About 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.
Created by: Duke University
Taught by: Mine Çetinkaya-Rundel, Assistant Professor of the Practice
Department of Statistical Science
| Basic Info |
Course 1 of 5 in the
Statistics with R Specialization.
|
| Level | Beginner |
| Commitment | 5 weeks of study, 5-7 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
About Introduction to Probability and Data
<p>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...
1 video, 2 readings
Introduction to Data
<p>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 proba...
6 videos, 5 readings, 1 practice quiz
Graded: Week 1 Quiz
Graded: Week 1 Lab: Introduction to R and RStudio
WEEK 2
Exploratory Data Analysis and Introduction to Inference
<p>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. </p>
7 videos, 5 readings, 1 practice quiz
Graded: Week 2 Quiz
Graded: Week 2 Lab: Introduction to Data
WEEK 3
Introduction to Probability
<p>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. </p>...
9 videos, 5 readings, 1 practice quiz
Graded: Week 3 Quiz
Graded: Week 3 Lab: Probability
WEEK 4
Probability Distributions
<p>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 knowle...
6 videos, 5 readings, 1 practice quiz
Graded: Week 4 Quiz
WEEK 5
Data Analysis Project
<p>Well done! You have reached the last week of Introduction to Probability and Data! There will not be any new videos in this week, instead, you will be asked to complete an initial data analysis project with a real-world data set. The project is designed to ...
2 readings
Graded: Data Analysis Project
FAQs
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Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
very interesting and well structured !!
A well-thought course on statistics. Some topic were explained too fast on the video, but I was able to catch up on the things that I have missed reading the text book. By the way, the text book is really well written - easy to read, concise and comprehensive. Great course overall. I wish they gave more additional info on R programming basics, but that's the only concern.
I doubt there are many professors around the globe who can match Mine Çetinkaya-Rundel's clarity of thought, her structured thinking and most importantly the the way info was packed into this course. Love the Openintro book, I still refer to it in my day to day work. Great going folks from Duke. Will see you in the Inferential Stats Module !
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Lectures were very well-prepared, slides were engaging, and there was a lot of optional but helpful reading material and exercise problems given. Overall a very well delivered introduction.
The final project was challenging in that it was open-ended, but it was also an opportunity to practice independent learning.