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.
Overview
Syllabus
- 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.
- 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.
- Introduction to Data Project
- To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
- Exploratory Data Analysis and Introduction to Inference
- 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.
- Exploratory Data Analysis and Introduction to Inference Project
- To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
- Introduction to Probability
- 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.
- Introduction to Probability Project
- To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
- 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). 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.
Taught by
Mine Çetinkaya-Rundel
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Reviews
3.5 rating, based on 6 Class Central reviews
4.7 rating at Coursera based on 5683 ratings
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There should be 2 separate ratings in this case:
a) the course is great in explaining basic concepts of probability and statistics; the videos are clear, there are nice examples and in general the material is quite engaging; it's probably one of the most concise and clear intros to statistics available;
b) however Coursera's policy makes the course unatractive for people, who tries to take a course for free (without certificate); quizes are unavailable, so there is no chance to verify your understanding and you're left with just watching videos; I strongly prefer edX.org approach - i.e. providing all features for free and closing only certificates behind the paywall. -
This is the first course in the new (at the time of this writing) Statistics with R specialization from Duke. This specialization comes out of the popular Data Analysis and Statistical Inference course which used to be offered on Coursera. Introdu…
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The instructor was very engaging, and I enjoyed the in-video knowledge checks.
However, I decided not to finish the course because the jump in difficulty from lecture to R programming assignments was astronomical. I'm new to R and was hoping this would help me learn R while refreshing my statistics knowledge. It ultimately just made me feel frustrated and resulted in me dropping the course because it felt like you needed to know R more intimately than a beginner in order to complete the assignments. -
