COMP 233 Concordia - Probability and Statistics for Computer Science

16 Sep, 2025

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Topics

Topics may differ depending on your professor.

When I took the course back in 2023, the topics included:

1. Introduction to statistics, probability axioms, sample spaces, events.
2. Conditional probability, Bayes' formula, independent events.
3. Random variables, jointly distributed random variables.
4. Expectation, variance, covariance, moments, Chebyshev inequality.
5. The Bernoulli, binomial, and Poisson random variables.
6. The uniform and exponential distributions.
7. The normal, Chi square, and t distributions.
8. Distribution of sampling statistics.
9. Sample mean, variance, and distribution; central limit theorem.
10. Parameter and interval estimation, maximum likelihood estimators.
11. Hypothesis testing.
12. Least squares approximation.
13. Random number generation.

The course followed the textbook "Introduction to probability and statistics for engineers and scientists by Sheldon Ross, 6th edition".

How to study

• Use AI to learn - in a smart manner. Speak with whichever LLM you prefer, feed it your lecture slides and ask it to explain concepts that are tricky to you. Ask it for examples and knowledge checks. Always double check its output though, LLMs are still prone to hallucinations.
• Read your professors slides (most professors provide hints about exam content based on the length of the content covered).
• Work through textbook practice problems, tutorial questions and past papers.
• I cannot share past papers here however, I believe you can find some online. You can also attempt to find exam papers from other universities in Canada as this course is generally the same across universities.
• Topics that will almost certainly come in your final exam include:

A few topics that will almost certainly come in your final exam

1. Permutations, Combinations, Conditional Probability
2. All distribution functions and their question variations
3. All distributions (Binomial, poisson, exponential, normal, bernoulli, etc..)
4. Hypothesis testing and testing methods
5. Linear regression Analysis

My Thoughts

Initially, most CS students think this course is not that practical when it comes to pursuing a career as a software developer. To be frank, as a first year, I was of the same opinion. However, through the years, I have come to learn that it is incredibly important to have some sort of background in probability and statistics. Forget ever getting a high paying job in the finance sector if you don't at least know the basics of this subject, for example.

But, in general, the main ideas taught in this course can be applied to your own life; personal finance, investing and so on. I do agree that the way some professors teach it and the way that universities examine it takes away some of the desire to truly understand the practical real life importance of these topics. You may be left just optimizing for exam success by mass solving past papers, which is fair, I get it. From the bottom of my heart, I recommend you really try to understand topics taught in this course if you even dream of becoming a decent:

1. AI/ ML Engineer
2. Quantitative Developer
3. Data Scientist
4. Risk Analyst
5. Actuarial Engineer

Oh and additionally, if you enjoy reading about finance and statistics, check out Thinking Fast and Slow by Daniel Kahneman, its a timeless classic, in my opinion.

#Concordia