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Dmitri Nesteruk
We live in a world of big data, and someone needs to make sense of all this data. In this course, you will learn to efficiently analyze data, formulate hypotheses, and generally reason about what the ocean of data out there is telling you.
Read more
We live in a world of big data, and someone needs to make sense of all this data. In this course, you will learn to efficiently analyze data, formulate hypotheses, and generally reason about what the ocean of data out there is telling you.
Read more
We live in a world of big data, and someone needs to make sense of all this data. In this course, you will learn to efficiently analyze data, formulate hypotheses, and generally reason about what the ocean of data out there is telling you.
We live in a world of big data: huge amounts of data generated by social networks, governments, consumers and markets. Someone needs to make sense of all this data. In this course, Statistics Foundations: Understanding Probability and Distributions, you will learn the fundamental topics essential for understanding probability and statistics. First, you will have an introduction to set theory, a non-rigorous introduction to probability, an overview of key terms and concepts of statistical research. Then, you will discover different statistical distributions, discrete and continuous random variables, probability density functions, and moment generating functions. Finally, you will use key distribution measures such as mean and variance, and explore topics of covariance and correlation. By the end of this course, you’ll be able to look at data and reason about it in terms of its descriptive statistics and possible distributions.
This course is no longer available. Find something similar by browsing:
Probability Theory
Statistical Distributions
Mean and Variance
Moment Generating Functions
Descriptive Statistics
Covariance and Correlation
Register for this course and see more details by visiting:
OpenCourser.com/course/crs66m/statistics
What's inside
Syllabus
Course Overview
Introducing the Concept of Probability
Calculating the Conditional Probability of Events
Understanding Random Variables and Distributions
Read more
Syllabus
Course Overview
Introducing the Concept of Probability
Calculating the Conditional Probability of Events
Understanding Random Variables and Distributions
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches fundamentals of probability and statistics, which are necessary for many fields
Designed for beginners, building a good foundation for understanding statistics and data analysis
Provides an introduction to key concepts in statistics, such as probability, distributions, and statistical measures
May help students develop skills in data analysis and reasoning, which are valuable in various industries
Taught by Dmitri Nesteruk, who has experience in the field of statistics
Covers only the foundational concepts of statistics and probability, and may not be sufficient for students seeking advanced knowledge
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Reviews summary
Foundational statistics for data understanding
According to learners, this course offers a strong foundation in probability and distributions, making complex statistical concepts easy to grasp. Many commend the instructor's clear and concise explanations and the well-structured modules, which are particularly engaging. While it is highly recommended for those starting in data science or seeking to solidify their knowledge, some students note that the pace can be challenging for absolute beginners or those without prior mathematical exposure. A few also wished for more real-world applications or hands-on activities to complement the theoretical understanding.
Could benefit from more real-world examples and hands-on activities.
"My only minor critique would be that some parts could use more real-world applications to make them more tangible."
"I wish there were more hands-on activities or coding examples, but for pure theoretical understanding, it's great."
"I had to look up external resources frequently to fully understand certain concepts."
Provides a solid and well-structured basis in statistics.
"This course was exactly what I needed to build a strong foundation in probability and statistics."
"The material is well-structured, and the lectures are very engaging."
"It sets a good stage for more advanced topics."
Instructor provides highly clear and concise explanations.
"The instructor's explanations were incredibly clear and concise, making complex topics easy to grasp."
"The instructor does a great job breaking down complex ideas."
"It really clarified concepts like random variables and probability distributions that I struggled with before."
Pace can be too fast for absolute beginners without prior math.
"Sometimes I felt the pace was a bit too fast for a complete beginner, especially in the sections on moment generating functions."
"I found this course quite challenging and sometimes confusing... Not for absolute beginners."
"I found some explanations lacked depth, especially for someone with no prior statistical background. It felt a bit rushed in parts."
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 Statistics Foundations: Understanding Probability and Distributions with these
activities:
Review algebra
Show steps
Brush up on the basics of algebra, which is important for understanding statistical equations and formulas.
Browse courses on
Algebra
Show steps
-
Go over your algebra notes from previous courses.
-
Re-familiarize yourself with solving linear equations.
-
Practice simplifying algebraic expressions.
Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang
Show steps
Read this foundational textbook to supplement your understanding of probability theory.
View
Introduction to Probability, Second Edition
on Amazon
Show steps
-
Read through each chapter thoroughly.
-
Take notes on key concepts and formulas.
-
Work through the practice problems at the end of each chapter.
Probability problem sets
Show steps
Complete problem sets to reinforce your understanding of probability concepts.
Browse courses on
Probability
Show steps
-
Find practice problems online or in textbooks.
-
Work through the problems step-by-step.
-
Check your answers against the provided solutions.
