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Probability and Statistics in Data Science using Python

Alon Orlitsky and Yoav Freund

1.8

Based on 32 ratings

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Alon Orlitsky and Yoav Freund

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL.

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL.

What you'll learn

The mathematical foundations for machine learning
Statistics literacy: understand the meaning of statements such as "at a 99% confidence level"

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What's inside

Learning objectives

The mathematical foundations for machine learning

Statistics literacy: understand the meaning of statements such as "at a 99% confidence level"

The mathematical foundations for machine learning
Statistics literacy: understand the meaning of statements such as "at a 99% confidence level"

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops the mathematical foundations for machine learning, which is highly relevant to industry

Taught by Alon Orlitsky and Yoav Freund, who are recognized for their work in machine learning and data science

Uses Jupyter notebooks for hands-on experience, which is highly valued in the field

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Reviews summary

Foundations in probability & statistics with python

According to learners, this course provides a solid mathematical and statistical foundation essential for data science, often described as a challenging yet rewarding experience. Students highlight the value of the clear theoretical explanations paired with practical application using Python and Jupyter notebooks. Many note that the material is dense and moves at a fast pace, often requiring a strong prerequisite knowledge in mathematics, making it potentially difficult for beginners. While covering a broad range of topics, some learners found the depth on certain advanced subjects could be increased. The course is frequently recommended for those serious about the mathematical underpinnings of data science.

Covers wide range of essential topics.

"They introduce you to a wide array of topics from probability basics to PCA."

"Good overview of the statistical tools needed for data science."

"It provides exposure to many concepts, though sometimes I wished for more depth on specific areas."

Hands-on application using Python.

"I loved the practical exercises and Jupyter notebooks; they made the theory stick."

"Applying the concepts directly in Python was incredibly helpful for learning."

"The coding assignments were challenging but highly valuable for building skills."

Excellent mathematical and statistical basis.

"This course gave me a very solid mathematical foundation for my data science journey."

"I finally understand the probabilistic and statistical concepts behind many ML algorithms thanks to this."

"Really helped solidify my understanding of key theoretical concepts."

Course content is dense and difficult.

"This course is very rigorous and demanding, much more than I expected."

"I found the pace quite fast, especially covering complex mathematical topics."

"Expect to spend a lot of time reviewing lectures and solving problems."

Assumes prior math and programming knowledge.

"Be warned, you need a solid background in calculus and linear algebra before starting."

"I struggled because I underestimated the required mathematical prerequisites."

"The course assumes you are comfortable with Python and basic programming concepts already."

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 and Statistics in Data Science using Python with these activities:

Review linear algebra

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Review basic concepts in linear algebra such as vector spaces, matrix operations, and linear transformations to prepare for the more advanced topics covered in this course.

Browse courses on Linear Algebra

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Review lecture notes from a previous linear algebra course or textbook.
Solve practice problems to reinforce your understanding.
Attend a refresher workshop or tutorial on linear algebra.

Read Introduction to Probability

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Provide a comprehensive introduction to the fundamental concepts of probability theory that will be used throughout the course.

View A Course on Reinforcement Learning on Amazon

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Read each chapter thoroughly and take notes on key concepts.
Solve the practice exercises at the end of each chapter.
Discuss the material with classmates or a study group.

Follow tutorials on probability distributions

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Provide hands-on experience in working with different probability distributions, which are essential for understanding the behavior of random variables.

Browse courses on Probability Distributions

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Find online tutorials or video lectures on probability distributions.
Work through the examples and exercises provided in the tutorials.
Apply the concepts to real-world examples.

Four other activities

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Solve practice problems on hypothesis testing

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Strengthen problem-solving skills in hypothesis testing, a fundamental technique for making inferences from data.

Browse courses on Hypothesis Testing

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Find practice problems on hypothesis testing from textbooks or online resources.
Solve the problems and compare your answers with the provided solutions.
Identify areas where you need improvement and focus on practicing those concepts.

Create a blog post on a statistical concept

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Deepen understanding of a statistical concept by explaining it to others in a clear and concise manner.

