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Statistics for Data Science Essentials

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.

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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.
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Week 2: Probability
The second week of our course is devoted to probability: since probability is the main language used by almost every data science concept, we will commit some time to deepening our understanding of it. By the end of the week, you will have far more tools in your probability toolkit, which will serve you throughout your AI and machine learning journey.
Week 3: Statistical Estimation
In this week, we will build up our general framework of statistical estimation, taking from several of the concepts we have discussed and more that we will continue to add this week. We will start by going over the sample mean, and we will analyze how good this is as an estimator. We will then explore the Central Limit Theorem, one of the most effective and widely-used tools in statistics and data science. We will also continue some probability review.
Week 4: Confidence Intervals & Point Estimation
Now that we have learned the important machinery of the Central Limit Theorem, we are ready to learn about confidence intervals this week. Confidence intervals are the main quantities to characterize error bars in almost any area of data science and machine learning. After going through confidence intervals and some examples, we will also explore a more general perspective on estimation: point estimation.

Good to know

Know what's good
, what to watch for
, 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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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
Strengthen your foundation in discrete math to enhance your understanding of probability.
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  • Review the concepts of set theory
  • Practice solving problems involving logic and proof techniques
Review combinatorics basics
Refresh your understanding of combinatorics to enhance your probability skills.
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  • Review the principles of counting
  • Solve practice problems on permutations and combinations
Discussion Forum on Statistical Estimation
Exchange ideas and learn from peers through discussions on statistical estimation.
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  • Participate in discussions on statistical estimation techniques
  • Share and critique approaches to reducing bias and variance
Five other activities
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Probability problem sets
Reinforce your understanding of probability concepts through regular problem-solving exercises.
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  • Solve problems involving probability distributions
  • Apply Bayes Theorem to solve conditional probability problems
Tutorial on Confidence Intervals
Deepen your understanding of confidence intervals by exploring tutorials and examples.
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  • Watch video tutorials on confidence intervals
  • Work through practice problems on calculating confidence intervals
Infographic on Probability Theorems
Solidify your understanding of probability theorems by creating a visually engaging infographic.
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  • Research different probability theorems
  • Design and create an infographic summarizing the theorems
Case Study: Interpreting Data
Develop your ability to interpret data by working through a real-world case study that applies statistical estimation and confidence intervals.
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  • Analyze a dataset
  • Apply the Central Limit Theorem
  • Construct confidence intervals
  • Draw conclusions from the data
Attend a Data Science Meetup
Expand your knowledge and connect with other data science professionals by attending industry meetups.
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  • 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:

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Course image
Effort
4 weeks of study, 8-12 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Pennsylvania
Instructors
Chris Callison-Burch
Hamed Hassani
Session
August 12, 2024 - September 16, 2024
Language
English
Good to know
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
Share and help others discover this course
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