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Mine Çetinkaya-Rundel

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.

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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.

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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

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

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.

Good to know

Know what's good ,
what to watch for , and
possible dealbreakers

Learn about exploring data, probability, and Bayes' rule with free software

Suitable for beginners in data analysis and probability

Develops foundational knowledge for inference and modeling

Save Introduction to Probability and Data with R to your list so you can find it easily later:

Introduction to Probability and Data Analysis with R, offered by Duke University is widely accepted. Learners say they appreciate the clear explanations and real-life examples.
The course is structured in a way that offers flexibility and convenience. Students can learn at their own pace, and they can access the materials whenever they want. The course also provides opportunities for students to interact with each other and with the instructors through discussion forums and peer review.
Students praise the course instructor, Mine Çetinkaya-Rundel, for her clear explanations and engaging teaching style. They also appreciate the fact that the course is project-based, which allows them to apply their learning to real-world problems.
However, students also note that the course can be challenging, especially for those who are new to R or to statistics and that the final project can be very time-consuming.
Overall, learners say Introduction to Probability and Data Analysis with R is a great course for anyone who wants to learn more about probability and data analysis.
Students found the following aspects of the course particularly valuable:
* The clear explanations and real-world examples
* The flexibility and convenience of the course format
* The opportunities for interaction with other students and with the instructors
* The project-based approach
Keep in mind that coursework can be challenging, particularly for those new to R programming or statistics, and the final project can be very time-consuming.

learners say
- Clear explanations
- Real-life examples
- Well-organized lectures
- Excellent instructor
- Project-based learning

"The course is very well organized. The contents of the videos are well aligned with the quizzes and R Labs . Very clear presentation of the learning goals of each week."

"I really liked this course, it is very well organised and the explanations are good and sufficient."

"Introduction to Probability and Data with R was the best course I have taken so far via Coursera. The course materials (textbook and RMD files for R) were very helpful and well organised and the examples given in the videos were elucidative. I will certainly continue the specialisation tract. I highly recommend it to everyone interested in using computational methods to understand data."

- Steep learning curve
- Time-consuming final project

"The course is very demanding and takes a lot of time. It is difficult as well."

"It does introduce students to probability very well. But the R learning was very limited to simply following instructions in a file, with little in the way of explaining R or what R commands do."

"Instead they leave students to figure it out."

- Insufficient instruction
- Assumes some prior knowledge
- Code no longer works

"The very first setup of R packages is broken."

"It is not designed for someone to start learning statistics or R language. The free textbook written by the course instructor is a better source to learn statistics. Unlike many other reviewers, the final project was fun and insightful for me."

"However, if your goal (like mine) is to gain introductory skills with R, I *do not* recommend this course. The labs in Weeks 1-4 were very difficult and there was almost no guidance on how to complete; I struggled through them by finding solutions on StackExchange, etc. But the worst was the final project in Week 5; the material in Weeks 1-4 was absolutely inadequate to prepare the novice user of R."

- Very time-consuming
- Requires a good grasp of R
- Vague instructions

"This course did an excellent job of challenging at an appropriate level. Full disclosure, I have never done any programming in R and have been playing catch-up on that part the whole time but the materials are set up in a very helpful but not too hand-holding way. I particularly enjoyed the final assignment. It really served to drive home the material covered!"

"The instruction in statistics is understandable and complete. The final homework problem/test is way outsized for this introductory level course."

"If your goal is to learn R & RStudio, I'd have to suggest a more rigorous, step-by-step approach."

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 Probability and Data with R with these
activities:

Review Notes from Previous Statistics Courses

Show steps

Review your notes from previous statistics courses to refresh your knowledge of the foundational concepts.

Browse courses on
Statistics

Show steps

- Gather your notes from previous statistics courses.
- Review the notes and focus on the key concepts.

Watch Video Tutorials on R and RStudio

Show steps

Watch video tutorials to learn the basics of using R and RStudio for data analysis.

Browse courses on
R

Show steps

- Find video tutorials on YouTube or other platforms.
- Watch the tutorials and take notes.
- Practice using R and RStudio on your own.

Join a Study Group

Show steps

Join a study group to discuss the course material with other students and reinforce your learning.

Show steps

- Find a study group or create your own.
- Meet regularly to discuss the material.
- Work together on practice problems and projects.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Solve Probability Problems

Show steps

Practice solving a variety of probability problems to improve your skills.

Browse courses on
Probability

Show steps

- Find a collection of probability problems online or in a textbook.
- Work through the problems step-by-step.
- Check your answers against the provided solutions.

Write a Summary of a Research Paper

Show steps

Write a summary of a research paper related to probability and data to deepen your understanding of the material.

Show steps

- Find a research paper that interests you.
- Read the paper carefully and take notes.
- Write a summary that includes the main findings and conclusions of the paper.

Create a Data Visualization Project

Show steps

Create a data visualization project to demonstrate your understanding of data exploration and visualization techniques.

