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Statistics for Data Science Essentials

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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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

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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

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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

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Exchange ideas and learn from peers through discussions on statistical estimation.

Browse courses on Statistical Estimation

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Participate in discussions on statistical estimation techniques
Share and critique approaches to reducing bias and variance

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Probability problem sets

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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

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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

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Solidify your understanding of probability theorems by creating a visually engaging infographic.

Browse courses on Law of Large Numbers

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Research different probability theorems
Design and create an infographic summarizing the theorems

Case Study: Interpreting Data

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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

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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

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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:

Reading list

We've selected 42 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 for Data Science Essentials.

Concrete Mathematics

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This influential textbook presents a rigorous and accessible introduction to discrete mathematics, with a focus on applications in computer science. It covers topics such as counting, probability, and graph theory, and it is suitable for advanced undergraduate and graduate students.

Concrete Mathematics: A Foundation for Computer...

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$$$

Mathematics for Computer Science

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This textbook provides a rigorous and comprehensive introduction to mathematics for computer science students. It covers topics such as logic, set theory, counting, and graph theory, with a focus on applications in computer science.

Statistics for Data Science Essentials

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What's inside

Syllabus

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Reviews summary

Foundational statistics for data science

Activities

Career center

Reading list

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