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Introduction to Probability and Data with R

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Introduction to Probability and Data with R

This course is part of Data Analysis with R Specialization

Instructor: Mine Çetinkaya-Rundel

310,610 already enrolled

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8 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments

Taught in English

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This course is part of the Data Analysis with R Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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Develop job-relevant skills with hands-on projects
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There are 8 modules in this course

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.

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.

What's included

1 video2 readings

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.

What's included

6 videos2 readings2 assignments

6 videosTotal 28 minutes

Introduction3 minutes
Data Basics5 minutes
Observational Studies & Experiments5 minutes
Sampling and sources of bias8 minutes
Experimental Design3 minutes
(Spotlight) Random Sample Assignment4 minutes

2 readingsTotal 20 minutes

Lesson Learning Objectives10 minutes
Suggested Readings and Practice10 minutes

2 assignmentsTotal 60 minutes

Week 1 Practice Quiz30 minutes
Week 1 Quiz30 minutes

To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.

What's included

2 readings1 assignment

Welcome to Week 2 of Introduction to Probability and Data! Hope you enjoyed materials from Week 1. This week we will delve into numerical and categorical data in more depth, and introduce inference.

What's included

7 videos3 readings2 assignments

7 videosTotal 46 minutes

Visualizing Numerical Data10 minutes
Measures of Center4 minutes
Measures of Spread7 minutes
Robust Statistics1 minute
Transforming Data3 minutes
Exploring Categorical Variables8 minutes
Introduction to Inference12 minutes

3 readingsTotal 30 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Suggested Readings and Practice10 minutes

2 assignmentsTotal 60 minutes

Week 2 Practice Quiz30 minutes
Week 2 Quiz30 minutes

To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.

What's included

2 readings1 assignment

Welcome to Week 3 of Introduction to Probability and Data! Last week we explored numerical and categorical data. This week we will discuss probability, conditional probability, the Bayes’ theorem, and provide a light introduction to Bayesian inference. Thank you for your enthusiasm and participation, and have a great week! I’m looking forward to working with you on the rest of this course.

What's included

9 videos3 readings2 assignments

9 videosTotal 82 minutes

Introduction6 minutes
Disjoint Events + General Addition Rule9 minutes
Independence10 minutes
Probability Examples9 minutes
(Spotlight) Disjoint vs. Independent3 minutes
Conditional Probability13 minutes
Probability Trees11 minutes
Bayesian Inference14 minutes
Examples of Bayesian Inference8 minutes

3 readingsTotal 30 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Suggested Readings and Practice10 minutes

2 assignmentsTotal 60 minutes

Week 3 Practice Quiz30 minutes
Week 3 Quiz30 minutes

To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.

What's included

2 readings1 assignment

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). Also this week, you will be asked to complete an initial data analysis project with a real-world data set. The project is designed to help you discover and explore research questions of your own, using real data and statistical methods we learn in this class. Please read the project instructions to complete this self-assessment.

What's included

6 videos5 readings2 assignments

6 videosTotal 67 minutes

Normal Distribution17 minutes
Evaluating the Normal Distribution3 minutes
Working with the Normal Distribution5 minutes
Binomial Distribution17 minutes
Normal Approximation to Binomial14 minutes
Working with the Binomial Distribution10 minutes

5 readingsTotal 160 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Suggested Readings and Practice10 minutes
Project Instructions, Data Files, and Checklist120 minutes
Share your learning experience10 minutes

2 assignmentsTotal 60 minutes

Week 4 Practice Quiz30 minutes
Week 4 Quiz30 minutes

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Instructor

Mine Çetinkaya-Rundel

Duke University

11 Courses432,841 learners

Offered by

Duke University

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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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. 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.

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.