Data Science: Probability from edX

In this course, part of our Professional Certificate Program in Data Science,you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance.

Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.

What's inside

Learning objectives

Important concepts in probability theory including random variables and independence
How to perform a monte carlo simulation

The meaning of expected values and standard errors and how to compute them in r
The importance of the central limit theorem

Important concepts in probability theory including random variables and independence
How to perform a monte carlo simulation
The meaning of expected values and standard errors and how to compute them in r
The importance of the central limit theorem

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Builds a strong foundation for beginners with key concepts in probability theory, including random variables and independence

Develops core skills in probability theory for data scientists, such as expected values, standard errors, and the Central Limit Theorem

Teaches foundational probability concepts for analyzing data affected by chance, essential for data scientists

Introduces important concepts in probability theory through hands-on simulations and real-world examples

Taught by Rafael Irizarry, a renowned biostatistician and data scientist

Reviews summary

Solid probability foundation for data science

According to learners, this course offers a strong foundation in probability essential for data science. Reviewers frequently praised the clear explanations and found the R labs helpful for reinforcing understanding. While many appreciate the in-depth theoretical coverage, some felt it lacked practical application and needed supplementing. Opinions differ on prerequisites; some note it's manageable with basic math/R, but others found it assumed some prior knowledge, potentially making it challenging for beginners. Overall, it's considered a valuable and often essential component for serious data science students.

Hands-on practice with R is helpful.

"...and the R labs help solidify understanding."

"The R exercises are good practice, but sometimes assume a bit more R knowledge than a complete beginner might have."

"R exercises are useful. A good building block for the rest of the certificate program."

Concepts are explained very clearly.

"This course provides an excellent foundation in probability theory for data science. The concepts are explained very clearly, and the R labs help solidify understanding."

"Fantastic course! Dr. Irizarry does a great job explaining complex ideas simply. The assignments reinforce the lectures well."

"Loved this course! The lectures are clear, the examples are spot on, and the assignments really make you think."

Provides a solid base for data science.

"This course provides an excellent foundation in probability theory for data science."

"Fantastic course!... This course gave me the probabilistic thinking necessary for understanding more advanced data science topics. Essential part of the professional certificate."

"Solid foundation. Cleared up many misconceptions I had about probability..."

"I finally understand probability in a way that's useful for analyzing data."

Some prior math/R knowledge helpful.

"Needed to supplement with other resources to fully grasp certain concepts."

"The R exercises are good practice, but sometimes assume a bit more R knowledge than a complete beginner might have."

"The pace is manageable if you have some basic math background."

"If you're a complete beginner, some parts might be difficult."

Strong focus on theoretical concepts.

"Expected more practical application. This course is very theoretical, focused heavily on mathematical concepts without showing enough real-world implementation..."

"I found some of the explanations a bit dry and overly theoretical. Needed to supplement with other resources to fully grasp certain concepts."

"Fairly basic introduction. If you have a strong math background, you might find this too simple... The R parts were okay, but didn't push me."

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 Data Science: Probability with these activities:

Review Probability Basics

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Helps refresh and recall previously learned probability concepts to strengthen the foundation for this course.

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Review the definition of probability and different types of probability distributions.
Solve practice problems on calculating probabilities using basic formulas and rules.
Read through examples and case studies to understand the practical applications of probability.

Read: 'Probability and Statistics' by Morris H. DeGroot

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Provides a comprehensive reference for further exploration and reinforcement of probability concepts.

View Probability and Statistics on Amazon

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Read selected chapters relevant to the course material.
Review key concepts and examples to enhance understanding.

Random Variable Exercises

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Provides focused practice on working with random variables, enhancing understanding of their behavior and properties.

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Work through a set of exercises on identifying and classifying random variables.
Practice calculating probability distributions for different types of random variables.
Analyze real-world scenarios involving random variables to apply knowledge.

Three other activities

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Central Limit Theorem Discussion Group

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Fosters collaboration and deepens understanding through peer discussions on the Central Limit Theorem and its applications.

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Join a study group or online forum to connect with peers.
Participate in discussions, sharing insights and asking questions about the Central Limit Theorem.
Work together to solve problems and clarify concepts related to the theorem.

Monte Carlo Simulation Tutorial

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Builds practical skills in performing Monte Carlo simulations, enabling students to apply them in real-world scenarios.

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Follow an interactive tutorial on the principles and steps of Monte Carlo simulation.
Implement a Monte Carlo simulation in R or Python to gain hands-on experience.
Analyze the results of the simulation to draw inferences and make predictions.

Expected Values and Standard Errors Project

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Encourages students to apply their knowledge by calculating expected values and standard errors in a practical context.

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Gather real-world data related to a specific research question.
Calculate the expected value and standard error for the data collected.
Write a report summarizing the findings and implications of the analysis.

Career center

Learners who complete Data Science: Probability will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use statistical methods to uncover patterns in data. Understanding probability theory is essential to drawing conclusions from data and making sound business decisions. This course will provide you with a strong foundation in probability theory and teach you how to apply these concepts to real-world data analysis. With these skills, you'll be well-equipped to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians use probability theory to develop models for making inferences about populations. This course will provide you with a strong foundation in probability theory and teach you如何应用这些概念来构建统计模型。With these skills, you'll be well-prepared to succeed as a Statistician.

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Quantitative Analyst

Quantitative Analysts use probability theory to develop models for making investment decisions.

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Actuary

Actuaries use probability theory to develop models for assessing risk.

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Data Scientist

Data Scientists use probability theory to develop models for making predictions and classifications.

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Risk Manager

Risk Managers use probability theory to develop models for assessing risk.

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Financial Analyst

Financial Analysts use probability theory to develop models for making investment decisions.

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Operations Research Analyst

Operations Research Analysts use probability theory to develop models for optimizing business operations.

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Market Researcher

Market Researchers use probability theory to develop models for understanding consumer behavior.

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Epidemiologist

Epidemiologists use probability theory to develop models for understanding the spread of disease.

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Biostatistician

Biostatisticians use probability theory to develop models for understanding biological data.

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Economist

Economists use probability theory to develop models for understanding economic behavior.

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Insurance Underwriter

Insurance Underwriters use probability theory to develop models for assessing risk.

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Teacher

Teachers may use probability theory to help students understand statistics and probability.

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Software Engineer

Software Engineers may use probability theory to develop models for software reliability.

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Data Science

Probability

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Reviews summary

Solid probability foundation for data science

Activities

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