About this Course

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This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Beginner Level

Approx. 14 hours to complete

English

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Skills you will gain

Statistics
R Programming
Rstudio
Exploratory Data Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Approx. 14 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.83/5 (678 Ratings)

Mine Çetinkaya-Rundel

Associate Professor of the Practice

Department of Statistical Science

359,551 Learners

7 Courses

Syllabus - What you will learn from this course

Content Rating94%(28,291 ratings)

Week1

Week 1

12 minutes to complete

About Introduction to Probability and Data

This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing. Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.

12 minutes to complete

1 video (Total 2 min), 1 reading

Week2

Week 2

2 hours to complete

Introduction to Data

Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on this module's forum (https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1) and discuss with your peers! To get started, view the learning objectives (https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives) of Lesson 1 in this module.

2 hours to complete

6 videos (Total 28 min), 2 readings, 2 quizzes

Week3

Week 3

1 hour to complete

Introduction to Data Project

1 hour to complete

2 readings

Week4

Week 4

2 hours to complete

Exploratory Data Analysis and Introduction to Inference

2 hours to complete

7 videos (Total 46 min), 3 readings, 2 quizzes

Week5

Week 5

1 hour to complete

Exploratory Data Analysis and Introduction to Inference Project

1 hour to complete

2 readings

Week6

Week 6

3 hours to complete

Introduction to Probability

3 hours to complete

9 videos (Total 82 min), 3 readings, 2 quizzes

Week7

Week 7

1 hour to complete

Introduction to Probability Project

1 hour to complete

2 readings

Week8

Week 8

5 hours to complete

Probability Distributions

Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz. There will be no labs for this week. Please don't hesitate to post any questions, discussions and related topics on this week's forum (https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1).

5 hours to complete

6 videos (Total 67 min), 4 readings, 2 quizzes

6 videos

Normal Distribution17mEvaluating the Normal Distribution2mWorking with the Normal Distribution5mBinomial Distribution17mNormal Approximation to Binomial14mWorking with the Binomial Distribution9m

4 readings

Lesson Learning Objectives10mLesson Learning Objectives10mSuggested Readings and Practice10mProject Instructions, Data Files, and Checklist2h

2 practice exercises

Week 4 Practice Quiz30mWeek 4 Quiz30m

Reviews

4.7

1300 reviews

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TOP REVIEWS FROM INTRODUCTION TO PROBABILITY AND DATA WITH R

by SFJul 24, 2021

Nice course! The professor gave very clear lectures to us, and the textbook is excellent and detailed. It took me more than 20 hours to finish the entire course, but I learned a lot from it!

by HWDec 13, 2017

The lectures were very clear and concise and the examples were very relevant. Some of the R instructions left a little something to be desired, but nothing a little time and google couldn't solve.

by BBSep 3, 2019

Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.

by RRSep 17, 2016

Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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Introduction to Probability and Data with R

About this Course

Skills you will gain