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 [“Cleaning Data” slides]
   1. Validity [Validity slides]
   2. Outliers [Outliers slides]
   3. Finding Outliers [Finding Outliers slides]
   4. Handling Outliers [Handling Outliers slides]
   5. Imputation [Imputation slides]
   6. Noise Filtering [Noise Filtering slides]
   7. Entity Resolution [Entity Resolution slides]
   8. Regular Expressions [Regular Expressions slides]
   9. Python re [Python re slides]
   10. Regex Summary [Regex Summary slides]
8. Stats Review [“Stats Review” slides]
   1. Context [Context slides]
   2. Types of Data [Types of Data slides]
   3. Population and Samples [Population and Samples slides]
   4. Probability Distributions [Probability Distributions slides]
   5. Central Tendancy [Central Tendancy slides]
   6. Dispersion [Dispersion slides]
   7. Relationships [Relationships slides]
   8. Plotting Data [Plotting Data slides]
   9. Specific Distributions [Specific Distributions slides]
   10. Normal Distribution [Normal Distribution slides]
9. Inferential Stats [“Inferential Stats” slides]
   1. Hypotheses [Hypotheses slides]
   2. T-Test [T-Test slides]
   3. p-values [p-values slides]
   4. Failure to Reject [Failure to Reject slides]
   5. Test Assumptions [Test Assumptions slides]
   6. Testing Normality [Testing Normality slides]
   7. Equal Variance Test [Equal Variance Test slides]
   8. Transforming Data [Transforming Data slides]
10. Statistical Tests [“Statistical Tests” slides]
    1. Multiple Groups [Multiple Groups slides]
    2. ANOVA [ANOVA slides]
    3. Post Hoc Analysis [Post Hoc Analysis slides]
    4. One- vs Two-Tailed Tests [One- vs Two-Tailed Tests slides]
    5. Hacking p-values [Hacking p-values slides]
    6. Central Limit Theorem [Central Limit Theorem slides]
    7. It's Probably Okay [It's Probably Okay slides]
    8. Mann–Whitney U-test [Mann–Whitney U-test slides]
    9. Chi-Square [Chi-Square slides]
    10. Regression [Regression slides]
    11. Stats Summary [Stats Summary slides]
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. Data Analysis Pipeline slide.

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)
2	Exer 1	4	May 20	Operating on Arrays	2-1 (0:48)
		5	May 20	Extract	2-2 (0:48)
		6	May 23	exercise time (in CSIL)
3	Exer 2	7	May 27	Kalman Example	3-1 (0:49)
		8	May 27	Cleaning Data	3-2 (0:49)
		9	May 30	exercise time (in CSIL)
4	Exer 3	10	Jun 3	Types of Data
		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.