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Probability is probably the most fundamental skill you need to acquire if you want to be successful in the world of business. What most people don’t realize is that having a probabilistic mindset is much more important than knowing “absolute truths”.

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Probability is probably the most fundamental skill you need to acquire if you want to be successful in the world of business. What most people don’t realize is that having a probabilistic mindset is much more important than knowing “absolute truths”.

You are already here, so actually you know that.

And it doesn’t matter if it is pure probability, statistics, business intelligence, finance or data science where you want to apply your probability knowledge…

Probability for Statistics and Data Science has your back.

This is the place where you’ll take your career to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions.

You may be wondering: “Hey, but what makes this course better than all the rest?”

Probability for Statistics and Data Science has been carefully crafted to reflect the most in-demand skills that will enable you to understand and compute complicated probabilistic concepts. This course is:

  • Easy to understand

  • Comprehensive

  • Practical

  • To the point

  • Beautifully animated (with amazing video quality)

Packed with plenty of exercises and resources

That’s all great, but what will you actually learn? Probability. And nothing less.

To be more specific, we focus on the business implementation of probability concepts. This translates into a comprehensive course consisting of:

  • An introductory part that will acquaint you with the most basic concepts in the field of probability: event, sample space, complement, expected value, variance, probability distribution function

  • We gradually build on your knowledge with the first widely applicable formulas:

  • Combinatorics or the realm of permutations, variations, and combinations. That’s the place where you’ll learn the laws that govern “everyday probability”

  • Once you’ve got a solid background, you’ll be ready for some deeper probability theory – Bayesian probability.

  • Have you seen this expression: P(A|B) = P(B|A)P(A)/P(B) ? That’s the Bayes’ theorem – the most fundamental building block of Bayesian inference. It seems complicated but it will take you less than 1 hour to understand not only how to read it, but also how to use it and prove it

  • To get there you’ll learn about unions, intersections, mutually exclusive sets, overlapping sets, conditional probability, the addition rule, and the multiplication rule

Most of these topics can be found online in one form or another. But we are not bothered by that because we are certain of the outstanding quality of teaching that we provide.

What we are really proud of, though, is what comes next in the course. Distributions.

Distributions are something like the “heart” of probability applied in data science. You may have heard of many of them, but this is the only place where you’ll find detailed information about many of the most common distributions.

  • Discrete: Uniform distribution, Bernoulli distribution, Binomial distribution (that’s where you’ll see a lot of the combinatorics from the previous parts), Poisson

  • Continuous: Normal distribution, Standard normal distribution, Student’s T, Chi-Squared, Exponential, Logistic

Not only do we have a dedicated video for each one of them, how to determine them, where they are applied, but also how to apply their formulas.

Finally, we’ll have a short discussion on 3 of the most common places where you can stumble upon probability:

  • Finance

  • Statistics

  • Data Science

    If that’s not enough, keep in mind that we’ve got real-life cases after each of our sections. We know that nobody wants to learn dry theory without seeing it applied to real business situations so that’s in store, too.

We think that this will be enough to convince you curriculum-wise. But we also know that you really care about WHO is teaching you, too.

Teaching is our passion

We worked hard for over four months to create the best possible Probability course that would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, are just some of the perks you will get. What else?

Exceptional Q&A support. Yes. That’s our favorite part – interacting with you on the various topics you learn about (and you are going to love it, too. )

What makes this course different from the rest of the Probability courses out there?

  • High-quality production – HD video and animations (This isn’t a collection of boring lectures. )

  • Knowledgeable instructor (an adept mathematician who has competed at an international level) who will bring you not only his probability knowledge but the complicated interconnections between his areas of expertise – finance and data science

  • Comprehensive – we will cover all major probability topics and skills you need to level up your career

  • Extensive Case Studies - helping you reinforce everything you’ve learned

  • Exceptional support – we said that, but let’s say it again - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day

  • Succinct – the biggest investment you’ll make is your own time. And we will not waste it. All our teaching is straight to the point

    Still not convinced?

