Introduction to Statistics from Coursera

What's inside

Syllabus

Introduction and Descriptive Statistics for Exploring Data

This module provides an overview of the course and a review of the main tools used in descriptive statistics to visualize information.

Producing Data and Sampling

In this module, you will look at the main concepts for sampling and designing experiments. You will learn about curious pitfalls and how to evaluate the effectiveness of such experiments.

Probability

In this module, you will learn about the definition of probability and the essential rules of probability that you will need for solving both simple and complex challenges. You will also learn about examples of how simple rules of probability are used to create solutions for real-life complex situations.

Normal Approximation and Binomial Distribution

This module covers the empirical rule and normal approximation for data, a technique that is used in many statistical procedures. You will also learn about the binomial distribution and the basics of random variables.

Sampling Distributions and the Central Limit Theorem

In this module, you will learn about the Law of Large Numbers and the Central Limit Theorem. You will also learn how to differentiate between the different types of histograms present in statistical analysis.

Regression

This module covers regression, arguably the most important statistical technique based on its versatility to solve different types of statistical problems. You will learn about inference, regression, and how to do regression diagnostics.

Confidence Intervals

In this module, you will learn how to construct and interpret confidence intervals in standard situations.

Tests of Significance

In this module, you will look at the logic behind testing and learn how to perform the appropriate statistical tests for different samples and situations. You will also learn about common misunderstandings and pitfalls in testing.

Resampling

This module focuses on the two main methods used in computer-intensive statistical inference: The Monte Carlo method, and the Bootstrap method. You will learn about the theoretic principles behind these methods and how they are applied in different contexts, such as regression and constructing confidence intervals.

Analysis of Categorical Data

This module focuses on the three important statistical analysis for categorical data: Chi-Square Goodness of Fit test, Chi-Square test of Homogeneity, and Chi-Square test of Independence.

One-Way Analysis of Variance (ANOVA)

This module covers the basics of ANOVA and how F-tests work on one-way ANOVA examples.

Multiple Comparisons

In this module, you will learn about very important issues that have surfaced in the era of big data: data snooping and the multiple testing fallacy. You will also explore the reasons behind challenges in data reproducibility and applicability, and how to prevent such issues in your own work.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

If this course could be used for your program, it would complement your existing coursework well

Strong reputation of Stanford in the field addressed by this course

Content is multi-modal with a mix of videos, readings, and images

Covers key principles of statistics critical to data analysis and insight generation

Features hands-on labs and interactive materials

Uses a creative approach to introduce foundational concepts

Reviews summary

Introductory statistics

learners say this course is a largely positive beginner-friendly overview of important introductory statistical concepts. It is well explained and well organized, with plenty of real-world examples. Students say it is especially useful for those who want to enter into data science or machine learning fields. The notes below include key features and flaws, as identified by learners.

The quizzes are well-designed and provide a good way to check your understanding of the material.

"The course structure and materials were well-organized, allowing for a smooth progression of learning."

"The interactive quizzes, assignments, and real-world examples provided a practical understanding of statistical concepts."

The course is beginner-friendly and assumes no prior knowledge of statistics.

"I took the course to brush up my knowledge."

"Having worked in Data Science for quite some time now I have definitely forgotten some concepts which I did not use for a long time."

"This reason this quick course was great refresher."

"I would also suggest this course to anyone starting in Data Science field"

The course is especially useful for those who want to enter into data science or machine learning fields.

"The course has been truly beneficial, providing me with a clear understanding of fundamental concepts."

"The content is well-crafted and effectively refreshes our knowledge."

"I think sometimes the professor can provide some details about the quiz and more questions in this quiz in order to help us have more deeper understanding of the stats."

"I'm from developing countries particularly from Yemen where the war have destroyed everything."

"I hope that you will give me the certificate for this course for granted."

The course provides plenty of real-world examples to illustrate the concepts.

"The course also offered opportunities for hands-on practice, which solidified my understanding of the material."

"One aspect that stood out for me was the strong emphasis on application."

