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Anne Dougherty
and
Jem Corcoran
Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.
Read more
Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.
Read more
Understand the foundations of probability and its relationship to statistics and data science. We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We’ll study discrete and continuous random variables and see how this fits with data collection. We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder
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What's inside
Syllabus
Start Here!
Welcome to the course! This module contains logistical information to get you started!
Descriptive Statistics and the Axioms of Probability
Read more
Syllabus
Start Here!
Welcome to the course! This module contains logistical information to get you started!
Descriptive Statistics and the Axioms of Probability
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches probability, which is a foundational concept for data science and statistics
Develops mathematical foundations that support the study of statistics and data science
Provides a gentle introduction to a complex field through approachable activities and lessons
Examines discrete and continuous random variables, which are crucial concepts for data analysis
Explores the Central Limit Theorem, which is a fundamental result used in data analysis and modeling
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Reviews summary
Solid probability foundation for data science
According to students, this course provides a strong and rigorous foundation in probability theory, essential for those pursuing data science. Many learners highlight the clarity of the lectures and the instructor's ability to explain complex concepts intuitively. The quizzes and assignments are frequently mentioned as effective tools for reinforcing understanding. While largely positive, some learners note that the course is heavily theoretical and might require a solid prior math background, particularly in calculus. The comprehensive coverage of topics, including the Central Limit Theorem, prepares students well for subsequent data science studies.
In-depth coverage of the Central Limit Theorem.
"The Central Limit Theorem module was particularly insightful and well-explained."
"The comprehensive coverage of topics, including the Central Limit Theorem, prepares students well."
"I really appreciated the detailed look into the CLT and its importance for data science."
Quizzes and homework effectively solidify understanding.
"Quizzes and homework solidified my understanding."
"The problem sets were challenging but fair, really helping concepts click."
"I found the assignments very helpful in applying what was learned in lectures."
Instructor effectively breaks down complex probability concepts.
"The lectures were clear and the quizzes reinforced concepts effectively."
"The explanations were intuitive and the problem sets challenging but fair."
"The professor explains complex concepts clearly, making them easy to grasp."
Provides a deep, rigorous base in probability for data science.
"This course provided an excellent, rigorous foundation in probability needed for advanced data science topics."
"Overall, a solid introduction to probability theory. I found the content comprehensive..."
"Truly outstanding! As someone transitioning into data science, this course demystified probability."
Assumes a background in calculus, challenging for some.
"I struggled with the pace. It felt like it assumed a stronger math background than advertised."
"Very theoretical. While the content is probably important, I felt it lacked practical application."
"I had to spend a lot of time reviewing calculus concepts to keep up."
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 Theory: Foundation for Data Science with these
activities:
Review probability theory
Show steps
Brings existing probability theory knowledge up to date and will help reinforce concepts.
View
Introduction to Probability Models
on Amazon
Show steps
-
Review the axioms of probability
-
Solve practice problems on probability
-
Summarize key concepts in probability theory
Solve practice problems on probability distributions
Show steps
Helps reinforce understanding of probability distributions by providing targeted practice on a variety of problem types.
Browse courses on
Probability Distributions
Show steps
-
Identify the type of probability distribution
-
Calculate probabilities using the probability mass function or probability density function
-
Solve word problems involving probability distributions
Create a data analysis project on real-world data
Show steps
Provides a practical application of probability and statistics concepts to real-world data.
Browse courses on
Data Analysis
Show steps
-
Identify a real-world dataset
-
Clean and prepare the data
-
Apply probability and statistics techniques to analyze the data
-
Create visualizations to present the results
-
Write a report summarizing the findings
Five other activities
Expand to see all activities and additional details
Show all eight activities
Write a blog post on a probability or statistics topic
Show steps
Requires students to synthesize their understanding of probability and statistics concepts and communicate them to others.
Browse courses on
Probability
Show steps
-
Choose a probability or statistics topic
-
Research the topic
-
Write a blog post explaining the topic in a clear and concise way
-
Proofread and edit the blog post
-
Publish the blog post
Follow tutorials on Bayesian statistics
Show steps
Provides a deeper dive into Bayesian statistics, a powerful technique used in probability and data analysis.
