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Chris Callison-Burch
and
Hamed Hassani
Review the basics of discrete math and probability before enhancing your probability skills and learning how to interpret data with tools such as the central limit theorem, confidence intervals and more. Complete short weekly mathematical assignments.
Register for this course and see more details by visiting:
OpenCourser.com/course/42jh7k/statistics
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What's inside
Syllabus
Week 1: Getting Started with Statistics for Data Science
In the first week of the course, we’ll introduce you to a broad definition of data science and go over some of its main building blocks. To prepare, we'll spend some time reviewing discrete math fundamentals. By the end of the week, we will solve our first data science task using random sampling.
Read more
Syllabus
Week 1: Getting Started with Statistics for Data Science
In the first week of the course, we’ll introduce you to a broad definition of data science and go over some of its main building blocks. To prepare, we'll spend some time reviewing discrete math fundamentals. By the end of the week, we will solve our first data science task using random sampling.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides foundational knowledge of data science, making it suitable for learners who are new to the field
Enhances probability skills, addressing a critical aspect of data interpretation
Covers essential statistical concepts, including confidence intervals and hypothesis testing
Builds a solid foundation for learners interested in data analysis and machine learning
Instructors have extensive experience in the field, providing credibility and subject matter expertise
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Reviews summary
Foundational statistics for data science
According to students, "Statistics for Data Science Essentials" offers a strong foundational understanding of key statistical concepts. Learners praise the instructor's clear explanations, which effectively break down complex topics like the Central Limit Theorem and confidence intervals. The course is particularly well-paced for beginners, building concepts logically. However, a common sentiment is that the course maintains a highly theoretical focus, with some students wishing for more hands-on coding or practical applications. While the weekly assignments are useful for practice, some with prior knowledge found them too basic, suggesting a need for more challenging problems for varied audiences.
Provides essential statistical groundwork for data science.
"This course provided an excellent foundational understanding of statistics for data science."
"A solid introduction to the statistical concepts essential for anyone getting into data science."
"It set a strong theoretical groundwork."
"I took it as a refresher and found it incredibly valuable."
Instructor excels at simplifying complex statistical concepts.
"The instructor's explanations were incredibly clear, breaking down complex topics into digestible parts."
"The lectures were well-structured and easy to follow."
"Absolutely brilliant! this course made probability and statistical estimation so much clearer."
"The explanation of the Central Limit Theorem was the best I've encountered."
Assignments may be too basic for those with prior knowledge.
"The weekly assignments were good for practice but sometimes felt a bit too simple, I wished for more challenging problems."
"For someone with a minor in statistics, much of it felt like review. The assignments were too basic and didn't offer much challenge."
"The assignments were very theoretical, not much coding."
Emphasizes mathematical theory over practical coding.
"However, it's very theoretical. I was hoping for more practical examples using Python/R, but it's mainly math."
"My main feedback is that it felt a bit academic... The hands-on application seemed limited to simple math problems."
"I needed more examples and clearer connections to real data science problems. The assignments didn't feel practical enough."
"Don't expect coding tutorials, as it's focused on mathematical principles."
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 for Data Science Essentials with these
activities:
Review Discrete Math Fundamentals
Show steps
Strengthen your foundation in discrete math to enhance your understanding of probability.
Browse courses on
Discrete Mathematics
Show steps
-
Review the concepts of set theory
-
Practice solving problems involving logic and proof techniques
Review combinatorics basics
Show steps
Refresh your understanding of combinatorics to enhance your probability skills.
Browse courses on
Combinatorics
Show steps
-
Review the principles of counting
-
Solve practice problems on permutations and combinations
Discussion Forum on Statistical Estimation
Show steps
Exchange ideas and learn from peers through discussions on statistical estimation.
