13.03.2025
Probability
A course by
Viktor Mehandzhiyski
$99.00
Lifetime access
14-Day Money-Back Guarantee
What you get:
- 5 hours of content
- 97 Interactive exercises
- 19 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
Lifetime access
$99.00
Lifetime access
14-Day Money-Back Guarantee
What you get:
- 5 hours of content
- 97 Interactive exercises
- 19 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Acquire foundational knowledge in probability theory
- Become familiar with key probability terms and ideas
- Explore the practical applications of probability theory
- Be able to determine what kind of distribution a dataset follows
- Learn how to describe events and analyze their interactions
- Use and interpret Bayesian notation
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Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
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1.1 Course Introduction
3 min

1.2 What is the Probability Formula
7 min

1.4 Expected Values
5 min

1.6 Probability Frequency Distribution
5 min

1.8 Complements
5 min

2.1 Fundamentals of Combinatorics
1 min
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.








Curriculum
- 2. Combinatorics12 Lessons 44 MinThis section is designed to teach you what combinatorics is and where we encounter it in life. We will consider the three central concepts in combinatorics – permutations, variations, and combinations – and you’ll learn how to calculate each of these with the correct formulas.Fundamentals of Combinatorics1 minComputing Permutations3 minSolving Factorials4 minVariations with Repetition3 minVariations without Repetition4 minCombinations without Repetition5 minCombinations with Repetition Read now1 minSymmetry of Combinations3 minCombinations with Separate Sample Spaces3 minWinning The Lottery3 minSummary of Combinatorics3 minPractical Example - Combinatrics11 min
- 3. Bayesian Inference12 Lessons 54 MinHere you will learn how to describe events and the ways they interact with one another. We introduce important concepts like intersections, unions, and conditional probability. Then we focus on Bayes’ Law and how to use it to interpret the relationships between the possible outcomes of various events.Sets and Events4 minThe Different Ways Events Can Interact4 minThe Intersection of Two Sets2 minThe Union of Two Sets5 minMutually Exclusive Sets2 minDependent and Independent Events3 minConditional Probability4 minLaw of Total Probability3 minAdditive Law2 minMultiplication Rule4 minBayes Rule6 minPractical Example - Bayesian Inference15 min
- 4. Discrete Distributions7 Lessons 33 MinIn this section of the probability for data science course, you will learn to determine what kind of distribution a dataset follows. This is crucial in making accurate predictions about the future. We talk about the possible values a random variable can take and how frequently they occur. We introduce well-known distributions and events that follow them, and then proceed to discuss each common distribution in greater detail.An Overview of Distributions6 minTypes of Distributions8 minDiscrete Distributions2 minUniform Distribution2 minBernoulli Distribution3 minBinomial Distribution7 minPoisson Distribution5 min
- 5. Continuous Distributions8 Lessons 41 MinHere, you will build upon the probability distributions knowledge you developed in the previous section of the probability class. We review several of the most widely encountered continuous distributions and discuss how to determine them, where they are applied, and how to apply their formulas.Continuous Distributions7 minNormal Distribution4 minStandardizing a Normal Distribution4 minStudents T Distribution2 minChi-Squared Distribution2 minExponential Distribution3 minLogistic Distribution4 minPractical Example - Distributions15 min
- 6. Probability in Other Fields3 Lessons 18 MinIn this section, we spend a minute exploring the tie-ins between this field and several others, such as finance, statistics and data science.Probability in Finance8 minProbability in Statistics6 minProbability in Data Science4 min
Topics
ProbabilityCombinatoricsBayesian InferenceBayes TheoremProbability DistributionsMath & Statistics
Course Requirements
- No prior experience or knowledge is required. We will start from the basics and gradually build your understanding. Everything you need is included in the course.
Who Should Take This Course?
Level of difficulty: Beginner
- People who want to improve their decision-making skills
- Aspiring data analysts, data scientists, business analysts
- Graduate students who need probability for their studies
- Business executives who are passionate about developing a probabilistic mindset
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

A Hamilton College graduate, Viktor has a strong analytics background, focusing on the fields of Statistics, Econometrics, Financial Time-Series Econometrics, and Behavioral Economics. Viktor’s coding experience is rather diverse – from working with C, C++, and Python through to the more math/econ-oriented MATLAB and STATA. He has been fascinated by coding algorithms since the age of 11 and describes himself as a “Bachelor of Science and overall cool guy”. We couldn’t agree more. Some of Viktor’s personal achievements include developing a model for forecasting transfer prices of soccer players across Europe’s top divisions and Stock Market Indexes analysis on the effects of contagion on the effectiveness of international portfolio diversification.
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