Browse courses on
Bayesian Statistics
Show steps
-
Find tutorials on Bayesian statistics
-
Follow the tutorials
-
Implement Bayesian statistical methods in practice
Attend a workshop on data visualization
Show steps
Provides hands-on experience and exposure to best practices in data visualization, a key skill for data analysis.
Browse courses on
Data Visualization
Show steps
-
Find a workshop on data visualization
-
Attend the workshop
Volunteer with a data science or statistics organization
Show steps
Offers practical experience in applying probability and statistics concepts in a real-world setting.
Browse courses on
Data Science
Show steps
-
Find a data science or statistics organization to volunteer with
-
Apply to volunteer
-
Participate in volunteer activities
Participate in a data science or statistics competition
Show steps
Provides a challenging and motivating way to apply probability and statistics skills in a competitive setting.
Browse courses on
Data Science
Show steps
-
Find a data science or statistics competition
-
Register for the competition
-
Prepare for the competition
-
Participate in the competition
Review probability theory
Show steps
Brings existing probability theory knowledge up to date and will help reinforce concepts.
View
Introduction to Probability Models
on Amazon
Show steps
Show steps
Show steps
- Review the axioms of probability
- Solve practice problems on probability
- Summarize key concepts in probability theory
Solve practice problems on probability distributions
Show steps
Helps reinforce understanding of probability distributions by providing targeted practice on a variety of problem types.
Browse courses on
Probability Distributions
Show steps
Show steps
Show steps
- Identify the type of probability distribution
- Calculate probabilities using the probability mass function or probability density function
- Solve word problems involving probability distributions
Create a data analysis project on real-world data
Show steps
Provides a practical application of probability and statistics concepts to real-world data.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a real-world dataset
- Clean and prepare the data
- Apply probability and statistics techniques to analyze the data
- Create visualizations to present the results
- Write a report summarizing the findings
Five other activities
Expand to see all activities and additional details
Show all eight activities
Write a blog post on a probability or statistics topic
Show steps
Requires students to synthesize their understanding of probability and statistics concepts and communicate them to others.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Choose a probability or statistics topic
- Research the topic
- Write a blog post explaining the topic in a clear and concise way
- Proofread and edit the blog post
- Publish the blog post
Follow tutorials on Bayesian statistics
Show steps
Provides a deeper dive into Bayesian statistics, a powerful technique used in probability and data analysis.
Browse courses on
Bayesian Statistics
Show steps
Show steps
Show steps
- Find tutorials on Bayesian statistics
- Follow the tutorials
- Implement Bayesian statistical methods in practice
Attend a workshop on data visualization
Show steps
Provides hands-on experience and exposure to best practices in data visualization, a key skill for data analysis.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a workshop on data visualization
- Attend the workshop
Volunteer with a data science or statistics organization
Show steps
Offers practical experience in applying probability and statistics concepts in a real-world setting.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science or statistics organization to volunteer with
- Apply to volunteer
- Participate in volunteer activities
Participate in a data science or statistics competition
Show steps
Provides a challenging and motivating way to apply probability and statistics skills in a competitive setting.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science or statistics competition
- Register for the competition
- Prepare for the competition
- Participate in the competition
Career center
Learners who complete Probability Theory: Foundation for Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Probability Theory: Foundation for Data Science course can prove useful to those aspiring to be Data Scientists. It covers the basics of probability and its relevance in data science. This course can be particularly helpful in making sense of data and drawing meaningful conclusions from it. Additionally, it provides a solid foundation for more advanced topics such as machine learning and artificial intelligence.
Statistical Analyst
Statistical Analyst
As the title suggests, Probability Theory: Foundation for Data Science can be a valuable asset for aspiring Statistical Analysts. It delves into the fundamentals of probability and its applications in data analysis, which provides a good grounding for the analysis and interpretation of data.
Actuary
Actuary
An understanding of probability is essential for Actuaries. The Probability Theory: Foundation for Data Science course covers the concepts and techniques involved in calculating probabilities, which is fundamental to actuarial work in assessing risks and making financial projections.
Statistician
Statistician
The Probability Theory: Foundation for Data Science course alligns well with the field of Statistics. It covers the mathematical and statistical principles of probability and their applications in data analysis and modeling, providing a strong foundation for aspiring Statisticians.