"The course not only focused on theory but also provided practical scenarios where statistics plays a crucial role."

The course is well explained and provides clear explanations of complex concepts.

"Professor Guenther Walther, with his vast knowledge and expertise in statistics, delivered the course content in an engaging and comprehensive manner."

"His ability to explain complex concepts in a clear and understandable way truly made a difference in my learning journey."

"The course is well explained and provides clear explanations of complex concepts."

The material is boring and difficult to engage with.

"It was an amazing course, but as a person who had never had strong background experience in algebra and statistics, I struggled a bit in understanding some parts."

"I also wish that the given examples were animated or illustrated in some way since we are visual beings and it makes things clearer to understand."

The course assumes some prior knowledge of statistics.

"This course is not an introductory course! It is more like a summary of statistics from beginner to advanced."

"The course instructor rushes through important concepts with minimum explanation and there are no exercises or practice questions."

"I do not recommend this course for those who want to learn statistics from the beginning."

The pace of the course is too fast for some learners.

"The course is really short and in my opinion, sometimes lack of explanations and details."

"I really had to re watch a video three or four times from time to time to be sure I understood the concepts even though not difficult I wanted to be sure I understood."

"Besides, he test can be retaken as many times as you desire which can sometimes create the incentive to answer randomly."

The course lacks examples to illustrate the concepts.

"This is more fitting to someone who has a background in mathematics and statistics and wants to refresh their knowledge (and potentially learn some additional topics in Statistica Analysis)."

"If you have never done any statistics and probability before you will struggle or lose the motivation to complete the course."

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 Introduction to Statistics with these activities:

Review "The Book of Why: The New Science of Cause and Effect"

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This book provides valuable insights into the principles of causality, which is a fundamental concept in statistics.

View The Book of Why: The New Science of Cause and... on Amazon

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Read the book and take notes
Highlight key concepts related to causality

Discuss the course syllabus within the first week

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Peer-based discussions offer a great way to solidify your understanding of the materials covered at the beginning of the course.

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Set up a study group with classmates
Outline the syllabus for each topic
Discuss key points, definitions, and examples
Foster a collaborative environment where you provide guidance to your peers

Complete additional problems on sampling and probability

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Solving additional practice problems will solidify your grasp of this topic and help you better understand their application.

Browse courses on Sampling

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Gather exercises from textbooks
Work through problems on probability and sampling
Check your answers with online resources

Four other activities

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Attend a workshop on statistical software

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Gain practical experience with statistical software tools used in the course to enhance your understanding.

Browse courses on Statistical Software

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Research available workshops in your area
Select a workshop that aligns with your skill level
Register and attend the workshop

Provide mentoring support to fellow students

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Engage with your peers and solidify your understanding by assisting them in their learning journey.

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Identify struggling students through discussion forums or class interactions
Offer guidance and support by explaining concepts and providing resources
Foster a supportive learning community

Write a short summary of each module after completing it

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By summarizing each module, you will be able to retain the key points more efficiently.

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Review the notes from each module
Identify key concepts and definitions
Craft a concise summary

Participate in a data analysis competition

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Put your skills to test and expand your knowledge through a competitive data analysis environment.

Browse courses on Data Analysis

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Identify relevant competitions
Form a team and collaborate on the project
Apply statistical techniques and algorithms to solve a specific problem

Career center

Learners who complete Introduction to Statistics will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists combine statistical analysis and machine learning techniques to extract meaningful insights from data. Stanford's Introduction to Statistics is an excellent starting point for aspiring Data Scientists, as it introduces fundamental statistical concepts. The course covers topics like probability, sampling distributions, and regression, which form the foundation for more advanced data science methods.

See salaries and explore the career path for Data Scientist

Survey Researcher

Survey Researchers design and conduct surveys to collect data from populations. Stanford's Introduction to Statistics is highly relevant to this field, as it provides a comprehensive introduction to sampling techniques and data analysis. The course will help you develop the skills needed to design effective surveys and analyze the results.