Browse courses on Probability

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Choose a statistical concept that you are familiar with.
Write a blog post that explains the concept in a way that is easy to understand.
Share the blog post with others and get feedback.

Develop a data visualization project

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Provide practical experience in presenting data effectively, which is crucial for communicating insights from probability and statistics.

Browse courses on Data Visualization

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Choose a dataset and identify the key insights you want to convey.
Select appropriate visualization techniques and create the visualizations.
Write a report that explains the visualizations and the insights they provide.

Contribute to an open-source statistical software project

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Gain practical experience in applying statistical concepts by contributing to real-world software projects.

Browse courses on Open Source

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Find an open-source statistical software project that aligns with your interests.
Identify an area where you can contribute, such as bug fixing or feature development.
Submit a pull request with your contributions and get feedback from the project maintainers.

Career center

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

Data Analyst

Data Analysts use their understanding of probability and statistics to interpret data. They use this understanding to make recommendations to businesses and other organizations. This course can help you develop the skills you need to become a successful Data Analyst. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians use their knowledge of probability and statistics to design and conduct studies, collect and analyze data, and interpret the results. This course can help you develop the skills you need to become a successful Statistician. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers use their understanding of probability and statistics to build and train machine learning models. These models can be used to solve a variety of problems, such as predicting customer churn, detecting fraud, and recommending products. This course can help you develop the skills you need to become a successful Machine Learning Engineer. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of probability and statistics to extract insights from data. These insights can be used to make better decisions, improve products, and develop new strategies. This course can help you develop the skills you need to become a successful Data Scientist. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

Quantitative Analysts use their understanding of probability and statistics to develop and implement financial models. These models can be used to value assets, assess risk, and make investment decisions. This course can help you develop the skills you need to become a successful Quantitative Analyst. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use their understanding of probability and statistics to assess risk and develop insurance policies. This course can help you develop the skills you need to become a successful Actuary. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts use their understanding of probability and statistics to improve the efficiency of business operations. This course can help you develop the skills you need to become a successful Operations Research Analyst. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Operations Research Analyst

Biostatistician

Biostatisticians use their understanding of probability and statistics to design and conduct clinical trials, collect and analyze data, and interpret the results. This course can help you develop the skills you need to become a successful Biostatistician. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Biostatistician

Risk Manager

Risk Managers use their understanding of probability and statistics to assess and manage risk. This course can help you develop the skills you need to become a successful Risk Manager. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Risk Manager

Financial Analyst

Financial Analysts use their understanding of probability and statistics to analyze financial data and make investment recommendations. This course can help you develop the skills you need to become a successful Financial Analyst. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Financial Analyst

Insurance Underwriter

Insurance Underwriters use their understanding of probability and statistics to assess risk and set insurance rates. This course can help you develop the skills you need to become a successful Insurance Underwriter. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Insurance Underwriter

Data Engineer

Data Engineers use their understanding of probability and statistics to design and build data pipelines. These pipelines can be used to collect, clean, and store data. This course can help you develop the skills you need to become a successful Data Engineer. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers use their understanding of probability and statistics to develop and test software. This course can help you develop the skills you need to become a successful Software Engineer. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers use their understanding of probability and statistics to understand user needs and develop new products. This course can help you develop the skills you need to become a successful Product Manager. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts use their understanding of probability and statistics to analyze business data and make recommendations. This course can help you develop the skills you need to become a successful Business Analyst. It will teach you the mathematical foundations of probability and statistics, as well as how to apply this theory to real-world data.

See salaries and explore the career path for Business Analyst

Reading list

We've selected 22 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 and Statistics in Data Science using Python.

Probabilistic Machine Learning

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Aligns well with the course's focus on the probabilistic foundations of machine learning.

Probability and Statistics in Data Science using Python

What you'll learn

What's inside

Learning objectives

Traffic lights

Save this course

Reviews summary

Foundations in probability & statistics with python

Activities

Career center

Reading list

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