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
  15. How Spark Calculates
  16. Working With Spark
  17. Other DataFrame Tools
  18. Warehouse
  19. Aside: NumPy/Pandas Speed
  20. Communicating
  21. More Data Science

Course home page.

Schedule, Fall 2024

Week Deliverables (*) Lecture Hour Lecture Date First Slide Video Link
1 1 Sep 4 Intro 1-1 (0:49)
2 Exer 1 2 Sep 9 Analyzing Data 2-1 (0:48)
3 Sep 9 Working With Pandas 2-2 (0:49)
4 Sep 11 exercise time (in CSIL)
3 Exer 2 5 Sep 16 Noise Filtering 3-1 (0:48)
6 Sep 16 Kalman Example 3-2 (0:49
7 Sep 18 exercise time (in CSIL)
4 Exer 3 8 Sep 23 Cleaning 4-1 (0:49)
9 Sep 23 Stats Review 4-2 (0:48)
10 Sep 25 exercise time (in CSIL)
5 Exer 4 11 Sep 30 Truth and Reconciliation Day
12 Sep 30
13 Oct 2 exercise time (in CSIL)
6 Exer 5 14 Oct 7 Test Assumptions 6-1 (0:48)
15 Oct 7 Hacking p-values 6-2 (0:50)
16 Oct 9 exercise time (in CSIL)
7 Exer 6, Quiz 1 17 Oct 15 ML 7-1 (0:48)
18 Oct 15 ML Classification 7-2 (0:48)
19 Oct 16 Quiz 1
8 Exer 7 20 Oct 21 More Than Points 8-1 (0:48)
21 Oct 21 Higher Dimensions 8-2 (0:50)
22 Oct 23 exercise time (in CSIL)
9 Exer 8 23 Oct 28 Other ML
24 Oct 28
25 Oct 30 exercise time (in CSIL)
10 Exer 9 26 Nov 4
27 Nov 4
28 Nov 6 exercise time (in CSIL)
11 Exer 10, Quiz 2 29 Nov 11 Remembrance Day
30 Nov 11
31 Nov 13 Quiz 2
12 Exer 11 32 Nov 18
33 Nov 18
34 Nov 20 exercise time (in CSIL)
13 Exer 12 35 Nov 25
36 Nov 25
37 Nov 27 exercise time (in CSIL)
14+ Quiz 3, Project 38 Dec 2 Quiz 3
39 Dec 2

* Check CourSys for the actual due dates and times.

Quiz instruction slide.