-
Revisit problems you struggled with until you fully understand them.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Probability cheat sheet
Show steps
Create a cheat sheet summarizing key probability formulas and concepts.
Browse courses on
Probability
Show steps
-
Compile a list of important probability formulas and concepts.
-
Organize the information in a clear and concise manner.
-
Use colors, diagrams, or other visuals to make the cheat sheet visually appealing and easy to understand.
Distribution visualization tutorials
Show steps
Watch tutorials that demonstrate how to visualize different types of statistical distributions.
Browse courses on
Statistical Distributions
Show steps
-
Search for tutorials on distribution visualization.
-
Choose tutorials that cover the distributions you are learning in class.
-
Follow the instructions in the tutorials to create visualizations.
Study group discussions
Show steps
Join a study group to discuss course material with peers.
Browse courses on
Probability
Show steps
-
Find or form a study group with other students in the course.
-
Meet regularly to discuss the course material.
-
Work together to solve problems and clarify concepts.
Data analysis project
Show steps
Conduct a data analysis project to apply the statistical concepts you have learned.
Browse courses on
Data Analysis
Show steps
-
Identify a dataset that is relevant to your interests.
-
Clean and explore the data.
-
Perform statistical analysis on the data.
-
Interpret the results and draw conclusions.
-
Present your findings in a report or presentation.
Statistical modeling project
Show steps
Develop a statistical model to solve a real-world problem.
Browse courses on
Statistical Modeling
Show steps
-
Identify a problem that can be solved using statistical modeling.
-
Gather data and preprocess it.
-
Choose and train a statistical model.
-
Evaluate the performance of the model.
-
Deploy the model and monitor its performance.
Review algebra
Show steps
Brush up on the basics of algebra, which is important for understanding statistical equations and formulas.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Go over your algebra notes from previous courses.
- Re-familiarize yourself with solving linear equations.
- Practice simplifying algebraic expressions.
Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang
Show steps
Read this foundational textbook to supplement your understanding of probability theory.
View
Introduction to Probability, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read through each chapter thoroughly.
- Take notes on key concepts and formulas.
- Work through the practice problems at the end of each chapter.
Probability problem sets
Show steps
Complete problem sets to reinforce your understanding of probability concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Work through the problems step-by-step.
- Check your answers against the provided solutions.
- Revisit problems you struggled with until you fully understand them.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Probability cheat sheet
Show steps
Create a cheat sheet summarizing key probability formulas and concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Compile a list of important probability formulas and concepts.
- Organize the information in a clear and concise manner.
- Use colors, diagrams, or other visuals to make the cheat sheet visually appealing and easy to understand.
Distribution visualization tutorials
Show steps
Watch tutorials that demonstrate how to visualize different types of statistical distributions.
Browse courses on
Statistical Distributions
Show steps
Show steps
Show steps
- Search for tutorials on distribution visualization.
- Choose tutorials that cover the distributions you are learning in class.
- Follow the instructions in the tutorials to create visualizations.
Study group discussions
Show steps
Join a study group to discuss course material with peers.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Find or form a study group with other students in the course.
- Meet regularly to discuss the course material.
- Work together to solve problems and clarify concepts.
Data analysis project
Show steps
Conduct a data analysis project to apply the statistical concepts you have learned.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a dataset that is relevant to your interests.
- Clean and explore the data.
- Perform statistical analysis on the data.
- Interpret the results and draw conclusions.
- Present your findings in a report or presentation.
Statistical modeling project
Show steps
Develop a statistical model to solve a real-world problem.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Identify a problem that can be solved using statistical modeling.
- Gather data and preprocess it.
- Choose and train a statistical model.
- Evaluate the performance of the model.
- Deploy the model and monitor its performance.
Career center
Learners who complete Statistics Foundations: Understanding Probability and Distributions will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course provides a solid foundation in probability and statistics, which are essential skills for Data Analysts. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing data. By taking this course, you will gain the skills and knowledge necessary to succeed in a career as a Data Analyst.
Statistician
Statistician
A Statistician is responsible for designing and conducting statistical studies to collect and analyze data. This course provides a strong foundation in probability and statistics, which are essential for Statisticians. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing data. By taking this course, you will gain the skills and knowledge necessary to succeed in a career as a Statistician.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is responsible for developing and using mathematical and statistical models to analyze financial data. This course provides a solid foundation in probability and statistics, which are essential for Quantitative Analysts. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing financial data. By taking this course, you will gain the skills and knowledge necessary to succeed in a career as a Quantitative Analyst.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and deploying machine learning models to solve real-world problems. This course provides a strong foundation in probability and statistics, which are essential for Machine Learning Engineers. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and developing machine learning models. By taking this course, you will gain the skills and knowledge necessary to succeed in a career as a Machine Learning Engineer.
