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 [“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 [“Machine Learning” slides]
    1. What is ML? [What is ML? slides]
    2. Linear Regression [Linear Regression slides]
    3. The Intercept [The Intercept slides]
    4. Polynomial Regression [Polynomial Regression slides]
    5. ML Pipelines [ML Pipelines slides]
    6. Training and Validation [Training and Validation slides]
12. ML: Classification [“ML: Classification” slides]
    1. Naïve Bayes [Naïve Bayes slides]
    2. Bayesian Classifier [Bayesian Classifier slides]
    3. Checking the Classifier [Checking the Classifier slides]
    4. Bayesian Priors [Bayesian Priors slides]
    5. Bayesian Failures [Bayesian Failures slides]
    6. Nearest Neighbours [Nearest Neighbours slides]
    7. More Than Points [More Than Points slides]
    8. Feature Scaling [Feature Scaling slides]
    9. Feature Engineering [Feature Engineering slides]
    10. Decision Trees [Decision Trees slides]
    11. Decisions [Decisions slides]
    12. Limiting the Tree [Limiting the Tree slides]
    13. Ensembles [Ensembles slides]
    14. Random Forests [Random Forests slides]
    15. Boosting [Boosting slides]
    16. Higher Dimensions [Higher Dimensions slides]
    17. PCA [PCA slides]
    18. Imbalanced Data [Imbalanced Data slides]
    19. Motivating Neural Nets [Motivating Neural Nets slides]
    20. Perceptrons [Perceptrons slides]
    21. Neural Networks [Neural Networks slides]
    22. Deep Learning [Deep Learning slides]
13. ML: Other Techniques [“ML: Other Techniques” slides]
    1. More Regression [More Regression slides]
    2. Clustering [Clustering slides]
    3. Clustering Colours [Clustering Colours slides]
    4. Anomaly Detection [Anomaly Detection slides]
    5. When Machine Learning? [When Machine Learning? slides]
14. Big Data and Spark [“Big Data and Spark” slides]
    1. Big Data [Big Data slides]
    2. Compute Clusters [Compute Clusters slides]
    3. Hadoop [Hadoop slides]
    4. Small-Data Spark [Small-Data Spark slides]
    5. First Spark Program [First Spark Program slides]
    6. Spark DataFrames [Spark DataFrames slides]
    7. Inspecting DataFrames [Inspecting DataFrames slides]
    8. Operating on DataFrames [Operating on DataFrames slides]
    9. DataFrames are Partitioned [DataFrames are Partitioned slides]
    10. Spark Input & Output [Spark Input & Output slides]
    11. Hadoop + Spark [Hadoop + Spark slides]
    12. HDFS [HDFS slides]
    13. YARN [YARN slides]
15. How Spark Calculates [“How Spark Calculates” slides]
    1. Controlling Partitions [Controlling Partitions slides]
    2. Shuffle Operations [Shuffle Operations slides]
    3. Grouping Data [Grouping Data slides]
    4. Execution Plans [Execution Plans slides]
    5. Lazy Evaluation [Lazy Evaluation slides]
    6. Too Lazy [Too Lazy slides]
    7. Caching [Caching slides]
    8. Spark Optimizer [Spark Optimizer slides]
    9. Spark DAG [Spark DAG slides]
    10. Spark Join [Spark Join slides]
16. Working With Spark [“Working With Spark” slides]
    1. Moving Data [Moving Data slides]
    2. Column Expressions [Column Expressions slides]
    3. Column Functions [Column Functions slides]
    4. Who Calculates? [Who Calculates? slides]
    5. User-Defined Functions [User-Defined Functions slides]
    6. SQL? [SQL? slides]
    7. RDDs [RDDs slides]
    8. Row-Oriented Data [Row-Oriented Data slides]
    9. Spark ↔ Python [Spark ↔ Python slides]
    10. More Spark I/O [More Spark I/O slides]
    11. Big Data is annoying. [Big Data is annoying. slides]
    12. Being Less Annoying [Being Less Annoying slides]
    13. How To Big Data? [How To Big Data? slides]
    14. When to Big Data? [When to Big Data? slides]
    15. More Big Data? [More Big Data? slides]
17. Aside: Dask [“Aside: Dask” slides]
    1. Python Data Tools [Python Data Tools slides]
    2. Dask [Dask slides]
    3. Working With Dask [Working With Dask slides]
    4. Scheduling Dask [Scheduling Dask slides]
    5. More Dask Features [More Dask Features slides]
    6. Dask Summary [Dask Summary slides]
18. Aside: NumPy/Pandas Speed [“Aside: NumPy/Pandas Speed” slides]
    1. Why So Slow? [Why So Slow? slides]
    2. NumPy Expression [NumPy Expression slides]
    3. Applying to a Series [Applying to a Series slides]
    4. Vectorizing [Vectorizing slides]
    5. Applying By Row [Applying By Row slides]
    6. Using Python [Using Python slides]
    7. With NumExpr [With NumExpr slides]
    8. Summary [Summary slides]
    9. Pandas vs Dask [Pandas vs Dask slides]
19. Communicating [“Communicating” slides]
    1. Asking the Right Question [Asking the Right Question slides]
    2. Communicating Results [Communicating Results slides]
    3. Visualizing Data [Visualizing Data slides]
    4. More Resources [More Resources slides]
20. More Data Science [“More Data Science” slides]
    1. Other Technologies [Other Technologies slides]
    2. Databases [Databases slides]
    3. Topics We Covered [Topics We Covered slides]
    4. Practical Omissions [Practical Omissions slides]
    5. Other Courses [Other Courses slides]