Browse courses on
Data Visualization

Show steps

- Choose a dataset that interests you.
- Explore the data and identify patterns and trends.
- Create visualizations that effectively communicate your findings.

Start a Data Analysis Project

Show steps

Start a data analysis project to apply the skills and knowledge you are learning in this course to a real-world problem.

Browse courses on
Data Analysis

Show steps

- Identify a problem that you want to solve using data.
- Collect and clean the data.
- Analyze the data using the techniques you have learned in this course.
- Present your findings in a clear and concise way.

Learners who complete Introduction to Probability and Data with R will develop knowledge and skills
that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. An understanding of probability and statistics is essential for this role as it allows one to design studies, analyze data, and draw conclusions from the results. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in data science. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin data science.

Operations Research Analyst

Operations Research Analysts are responsible for using mathematical and statistical techniques to solve business problems. An understanding of probability and statistics is essential for this role as it allows one to model systems, analyze data, and make recommendations for improvement. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in operations research. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin operations research.

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. An understanding of probability and statistics is essential for this role as it allows one to design studies, analyze data, and draw conclusions from the results. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in statistics. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin statistics.

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. An understanding of probability and statistics is essential for this role as it allows one to analyze data, draw conclusions, and make recommendations for improving business decisions. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in data analysis. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin data analysis.

Quantitative Analyst

Quantitative Analysts are responsible for using mathematical and statistical techniques to analyze financial data and make investment decisions. An understanding of probability and statistics is essential for this role as it allows one to analyze financial data, assess risk, and make informed decisions. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in quantitative analysis. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin quantitative analysis.

Actuary

Actuaries are responsible for assessing financial risks and developing strategies to mitigate those risks. An understanding of probability and statistics is essential for this role as it allows one to analyze data, assess risk, and make informed decisions. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in actuarial science. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin actuarial science.

Risk Analyst

Risk Analysts are responsible for identifying, assessing, and mitigating risks. An understanding of probability and statistics is essential for this role as it allows one to analyze data, assess risk, and make informed decisions. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in risk analysis. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin risk analysis.

Survey Researcher

Survey Researchers are responsible for designing, conducting, and analyzing surveys. An understanding of probability and statistics is essential for this role as it allows one to design studies, analyze data, and draw conclusions from the results. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in survey research. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin survey research.

Clinical Data Manager

Clinical Data Managers are responsible for the design, collection, and analysis of clinical data. An understanding of probability and statistics is essential for this role as it allows one to design studies, analyze data, and draw conclusions from the results. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in clinical data management. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin clinical data management.

Epidemiologist

Epidemiologists are responsible for studying the distribution and determinants of health-related states and events in specified populations. An understanding of probability and statistics is essential for this role as it allows one to analyze data, identify risk factors, and make recommendations for improving public health. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in epidemiology. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin epidemiology.

Biostatistician

Biostatisticians are responsible for applying statistical methods to solve problems in biology and medicine. An understanding of probability and statistics is essential for this role as it allows one to analyze data, draw conclusions, and make recommendations for improving health outcomes. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in biostatistics. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin biostatistics.

Market Research Analyst

Market Research Analysts are responsible for collecting and analyzing data to understand consumer behavior. An understanding of probability and statistics is essential for this role as it allows one to design studies, analyze data, and draw conclusions from the results. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in market research. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin market research.

Research Analyst

Research Analysts are responsible for conducting research and analyzing data to identify trends and patterns. An understanding of probability and statistics is essential for this role as it allows one to analyze data, draw conclusions, and make recommendations for improving business decisions. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in research analysis. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin research analysis.

Financial Analyst

Financial Analysts are responsible for analyzing financial data to make investment recommendations. An understanding of probability and statistics is essential for this role as it allows one to analyze financial data, assess risk, and make informed decisions. The course introduces learners to sampling, probability, and hypothesis testing, which are all key concepts in financial analysis. Those wishing to enter this career field should take this course to build a strong foundation in the statistical principles that underpin financial analysis.

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. While an understanding of probability and statistics is not essential for this role, it may be helpful in certain areas of software engineering, such as data analysis and machine learning. The course introduces learners to sampling, probability, and hypothesis testing, which may be useful for Software Engineers who wish to specialize in these areas. Those wishing to enter this career field may consider taking this course to gain a better understanding of the statistical principles that underpin data analysis and machine learning.

For more career information including salaries, visit:
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OpenCourser.com/course/k2njvj/introduction
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We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Probability and Data with R.

Provides a comprehensive guide to the ggplot2 package for data visualization.

Provides a comprehensive overview of data analysis and graphics using the R programming language.

Provides a comprehensive introduction to causal inference.

Provides a practical introduction to data science using the R programming language.

Provides a comprehensive introduction to Bayesian analysis using the R programming language.

Provides a comprehensive guide to the R programming language.

Provides a comprehensive introduction to machine learning using the R programming language.

Provides a collection of recipes for common data analysis tasks in R.

Provides a comprehensive introduction to deep learning using the R programming language.

For more information about how these books relate to this course, visit:
**
OpenCourser.com/course/k2njvj/introduction
**

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