 Video
5	Mon 02/15	No school
	Tues 02/16	Discussion
	Wed 02/17	Lecture 12: Sums of Poisson RVs, Expectation, Functions of RVs (section 4.4, 5.1, 5.2)	Lecture 12 Pre-lecture Notes Lecture 12 Notes Lecture 12 Video HW 5 (Due Tue 02/23 at 11:59 PM)
	Thu 02/18	Discussion
	Fri 02/19	Lecture 13: Functions of RVs, Method of indicators (section 5.2, 5.3)	Lecture 13 Pre-lecture Notes Lecture 13 Notes Lecture 13 Video
6	Mon 02/22	Lecture 14: Method of indicators, definition of unbiased estimators (section 5.3, 5.4)	Lecture 14 Pre-lecture Notes Lecture 14 Notes Lecture 14 Video HW 6 (Due Mon 03/01 at 11:59 PM)
	Tues 02/23	Discussion
	Wed 02/24	Lecture 15: Unbiased estimators, Conditional expectation (section 5.4, 5.5)	Lecture 15 Pre-lecture Notes Lecture 15 Notes Lecture 15 Video
	Thu 02/25	Discussion
	Fri 02/26	Lecture 16: Expectation by conditioning (section 5.5)	Lecture 16 Pre-lecture Notes Lecture 16 Notes Lecture 16 Video
7	Mon 03/01	Lecture 17: Examples: putting things together
	Tues 03/02	Discussion
	Wed 03/03	Midterm review
	Thu 03/04	Discussion
	Fri 03/05	Midterm
8	Mon 03/08	Lecture 20: Measuring variability (section 6.1, 6.2)
	Tues 03/09	Discussion
	Wed 03/10	Lecture 21: Typical or unusual? Tails of distributions (section 6.3, 6.4)
	Thu 03/11	Discussion
	Fri 03/12	Lecture 22: Variability of a random sample sum (section 7.1)
9	Mon 03/15	Lecture 23: The accuracy of a simple random sample (section 7.2)
	Tues 03/16	Discussion
	Wed 03/17	Lecture 24: The law of averages (section 7.3)
	Thu 03/18	Discussion
	Fri 03/19	Lecture 25: Central Limit Theorem (section 8.1, 8.2)
10	Mon 03/22	No school
	Tues 03/23	No school
	Wed 03/24	No school
	Thu 03/25	No school
	Fri 03/26	No school
11	Mon 03/29	Lecture 26: Normal approximations (section 8.3, 8.4)
	Tues 03/30	Discussion
	Wed 03/31	Lecture 27: Inference: testing hypotheses (section 9.1)
	Thu 04/01	Discussion
	Fri 04/02	Lecture 28: Inference: testing hypotheses (section 9.2) Continued
12	Mon 04/05	Lecture 29: Confidence intervals continue (section 9.3, 9.4)
	Tues 04/06	Discussion
	Wed 04/07	Lecture 30: Probability density (section 10.1, 10.2)
	Thu 04/08	Discussion
	Fri 04/09	Lecture 31: The exponential distribution (section 10.3)
13	Mon 04/12	Lecture 32: Some properties of the normal distribution (section 10.4)
	Tues 04/13	Discussion
	Wed 04/14	Lecture 33: Prediction: bias-variance (section 11.1)
	Thu 04/15	Discussion
	Fri 04/16	Lecture 34: The bias-variance tradeoff (section 11.2)
14	Mon 04/19	Lecture 35: Correlation (section 11.3, 11.4)
	Tues 04/20	Discussion
	Wed 04/21	Lecture 36: The regression estimates, effect and fallacy (section 11.4, 11.5)
	Thu 04/22	Discussion
	Fri 04/23	Lecture 37: The regression model for data (section 12.1)
15	Mon 04/26	Lecture 38: Inference for the true slope in the regression model (section 12.2, section 12.3)
	Tues 04/27	Discussion
	Wed 04/28	Lecture 39: Conclusion
	Thu 04/29	Discussion
	Fri 04/30	Lecture 40: Review