CMPT 353 Lecture Notes

1. Course Introduction [“Course Introduction” slides]
   1. This Course [This Course slides]
   2. Offering Strategy [Offering Strategy slides]
   3. Grades [Grades slides]
   4. Exercises [Exercises slides]
   5. Project [Project slides]
   6. Quizzes [Quizzes slides]
   7. Us [Us slides]
   8. Lectures and Labs [Lectures and Labs slides]
   9. References [References slides]
   10. Programming [Programming slides]
   11. Expectations [Expectations slides]
   12. Computational Data Science? [Computational Data Science? slides]
   13. Data Science? [Data Science? slides]
   14. Why Data Science? [Why Data Science? slides]
   15. Topics (1) [Topics (1) slides]
2. Data Analysis Pipeline [“Data Analysis Pipeline” slides]
   1. Your Question [Your Question slides]
   2. Getting Data [Getting Data slides]
   3. Preparing Data [Preparing Data slides]
   4. Analyzing Data [Analyzing Data slides]
   5. Presenting Results [Presenting Results slides]
   6. Creating a Pipeline [Creating a Pipeline slides]
   7. Manual Pipeline Steps [Manual Pipeline Steps slides]
   8. The Pipeline [The Pipeline slides]
3. Data In Python [“Data In Python” slides]
   1. Built-In Data Structures [Built-In Data Structures slides]
   2. NumPy [NumPy slides]
   3. Operating on Arrays [Operating on Arrays slides]
   4. Pandas [Pandas slides]
   5. Working With Pandas [Working With Pandas slides]
4. Getting Data [“Getting Data” slides]
   1. Where Data Comes From [Where Data Comes From slides]
   2. Data from Files [Data from Files slides]
   3. Databases [Databases slides]
   4. Web APIs [Web APIs slides]
   5. Scraping HTML [Scraping HTML slides]
   6. File Formats [File Formats slides]
   7. CSV [CSV slides]
   8. JSON [JSON slides]
   9. XML [XML slides]
   10. Others [Others slides]
5. Extract-Transform-Load [“Extract-Transform-Load” slides]
   1. Extract [Extract slides]
   2. Transform [Transform slides]
   3. Load [Load slides]
   4. Summary [Summary slides]
6. Noise Filtering [“Noise Filtering” slides]
   1. Noise [Noise slides]
   2. LOESS Smoothing [LOESS Smoothing slides]
   3. LOESS in Python [LOESS in Python slides]
   4. Kalman Filtering [Kalman Filtering slides]
   5. Probability Distributions [Probability Distributions slides]
   6. Kalman Operation [Kalman Operation slides]
   7. Kalman Predictions [Kalman Predictions slides]
   8. Kalman Variances [Kalman Variances slides]
   9. pykalman [pykalman slides]
   10. Kalman Example [Kalman Example slides]
   11. Kalman Parameters [Kalman Parameters slides]
   12. Kalman Summary [Kalman Summary slides]
   13. Kalman Links [Kalman Links slides]
   14. Other Filtering [Other Filtering slides]
7. Cleaning Data
8. Stats Review
9. Inferential Stats
10. Statistical Tests
11. Machine Learning
12. ML: Classification
13. ML: Other Techniques
14. Big Data and Spark
15. How Spark Calculates
16. Working With Spark
17. Aside: NumPy/Pandas Speed
18. Other DataFrame Tools
19. Data Warehouses
20. Communicating
21. More Data Science

Course home page. Data Analysis Pipeline slide.

Schedule, Summer 2026

Week	Deliverables (*)	Lecture Hour	Lecture Date	First Slide	Video Link
1		1	May 12	Intro	1-1 (0:50)
		2	May 12	Creating a Pipeline	1-2 (0:49)
		3	May 15	exercise time (in CSIL)
2	Exer 1	4	May 19	Working w Pandas	2-1 (0:48)
		5	May 19	Load	2-2 (0:48)
		6	May 22	exercise time (in CSIL)
3	Exer 2	7	May 26	pykalman
		8	May 26
		9	May 29	exercise time (in CSIL)
4	Exer 3	10	Jun 2
		11	Jun 2
		12	Jun 5	exercise time (in CSIL)
5	Exer 4	13	Jun 9
		14	Jun 9
		15	Jun 12	exercise time (in CSIL)
6	Exer 5, Quiz 1	16	Jun 16
		17	Jun 16
		18	Jun 19		Quiz 1
7	Exer 6	19	Jun 23
		20	Jun 23
		21	Jun 26
8	Exer 7	22	Jun 30
		23	Jun 30
		24	Jul 3
9	Exer 8	25	Jul 7
		26	Jul 7
		27	Jul 10
10	Exer 9, Quiz 2	28	Jul 14
		29	Jul 14		Quiz 2
		30	Jul 17
11	Exer 10	31	Jul 21
		32	Jul 21
		33	Jul 24
12	Exer 11	34	Jul 28
		35	Jul 28
		36	Jul 31
13	Exer 12	37	Aug 4
		38	Aug 4
		39	Aug 7
14+	Project, Quiz 3	—

* Check CourSys for the actual due dates and times.