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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.
Read more
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.
Read more
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.
What you'll learn
- The mathematical foundations for machine learning
- Statistics literacy: understand the meaning of statements such as "at a 99% confidence level"
Register for this course and see more details by visiting:
OpenCourser.com/course/nkbj82/probability
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"
Good to know
Good to know
Know what's good ,
what to watch for , 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
Rigorous probability and stats for data science
Students say this course provides mathematically rigorous coverage of probability and statistics, with detailed video lectures, problem sets, and a final exam. However, many students complain that the course is extremely time-consuming, with lengthy videos and challenging problem sets that often require outside research and assistance. Additionally, the course has been criticized for its lack of real-world applications and relevance to data science.
The video lectures are visually engaging and often include helpful diagrams and examples.
"The video lectures are at times entertaining and lots of visuals help with understanding the concept"
The course provides a deep and thorough understanding of the mathematical foundations of probability and statistics.
"The course material is extremely detailed and provides a very good understanding of probability and statistics."
"This course covers almost everything you need to know about the foundational concepts of probability and statistics from a university graduate level standpoint"
The course focuses heavily on theoretical concepts and does not provide many real-world examples or applications.
"The content is not engaging at all, video lessons are much too long (more than 1 hour per topic) and 80% of the time is spent with mathematical proofs instead of real world application examples."
"This creates a huge disconnect from the lecture and the exercises, more than once I spent hours researching for additional content just to solve the exercises."
The problem sets and assignments are often disconnected from the material covered in the video lectures, requiring students to do additional research or seek outside help.
"The problem sets are given were much much much harder than what was taught in the lecture."
"The questions that are being asked are not part of the content and are often at difficulty level on a scale of 1 - 10 @ 9 most of the time"
This course is challenging and requires a significant time investment, often exceeding the advertised 10-15 hours per week.
"It took me more than an hour to solve one problem, and there were many of them."
"There is no way somebody on a full time job will be able to complete this in the provided time, unless they spend all their evenings and nights and weekends working on it."
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on hypothesis testing
Show steps
Strengthen problem-solving skills in hypothesis testing, a fundamental technique for making inferences from data.
Browse courses on
Hypothesis Testing
Show steps
-
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
Show steps
Deepen understanding of a statistical concept by explaining it to others in a clear and concise manner.
Browse courses on
Probability
Show steps
-
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
Show steps
Provide practical experience in presenting data effectively, which is crucial for communicating insights from probability and statistics.
Browse courses on
Data Visualization
Show steps
-
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
Show steps
Gain practical experience in applying statistical concepts by contributing to real-world software projects.
Browse courses on
Open Source
Show steps
-
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.
Review linear algebra
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on hypothesis testing
Show steps
Strengthen problem-solving skills in hypothesis testing, a fundamental technique for making inferences from data.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- 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
Show steps
Deepen understanding of a statistical concept by explaining it to others in a clear and concise manner.
Browse courses on
Probability
Show steps
Show steps
Show steps
- 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
Show steps
Provide practical experience in presenting data effectively, which is crucial for communicating insights from probability and statistics.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- 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
Show steps
Gain practical experience in applying statistical concepts by contributing to real-world software projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- 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:
Insurance Underwriter
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.
Machine Learning Engineer
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.
Actuary
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.
Risk Manager
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.
Biostatistician
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.
Data Scientist
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.
Operations Research Analyst
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.
Statistician
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.
Data Analyst
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.
Financial Analyst
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.
Quantitative Analyst
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.
Data Engineer
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.
Business Analyst
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.
Product Manager
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.
Software 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.
For more career information including salaries, visit:
OpenCourser.com/course/nkbj82/probability
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.
Aligns well with the course's focus on the probabilistic foundations of machine learning.
Save
This textbook is specifically written for an introductory course on probability and statistics for data science. It has a companion website with additional resources, including Jupyter notebooks and solutions to exercises.
Refer to this title for additional reading that provides explanations of concepts and includes many thorough examples and excercises, including solutions.
Consider this title for supplemental reading on Bayesian statistics and data analysis, which valuable perspective for those interested in data science.
Save
This classic textbook provides a comprehensive introduction to probability and statistics. It good choice for students who want a more theoretical and rigorous treatment of the subject.
Refer to this book for supplemental reading and reference on statistical learning techniques and their applications in data mining, inference, and prediction.
Refer to this title for additional depth on causal inference, a topic that is increasingly relevant in data science and machine learning.
Save
Use this book for supplemental reading that offers an in-depth examination of statistical theory and its applications. It includes a variety of examples and exercises.
Use this book for supplemental reading that provides a more practical perspective on probability and stochastic processes, with applications in various fields.
Save
Consider this title for additional reading on the intersection of probability, statistics, and machine learning.
Consider this title for additional reading that explores the optimization techniques used in machine learning, which complements the course's focus on probability and statistics.
This textbook is written for students in computer science. It provides a good introduction to probability and statistics, with a focus on applications in computer science.
Provides a foundational understanding of reinforcement learning, a topic that complements the course's focus on probability and statistics.
Refer to this title as a complement to the course, as it covers similar topics but offers alternative approaches and perspectives. It also offers additional practice problems.
Save
While not directly related to the course's core topics, this book offers valuable insights into deep learning techniques.
This title provides more advanced coverage of statistical inference for those seeking a deeper understanding of the topic.
Consider this title to provide additional depth and breadth to foundational concepts such as random variables, expectations, and distributions.
This textbook provides a comprehensive introduction to statistical inference. It good choice for students who want a more theoretical and rigorous treatment of the subject.
Provides a comprehensive introduction to natural language processing. It good choice for students who want to learn about natural language processing methods.
Save
Provides a comprehensive introduction to computer vision. It good choice for students who want to learn about computer vision methods.
Provides a comprehensive introduction to speech and language processing. It good choice for students who want to learn about speech and language processing methods.
Save
Provides a comprehensive introduction to data mining. It good choice for students who want to learn about data mining methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nkbj82/probability
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Effort
10 to 12 hours per week for 10 weeks
Level
Introductory
Via
edX
Institution
The University of California, San Diego
Instructors
Alon Orlitsky
Yoav Freund
Session
March 26, 2024
- July 12, 2024
Language
English
Transcript
English
This course belongs to a
collection
called
Data Science. It's comprised of five courses:
- Probability and Statistics in Data Science using Python (viewing)
- Machine Learning Fundamentals
- Big Data Analytics Using Spark
- Python for Data Science, AI & Development
- Python for Data Science
Good to know
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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