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Data Science

Data Science: Metro-rider Snapshots

Survey probability, statistics, linear algebra, visualization, and machine learning foundations using Python.

Instructor Dr. Waleed A. Yousef Adjunct Professor, University of Victoria, Canada
A scatter plot, regression line, and bars revealing patterns in data

01

Course overview

Survey probability, statistics, linear algebra, visualization, and machine learning foundations using Python.

02

What you will learn

This course serves as an overview of a comprehensive field comprising 15+ courses. It is not a prerequisite for any other course, nor is any other course a prerequisite for it, except for basic knowledge of calculus and probability at the high school or elementary college level. The primary goal of this course is to motivate students to pursue further studies in the field and excel in each course.

The course begins by covering the fundamentals of probability theory, statistics, linear algebra, and data visualization. It then progresses to introduce the basics of Statistical Decision Theory, followed by an exploration of Linear Models and Regression. The course will derive and explain Bayes' classifier, along with its application to multinormal distributions. This will lead to the definition of various statistical concepts such as estimation, loss function, and risk minimization.

Since each of these topics is extensively covered in standalone courses within the field (such as probability, statistics, and pattern recognition), this course will focus on practical applications rather than theoretical aspects. Mathematical derivations and heavy mathematical treatment will not be extensively covered. Emphasis will be placed on developing intuition and solving computer problems using Python.

03

Lecture syllabus

Video titles and durations as published on Arabsera. The advertised lecture total may differ from the number of video entries, which can include introductions, tutorials, and split sessions.

Video titles and durations 24
  1. 01 "Data Science": introduction, what, why?
  2. 02 Snapshot of Probability: PDF, CDF, Normal Distribution
  3. 03 Snapshot of Probability: Joint Distributions
  4. 04 Snapshot of Probability: Independent Random Variables
  5. 05 Snapshot of Probability: Expectation and Variance
  6. 06 Snapshot of Probability: Bias-Variance Decomposition
  7. 07 Snapshot of Probability: Covariance and Correlation
  8. 08 Snapshot of Probability: Conditional expectation and Prediction
  9. 09 Snapshot of Statistics: Sampling, Statistics, and WLLN
  10. 10 Snapshot of Statistics: Estimation, mean, variance, covariance
  11. 11 Snapshot of Statistics: Estimation, median, quantile, outliers
  12. 12 Snapshot of Data Visualization: introduction, rug plot, histogram
  13. 13 Snapshot of Data Visualization: box-plot and categorical variables
  14. 14 Snapshot of Data Visualization: scatter plot
  15. 15 Snapshot of Data Visualization: rigor, higher dimensions, contemplation
  16. 16 Snapshot of Linear Algebra: back to school and visual space
  17. 17 Snapshot of Linear Algebra: rules of matrix operations
  18. 18 Snapshot of Linear Algebra: projection
  19. 19 Snapshot of Multivariate Probability and Statistics: multivariate probability
  20. 20 Snapshot of Multivariate Probability and Statistics: multivariate statistics
  21. 21 Snapshot of Multivariate Probability and Statistics: random vector projection (transformation)
  22. Snapshot of Optimization: Introduction(1/3)
  23. Snapshot of Optimization: introduction(2/3)
  24. Snapshot of Optimization: introduction(3/3)

04

Teaching support (check your plan)

  • Discussion groups
  • TA-human texting for Q&A
  • TA-GPT (coming soon)

05

Textbook

No textbook.

06

Certificate

Awarded after passing a brief sample exam, which you may attempt multiple times.