Course home page.

Schedule, Fall 2022

Week	Deliverables (*)	Lecture Hour	Lecture Date	First Slide	Video Link
1		1	Sep 9	Intro	1-1 (0:50)
2	Exer 1	2	Sep 13	Getting Data	2-1 (0:48)
		3	Sep 13	Operating on Arrays	2-2 (0:49)
		4	Sep 16	exercise time (in-person)
3	Exer 2	5	Sep 20	Getting Data	3-1 (0:48)
		6	Sep 20	Noise	3-2 (0:48)
		7	Sep 23	exercise time (in-person)
4	Exer 3	8	Sep 27	Kalman Parameters	4-1 (0:48)
		9	Sep 27	Entity Resolution	4-2 (0:48)
		10	Sep 30	Truth and Reconciliation Day
5	Exer 4	11	Oct 4	Specific Distrib	5-1 (0:47)
		12	Oct 4	Transforming Data	5-2 (0:46)
		13	Oct 7	exercise time (in-person)
6	Exer 5	14	Oct 11	p-hacking	6-1 (0:49)
		15	Oct 11	Machine Learning	6-2 (0:39)
		16	Oct 14	exercise time (in-person)
7	Exer 6	17	Oct 18	Polynomial Regression	7-1 (0:47)
		18	Oct 18	Naïve Bayes	7-2 (0:48)
		19	Oct 21	exercise time (in-person)
8	Exer 7, Quiz 1	20	Oct 25	More Than Points	8-1 (0:48)
		21	Oct 25	Random Forests	8-2 (0:46)
		22	Oct 28	Quiz 1
9	Exer 8	23	Nov 1	Perceptrons	9-1 (0:48)
		24	Nov 1	Anomaly Detection	9-2 (0:48)
		25	Nov 4	exercise time (in-person)
10	Exer 9	26	Nov 8	DFs are Partitioned	10-1 (0:45)
		27	Nov 8	How Spark Calculates	10-2 (0:49)
		28	Nov 11	Remembrance Day
11	Exer 10	29	Nov 15	Execution Plans	11-1 (0:48)
		30	Nov 15	Spark Join	11-2 (0:47)
		31	Nov 18	exercise time (in-person)
12	Exer 11	32	Nov 22	SQL?	12-1 (0:50)
		33	Nov 22	Big Data is annoying.	12-2 (0:47)
		34	Nov 25	exercise time (in-person)
13	Exer 12, Quiz 2	35	Nov 29	Pandas Speed: Numexpr	13-1 (1:00)
		36	Nov 29
		37	Dec 2	Quiz 2
14–15	Project, Final Quiz	38	Dec 6	no lecture
		39	Dec 6

* Check CourSys for the actual due dates and times.