See salaries and explore the career path for Survey Researcher

Data Analyst

Data Analysts collect, clean, and analyze data to provide insights for businesses. Stanford's Introduction to Statistics provides a solid foundation for aspiring Data Analysts, as it teaches them essential statistical thinking concepts and techniques. The course will help you understand how to explore data, perform sampling, and conduct hypothesis testing, all valuable skills for Data Analysts.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use statistical models and techniques to evaluate investments and make financial decisions. Stanford's Introduction to Statistics is highly relevant to this field, as it provides a solid foundation in statistical thinking and analysis. The course will help you develop the skills needed to succeed as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use statistical models and techniques to assess risk and make investment decisions. Stanford's Introduction to Statistics is highly relevant to this field, as it provides a solid foundation in statistical thinking and analysis. The course will help you develop the skills needed to succeed as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market Researchers analyze data to understand consumer behavior and market trends. Stanford's Introduction to Statistics provides a strong foundation for this field, as it teaches essential statistical techniques for data analysis and interpretation. By completing this course, you will gain a competitive edge in understanding and communicating market research findings.

See salaries and explore the career path for Market Researcher

Epidemiologist

Epidemiologists investigate the causes and patterns of health and disease in populations. Stanford's Introduction to Statistics provides a solid foundation for this field, as it teaches essential statistical concepts and methods for analyzing health data. By completing this course, you will gain a competitive edge in understanding and interpreting epidemiological studies.

See salaries and explore the career path for Epidemiologist

Economist

Economists analyze economic data to understand economic trends and make policy recommendations. Stanford's Introduction to Statistics provides a strong foundation for this field, as it teaches essential statistical techniques for data analysis and interpretation. By completing this course, you will gain a competitive edge in understanding and communicating economic data.

See salaries and explore the career path for Economist

Biostatistician

Biostatisticians apply statistical methods to analyze biological and medical data. Stanford's Introduction to Statistics provides a solid foundation for this field, as it teaches essential statistical concepts and techniques. By completing this course, you will gain a competitive edge in understanding and interpreting biostatistical data.

See salaries and explore the career path for Biostatistician

Statistician

Statisticians apply mathematical and statistical techniques to collect, analyze, interpret, and present data. Introduction to Statistics from Stanford University may be useful because it offers a comprehensive introduction to statistical concepts essential for learning from data and communicating insights. By mastering these concepts, you will gain a solid foundation to pursue advanced statistical methods and techniques used by Statisticians.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts use data and analysis to solve business problems and improve decision-making. Stanford's Introduction to Statistics provides a valuable foundation in statistical methods and techniques for aspiring Business Analysts. The course will help you develop the skills needed to analyze data, identify trends, and make data-driven recommendations.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use statistical techniques to optimize business processes and operations. Stanford's Introduction to Statistics may be useful for this role, as it provides a foundation in statistical thinking and analysis. The course will help you develop the skills needed to understand and apply statistical methods in business operations.

See salaries and explore the career path for Operations Research Analyst

Insurance Analyst

Insurance Analysts use statistical techniques to assess risk and determine insurance premiums. Stanford's Introduction to Statistics may be useful for this role, as it provides a foundation in statistical thinking and analysis. The course will help you develop the skills needed to understand and apply statistical methods in the insurance industry.

See salaries and explore the career path for Insurance Analyst

Risk Analyst

Risk Analysts evaluate and quantify risks faced by businesses and organizations. Stanford's Introduction to Statistics may be useful for this role, as it offers a comprehensive introduction to statistical concepts and techniques. By mastering these concepts, you will gain a solid foundation to pursue more specialized risk analysis methods.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers design and build systems for storing, managing, and analyzing data. Stanford's Introduction to Statistics may be useful for this role, as it provides a foundation in statistical thinking and data analysis. The course will help you develop the skills needed to understand and apply statistical methods in data engineering.

See salaries and explore the career path for Data Engineer

Introduction to Statistics

What's inside

Syllabus

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Reviews summary

Introductory statistics

Activities

Career center

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