Applied Statistician
Applied Statistician
Probability Theory: Foundation for Data Science is highly relevant to Applied Statisticians. It covers the concepts and techniques of probability and their applications in real-world data analysis and modeling. This course can provide Applied Statisticians with a strong foundation for their work.
Risk Analyst
Risk Analyst
The Probability Theory: Foundation for Data Science course aligns well with the field of Risk Analysis. It covers the principles of probability and its use in assessing and managing risks. This course can provide aspiring Risk Analysts with a strong theoretical foundation for their work.
Machine Learning Engineer
Machine Learning Engineer
A Probability Theory: Foundation for Data Science course can be a valuable asset for Machine Learning Engineers. It covers the mathematical foundations of probability and statistics, which are essential for understanding and developing machine learning algorithms.
Quantitative Analyst
Quantitative Analyst
A Probability Theory: Foundation for Data Science course is highly relevant to those aspiring to be Quantitative Analysts. It covers the mathematical and statistical concepts used in financial modeling and risk assessment, which are essential skills for Quantitative Analysts.
Data Analyst
Data Analyst
The Probability Theory: Foundation for Data Science course offers a solid foundation for aspiring Data Analysts. It covers the principles of probability and its use in data analysis and interpretation, which are essential skills for Data Analysts in extracting insights from data.
Business Analyst
Business Analyst
Business Analysts often rely on data to make informed decisions. Having a strong foundation in probability can enable them to better understand and analyze data. The Probability Theory: Foundation for Data Science course can provide that foundation, equipping Business Analysts with the skills to extract meaningful insights from data.
Operations Research Analyst
Operations Research Analyst
For those interested in becoming Operations Research Analysts, a Probability Theory: Foundation for Data Science course can be beneficial. It provides a solid understanding of probability and its applications in modeling and optimizing systems, which is crucial for Operations Research Analysts in analyzing and improving complex processes.
Data Engineer
Data Engineer
Probability Theory: Foundation for Data Science offers relevant knowledge for Data Engineers. It provides a comprehensive overview of probability and its applications in data analysis and management, which are essential skills for Data Engineers in designing and implementing data pipelines.
Economist
Economist
Economists often use probability and statistics to analyze economic data and make predictions. The Probability Theory: Foundation for Data Science course can provide a strong foundation in probability and its applications in economics, which can be beneficial for aspiring Economists.
Financial Analyst
Financial Analyst
Probability Theory: Foundation for Data Science can be a helpful resource for aspiring Financial Analysts. It covers the principles of probability and its use in financial modeling and analysis, which can provide a strong foundation for understanding financial markets and making investment decisions.
Software Engineer
Software Engineer
For Software Engineers pursuing roles in data-intensive fields, Probability Theory: Foundation for Data Science can be helpful. It provides a solid understanding of probability and its applications in data analysis and modeling, which can be beneficial in designing and developing software solutions for data-driven applications.
For more career information including salaries, visit:
OpenCourser.com/course/zdy2qs/probability
Reading list
We've selected 14 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 Theory: Foundation for Data Science.
Provides a comprehensive introduction to causal inference in statistics, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to econometric analysis of cross section and panel data, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to probability theory, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Save
Provides a comprehensive introduction to deep learning, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to statistics, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to statistical learning, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to machine learning, with a focus on probabilistic models. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to reinforcement learning, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to statistical inference, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to Bayesian data analysis, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to probability theory, with a focus on applications in statistics. It is written in a clear and rigorous style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to probability theory and random processes, with a focus on applications in engineering and the natural sciences. It is written in a clear and rigorous style, making it ideal for use as a textbook or for self-study.
Provides a concise introduction to probability theory and its applications. It is written in a clear and accessible style, making it ideal for use as a textbook or for self-study.
Provides a comprehensive introduction to mathematical statistics, with a focus on applications in data science and machine learning. It is written in a clear and engaging style, making it ideal for use as a textbook or for self-study.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zdy2qs/probability
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Similar courses
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Rating
4.5
Effort
Approx. 40 hours
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructors
Anne Dougherty
Jem Corcoran
Language
English
This course belongs to a
collection
called
Data Science Foundations: Statistical Inference. It's comprised of three courses:
- Probability Theory: Foundation for Data Science (viewing)
- Statistical Inference for Estimation in Data Science
- Statistical Inference and Hypothesis Testing in Data Science Applications
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