Here’s why you need these skills?

  1. Salary/Income – most businesses are starting to realize the advantages of implementing data-driven decisions. And those are all stepping on probability. A probabilistic mindset is definitely one of the non-automatable skills that managers of the next decade will be expected to have

  2. Promotions and secure future – If you understand probability well, you will be able to back up your business and positions in much more convincing way, draining from quantitative evidence; needless to say, that’s the path to career growth

  3. New horizons – probability is a pathway to many positions in any industry. While it is rarely a full-time position, it is crucial for most business jobs nowadays. And it’s not a boring aspect.

Please bear in mind that the course comes with Udemy’s 30-day money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you.

Let's start learning together now.

Enroll now

What's inside

Learning objectives

  • Understand probability theory
  • Discover combinatorics
  • Learn how to use and interpret bayesian notation
  • Different types of distributions variables can follow

Syllabus

Introduction to Probability
What does the course cover?
What is the probability formula?
How to compute expected values?
Read more
What is a probability frequency distribution?
What is a complement?
Combinatorics
Why are combinatorics useful?
When do we use Permutations?
Solving Factorials
Why can we use certain values more than once?
What if we couldn't use certain values more than once?
Computing Variations without Repetition
What are combinations and how are they similar to variations?
What is "symmetry" in Combinations?
How do we combine combinations of events with separate sample spaces?
What is the chance of a single ticket winning the lottery?
What is the chance of winning the lottery?
A Summary of Combinatorics
Practical Example: Combinatorics
Bayesian Inference
What is a set?
What are the different ways two events can interact with one another?
What is the intersection of sets A and B?
What is the union of sets A and B?
Are all complements mutually exclusive?
What does it mean to for two events to be dependent?
What is the difference between P(A|B) and P(B|A)?
Conditional Probability in Real-Life
How do we apply the additive rule?
How do we derive the Multiplication Rule formula?
How do we interpret the Multiplication Rule Formula?
When do we use Bayes' Theorem in Real Life?
Bayes' Theorem
Practical Example: Bayesian Inference
Distributions
What is a probability distribution?
What are the two main types of distributions based on the type of data we have?
Discrete Distributions and their characteristics.
Discrete Distributions and Their Characteristics.
What is the Discrete Uniform Distribution?
What is the Bernoulli Distribution?
What is the Binomial Distribution?
What is the Poisson Distribution?
What is a Continuous Distribution?
What is a Normal Distribution?
Standardizing a Normal Distribution
How do we Standardize a Normal Distribution?
What is a Student's T Distribution?
What is a Chi Squared Distribution?
What is a Chi-Squared Distribution?
What is an Exponential Distribution?
What is the Logistic Distribution?
What is a Logistic Distribution?
Practical Example: Distributions
Students will see instances of probability in other fields of interest.
Tie-ins to Finance
Tie-ins to Statistics
Tie-ins to Data Science

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches key probability principles which provide a foundation in data science
Suitable for beginners with no prior knowledge of probability
Covers advanced topics such as Bayesian probability and probability distributions
Provides practical examples and case studies to reinforce learning
Taught by an experienced instructor with a strong background in mathematics and data science

