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/Exam [Quizzes/Exam 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
  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. Other DataFrame Tools
  18. Data Warehouses
  19. Aside: NumPy/Pandas Speed
  20. Communicating
  21. More Data Science

Course home page.

Schedule, Summer 2025

Week Deliverables (*) Lecture Hour Lecture Date First Slide Video Link
1 1 May 13 Intro 1-1 [0:48]
2 May 13 Your Question 1-2 [0:50]
3 May 16 exercise time (in CSIL)
[if CSIL is open + remote]
2 Exer 1 4 May 20 Operating on Arrays
5 May 20
6 May 23 exercise time (in CSIL)
3 Exer 2 7 May 27
8 May 27
9 May 30 exercise time (in CSIL)
4 Exer 3 10 Jun 3
11 Jun 3
12 Jun 6 exercise time (in CSIL)
5 Exer 4 13 Jun 10
14 Jun 10
15 Jun 13 exercise time (in CSIL)
6 Exer 5, Quiz 1 16 Jun 17
17 Jun 17
18 Jun 20 Quiz 1
7 Exer 6 19 Jun 24
20 Jun 24
21 Jun 27 exercise time (in CSIL)
8 Exer 7 22 Jul 1 Canada Day holiday 🍁
23 Jul 1
24 Jul 4 exercise time? Lecture?
9 Exer 8 25 Jul 8
26 Jul 8
27 Jul 11 exercise time (in CSIL)
10 Exer 9, Quiz 2 28 Jul 15
29 Jul 15
30 Jul 18 Quiz 2
11 Exer 10 31 Jul 22
32 Jul 22
33 Jul 25 exercise time (in CSIL)
12 Exer 11 34 Jul 29
35 Jul 29
36 Aug 1 exercise time (in CSIL)
13 Exer 12, Quiz 3 37 Aug 5
38 Aug 5
39 Aug 8 Quiz 3
14 Project —

* Check CourSys for the actual due dates and times.