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course provides a solid foundation in probability and statistics, which are essential for Data Scientists. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing data. By taking this course, you will gain the skills and knowledge necessary to succeed in a career as a Data Scientist.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. While a background in probability and statistics is not always required for Software Engineers, it can be helpful for those who are interested in developing data-driven applications. This course provides a solid foundation in probability and statistics, which can help Software Engineers to better understand and analyze data, and to develop more effective software applications.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business data to identify opportunities for improvement. This course provides a solid foundation in probability and statistics, which can help Business Analysts to better understand and analyze data, and to make more informed recommendations. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing business data.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to make investment recommendations. This course provides a solid foundation in probability and statistics, which can help Financial Analysts to better understand and analyze financial data, and to make more informed investment recommendations. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing financial data.
Market Researcher
Market Researcher
A Market Researcher is responsible for conducting market research to identify customer needs and preferences. This course provides a solid foundation in probability and statistics, which can help Market Researchers to better understand and analyze data, and to make more informed recommendations. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing market research data.
Actuary
Actuary
An Actuary is responsible for assessing and managing risk. This course provides a solid foundation in probability and statistics, which are essential for Actuaries. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and managing risk.
Data Journalist
Data Journalist
A Data Journalist is responsible for collecting, cleaning, and analyzing data to create data-driven journalism. This course provides a solid foundation in probability and statistics, which can help Data Journalists to better understand and analyze data, and to create more informative and engaging data-driven journalism. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing data.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for using mathematical and statistical models to solve business problems. This course provides a solid foundation in probability and statistics, which can help Operations Research Analysts to better understand and analyze data, and to develop more effective solutions to business problems. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and analyzing data.
Risk Manager
Risk Manager
A Risk Manager is responsible for identifying, assessing, and managing risk. This course provides a solid foundation in probability and statistics, which can help Risk Managers to better understand and analyze risk. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and managing risk.
Underwriter
Underwriter
An Underwriter is responsible for assessing and managing risk. This course provides a solid foundation in probability and statistics, which can help Underwriters to better understand and analyze risk. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding and managing risk.
Teacher
Teacher
A Teacher is responsible for educating students. While a background in probability and statistics is not always required for Teachers, it can be helpful for those who are interested in teaching math or science. This course provides a solid foundation in probability and statistics, which can help Teachers to better understand and teach these subjects. The course covers topics such as probability distributions, random variables, and statistical inference, which are all important for understanding probability and statistics.
For more career information including salaries, visit:
OpenCourser.com/course/crs66m/statistics
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
Statistics Foundations: Understanding Probability and Distributions.
This well-known and used textbook is suitable for the course. The 9th edition's chapters align well with the course's syllabus and objectives. It is readable and appropriate for undergraduate students and general readers.
This readable and comprehensive textbook presents a balanced overview of statistical methods and applications. It provides substantial theoretical content, as well as computational and applied elements.
This classic textbook offers a thorough grounding in probability theory, with a focus on applications. It provides clear explanations and numerous examples, making it a valuable resource for the course.
This comprehensive and mathematically rigorous textbook provides a deep understanding of probability theory. It is suitable for advanced undergraduates and graduate students with a strong mathematical background.
This concise and well-written textbook focuses on the application of probability and statistics in engineering. It provides clear explanations and numerous examples, making it suitable for undergraduate students and professionals alike.
This influential textbook provides a comprehensive overview of Bayesian data analysis methods. It is suitable for advanced undergraduates and graduate students with a strong statistical background.
This advanced textbook provides a rigorous and comprehensive treatment of probability and random processes. It is suitable for graduate students with a strong mathematical background.
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This concise and accessible textbook provides a comprehensive overview of statistical inference. It is suitable for advanced undergraduates and graduate students who are comfortable with mathematical concepts.
This practical textbook provides a hands-on introduction to Bayesian statistics. It uses R and Stan to illustrate the concepts and methods discussed in the text.
This classic textbook provides a rigorous and comprehensive introduction to mathematical statistics. It is suitable for advanced undergraduates and graduate students with a strong mathematical background.
This influential textbook provides a comprehensive overview of statistical learning methods. It is suitable for advanced undergraduates and graduate students with a strong background in statistics and machine learning.
This practical textbook provides a comprehensive overview of statistics and probability for engineers. It is suitable for undergraduate students and practitioners who need to apply statistical methods in their work.
This modern textbook provides a comprehensive overview of probability and statistics for computer science students. It is suitable for undergraduate students and practitioners who need to apply statistical methods in their work.
This accessible and comprehensive textbook provides an introduction to probability and mathematical statistics. It is suitable for undergraduate students who are not familiar with the subject.
This introductory textbook provides a clear and concise overview of probability and statistics. It is suitable for undergraduate students who are not familiar with the subject.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/crs66m/statistics
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