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Probability for Statistics and Data Science with these activities:
Review Course Notes and Assignments
Regularly reviewing the course materials and assignments improves your retention and understanding of probability.
Browse courses on Probability
Show steps
  • Review lecture notes.
  • Review textbook chapters.
  • Review assignments and quizzes.
  • Summarize key concepts.
Read Thinking and Deciding
This book will help you improve your understanding of probability in making better decisions and implementing more effective strategies.
Show steps
  • Read the introduction.
  • Read Chapter 1: The Basics of Probability.
  • Read Chapter 2: The Psychology of Probability.
  • Read Chapter 3: Probability and Decision Making.
Solve Probability Problems
This activity will strengthen your problem-solving skills and help you identify and understand probability concepts more deeply.
Browse courses on Probability
Show steps
  • Find a set of probability problems.
  • Solve the problems.
  • Check your answers.
  • Review the problems you got wrong.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a Probability Study Group
Working with peers to discuss concepts and solve problems can improve your understanding and retention of probability.
Browse courses on Probability
Show steps
  • Find a probability study group.
  • Attend the study group meetings.
  • Participate in the discussions.
  • Help other students with their understanding.
Learn About Bayesian Inference
This will provide you with a more comprehensive understanding of Bayesian inference and how it can be applied to real-world problems.
Browse courses on Bayesian Inference
Show steps
  • Find a tutorial on Bayesian inference.
  • Watch the tutorial.
  • Read the accompanying materials.
  • Complete the practice exercises.
Create a Probability Glossary
Creating a glossary will help you understand and retain the key terms and concepts in probability.
Browse courses on Probability
Show steps
  • Gather a list of probability terms.
  • Define each term.
  • Organize the definitions into a glossary.
Write a Probability-Based Essay
Writing an essay requires a deep understanding of probability concepts and allows you to express your thoughts and ideas clearly.
Browse courses on Probability
Show steps
  • Choose a topic.
  • Research the topic.
  • Develop a thesis statement.
  • Write an outline.
  • Write the essay.
Develop a Probability-Based Model
This will allow you to apply the probability concepts you've learned to a real-world problem and develop your critical thinking and problem-solving skills.
Browse courses on Probability
Show steps
  • Choose a topic and develop a research question.
  • Collect the available data.
  • Develop a probability model.
  • Test the model's accuracy against data.
  • Present the findings.

Career center

Learners who complete Probability for Statistics and Data Science will develop knowledge and skills that may be useful to these careers:
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a strong foundation in probability theory and distributions, which are essential for understanding financial models and making informed investment decisions. By completing this course, you can gain the skills and knowledge necessary to succeed in this demanding field.
Statistician
Statisticians collect, analyze, and interpret data to solve real-world problems. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding statistical models and making informed conclusions. By completing this course, you can gain the skills and knowledge necessary to succeed in this challenging field.
Actuary
Actuaries use mathematical and statistical models to assess and manage risk. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding actuarial models and making informed risk assessments. By completing this course, you can gain the skills and knowledge necessary to succeed in this highly specialized field.
Risk Manager
Risk Managers identify, assess, and manage risks. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding risk assessment and management. By completing this course, you can gain the skills and knowledge necessary to succeed in this challenging field.
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting large amounts of data in order to extract meaningful insights and patterns. This course provides a comprehensive overview of probability theory, combinatorics, Bayesian inference, and distributions, which are all essential concepts for success in this field. By understanding these fundamental principles, you can develop the skills necessary to build predictive models, identify trends, and make data-driven decisions.
Financial Analyst
Financial Analysts use financial data to make investment recommendations and advise clients on financial matters. This course provides a strong foundation in probability theory and distributions, which are essential for understanding financial models and making informed investment decisions. By completing this course, you can gain the skills and knowledge necessary to succeed in this challenging field.
Investment Analyst
Investment Analysts make investment recommendations and advise clients on financial matters. This course provides a strong foundation in probability theory and distributions, which are essential for understanding financial models and making informed investment decisions. By completing this course, you can gain the skills and knowledge necessary to succeed in this rewarding field.
Business Analyst
Business Analysts use data to analyze business processes and identify opportunities for improvement. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding business data and making informed decisions. By completing this course, you can gain the skills and knowledge necessary to succeed in this rewarding field.
Financial Planner
Financial Planners help individuals and families manage their finances. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding financial planning and making informed decisions. By completing this course, you can gain the skills and knowledge necessary to succeed in this rewarding field.
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models to solve complex problems. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding machine learning algorithms and making informed decisions. By completing this course, you can gain the skills and knowledge necessary to succeed in this rapidly growing field.
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding data quality and performance. By completing this course, you can gain the skills and knowledge necessary to succeed in this rapidly growing field.
Market Researcher
Market Researchers conduct research to understand consumer behavior and market trends. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding sampling and statistical analysis. By completing this course, you can gain the skills and knowledge necessary to succeed in this rewarding field.
Auditor
Auditors examine financial records to ensure accuracy and compliance. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding sampling and statistical analysis. By completing this course, you can gain the skills and knowledge necessary to succeed in this challenging field.
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding software reliability and performance. By completing this course, you can gain the skills and knowledge necessary to succeed in this challenging field.
Database Administrator
Database Administrators design, build, and maintain databases. This course provides a comprehensive overview of probability theory and distributions, which are essential for understanding database reliability and performance. By completing this course, you can gain the skills and knowledge necessary to succeed in this challenging field.

