CMPT 353 Lecture Notes

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]
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]
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]
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]
Extract-Transform-Load [“Extract-Transform-Load” slides]
1. Extract [Extract slides]
2. Transform [Transform slides]
3. Load [Load slides]
4. Summary [Summary slides]
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]
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]
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]
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]
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]
Machine Learning
ML: Classification
ML: Other Techniques
Big Data and Spark
How Spark Calculates
Working With Spark
Aside: Dask
Aside: NumPy/Pandas Speed
Communicating
More Data Science

Schedule, Summer 2023

Week	Deliverables (*)	Lecture Hour	Lecture Date	First Slide	Video Link
1		1	May 9	Intro	1-1 (0:49)
		2	May 9	Getting Data	1-2 (0:46)
		3	May 12	exercise time (in-person)
2	Exer 1	4	May 16	Operating on Arrays	2-1 (0:48)
		5	May 16	JSON	2-2 (0:45)
		6	May 19	exercise time (in-person)
3	Exer 2	7	May 23	LOESS Smoothing	3-1 (0:48)
		8	May 23	Kalman Example	3-2 (0:48)
		9	May 26	exercise time (in-person)
4	Exer 3	10	May 30	Cleaning Data	4-1 (0:47)
		11	May 30	Types of Data	4-2 (0:48)
		12	Jun 2	exercise time (in-person)
5	Exer 4	13	Jun 6	T-Test
		14	Jun 6
		15	Jun 9	exercise time (in-person)
6	Exer 5	16	Jun 13
		17	Jun 13
		18	Jun 16	exercise time (in-person)
7	Exer 6, Quiz 1	19	Jun 20
		20	Jun 20
		21	Jun 23	Quiz 1
8	Exer 7	22	Jun 27
		23	Jun 27
		24	Jun 30	exercise time (in-person)
9	Exer 8	25	Jul 4
		26	Jul 4
		27	Jul 7	exercise time (in-person)
10	Exer 9	28	Jul 11
		29	Jul 11
		30	Jul 14	exercise time (in-person)
11	Exer 10, Quiz 2	31	Jul 18
		32	Jul 18
		33	Jul 21	Quiz 2
12	Exer 11	34	Jul 25
		35	Jul 25
		36	Jul 28	exercise time (in-person)
13	Exer 12	37	Aug 1
		38	Aug 1
		39	Aug 4	exercise time (in-person)
14+	Project, Final Quiz

* Check CourSys for the actual due dates and times.