Browse courses on
Statistical Estimation
Show steps
-
Participate in discussions on statistical estimation techniques
-
Share and critique approaches to reducing bias and variance
Five other activities
Expand to see all activities and additional details
Show all eight activities
Probability problem sets
Show steps
Reinforce your understanding of probability concepts through regular problem-solving exercises.
Browse courses on
Probability
Show steps
-
Solve problems involving probability distributions
-
Apply Bayes Theorem to solve conditional probability problems
Tutorial on Confidence Intervals
Show steps
Deepen your understanding of confidence intervals by exploring tutorials and examples.
Browse courses on
Confidence Intervals
Show steps
-
Watch video tutorials on confidence intervals
-
Work through practice problems on calculating confidence intervals
Infographic on Probability Theorems
Show steps
Solidify your understanding of probability theorems by creating a visually engaging infographic.
Browse courses on
Law of Large Numbers
Show steps
-
Research different probability theorems
-
Design and create an infographic summarizing the theorems
Case Study: Interpreting Data
Show steps
Develop your ability to interpret data by working through a real-world case study that applies statistical estimation and confidence intervals.
Browse courses on
Statistical Estimation
Show steps
-
Analyze a dataset
-
Apply the Central Limit Theorem
-
Construct confidence intervals
-
Draw conclusions from the data
Attend a Data Science Meetup
Show steps
Expand your knowledge and connect with other data science professionals by attending industry meetups.
Show steps
-
Find a local data science meetup
-
Attend the event and engage with other attendees
Review Discrete Math Fundamentals
Show steps
Strengthen your foundation in discrete math to enhance your understanding of probability.
Browse courses on
Discrete Mathematics
Show steps
Show steps
Show steps
- Review the concepts of set theory
- Practice solving problems involving logic and proof techniques
Review combinatorics basics
Show steps
Refresh your understanding of combinatorics to enhance your probability skills.
Browse courses on
Combinatorics
Show steps
Show steps
Show steps
- Review the principles of counting
- Solve practice problems on permutations and combinations
Discussion Forum on Statistical Estimation
Show steps
Exchange ideas and learn from peers through discussions on statistical estimation.
Browse courses on
Statistical Estimation
Show steps
Show steps
Show steps
- Participate in discussions on statistical estimation techniques
- Share and critique approaches to reducing bias and variance
Five other activities
Expand to see all activities and additional details
Show all eight activities
Probability problem sets
Show steps
Reinforce your understanding of probability concepts through regular problem-solving exercises.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Solve problems involving probability distributions
- Apply Bayes Theorem to solve conditional probability problems
Tutorial on Confidence Intervals
Show steps
Deepen your understanding of confidence intervals by exploring tutorials and examples.
Browse courses on
Confidence Intervals
Show steps
Show steps
Show steps
- Watch video tutorials on confidence intervals
- Work through practice problems on calculating confidence intervals
Infographic on Probability Theorems
Show steps
Solidify your understanding of probability theorems by creating a visually engaging infographic.
Browse courses on
Law of Large Numbers
Show steps
Show steps
Show steps
- Research different probability theorems
- Design and create an infographic summarizing the theorems
Case Study: Interpreting Data
Show steps
Develop your ability to interpret data by working through a real-world case study that applies statistical estimation and confidence intervals.
Browse courses on
Statistical Estimation
Show steps
Show steps
Show steps
- Analyze a dataset
- Apply the Central Limit Theorem
- Construct confidence intervals
- Draw conclusions from the data
Attend a Data Science Meetup
Show steps
Expand your knowledge and connect with other data science professionals by attending industry meetups.
Show steps
Show steps
Show steps
- Find a local data science meetup
- Attend the event and engage with other attendees
Career center
Learners who complete Statistics for Data Science Essentials will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/42jh7k/statistics
Reading list
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For more information about how these books relate to this course, visit:
OpenCourser.com/course/42jh7k/statistics
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Effort
4 weeks of study, 8-12 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Pennsylvania
Instructors
Chris Callison-Burch
Hamed Hassani
Language
English
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