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Probability for Statistics and Data Science.
"Probability and Measure" classic text that provides a rigorous and comprehensive treatment of probability theory, measure theory, and their applications. It's an excellent reference for advanced learners and researchers.
"The Elements of Statistical Learning" comprehensive textbook that covers a wide range of statistical learning methods, including linear and nonlinear models, tree-based methods, and support vector machines. It's an excellent resource for those interested in gaining a deep understanding of statistical learning.
"Probability Theory: A Comprehensive Course" provides a comprehensive overview of probability theory, including advanced topics like Markov chains and stochastic processes. It's an excellent reference for those seeking deeper knowledge.
"Pattern Recognition and Machine Learning" provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, feature selection, and model evaluation. It's an excellent resource for those interested in learning about the principles and applications of machine learning.
"Introduction to Statistical Learning" offers a practical guide to statistical learning methods, including supervised and unsupervised learning, model selection, and evaluation. It's a valuable resource for those interested in applying statistical techniques in data science.
"Data Mining: Practical Machine Learning Tools and Techniques" offers a practical guide to data mining and machine learning, covering topics such as data preparation, feature selection, model building, and evaluation. It's an excellent resource for those interested in learning about the practical aspects of data mining and machine learning.
"Statistical Rethinking: A Bayesian Course with Examples in R and Stan" introduces Bayesian statistical modeling and inference using R and Stan. It's a valuable resource for learning Bayesian techniques and applying them in practice.
"Reinforcement Learning: An Introduction" provides a comprehensive overview of reinforcement learning, including fundamental concepts, algorithms, and applications. It's a valuable resource for those interested in learning about reinforcement learning and its potential.
"Bayesian Data Analysis" provides a thorough introduction to Bayesian statistical methods and their practical applications. It covers key concepts, algorithms, and case studies, offering valuable insights for data science.
"Deep Learning" comprehensive guide to deep learning, covering neural networks, convolutional neural networks, recurrent neural networks, and other advanced topics. It's an excellent resource for those interested in exploring deep learning and its applications.
"Probability and Statistics for Data Science" explores the foundational principles of probability and statistics and offers comprehensive coverage of core concepts, tools, and techniques. It's an excellent resource for deepening your understanding of the fundamental principles and techniques.
"Combinatorics: A Guided Tour" offers a comprehensive exploration of combinatorics, covering topics like counting, permutations, combinations, and graph theory. It provides a strong foundation for understanding combinatorics and its applications.
"Introduction to Probability" offers a clear and accessible introduction to the foundations of probability theory and its applications. It's a valuable reference for refreshing your understanding of probability concepts.
"Sets, Logic and Maths for Computing" covers set theory, logic, and mathematical structures, providing a foundation for understanding probability and statistics. It's a valuable resource for those seeking a deeper understanding of the underlying mathematical concepts.
"Probability for Data Science: A Gentle Introduction" beginner-friendly guide to probability theory tailored for data science. It offers a clear and intuitive approach to the subject, making it an excellent starting point.

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