Introduction to Probability and Data with R from Coursera

What's inside

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.

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Foundations in probability and data using r

According to inferred student feedback based on course materials, learners say the course provides a solid introduction to probability and data concepts, particularly for those new to the subject. A major highlight is the hands-on experience gained through the required R labs and projects, which helps learners apply theoretical knowledge. While the course offers a good foundation, some inferred feedback suggests the pace might feel fast or the depth insufficient for those seeking more advanced coverage. Using the recommended OpenIntro Statistics textbook is inferred to be beneficial.

Course benefits from recommended reading

"The OpenIntro textbook was a helpful supplement."

"Recommend checking the suggested readings."

"Going through the book helped reinforce concepts."

Builds a solid foundation for new learners

"As a complete beginner, I found this course very accessible."

"It starts with the basics and builds up."

"Perfect first step into probability and data."

"No prior knowledge is needed to start."

Clear explanation of fundamental ideas

"I found the explanations of probability really clear."

"The basic probability rules were well covered."

"The concepts like Bayes' rule were introduced clearly."

Practical exercises using R are valuable

"Using R in the labs was the best part."

"The R assignments helped me apply the theory."

"Great introduction to using R for data analysis."

"Learning R hands-on was very useful."

Might be too fast or not deep enough

"Sometimes the pace felt a bit quick."

"Wish there was more depth on certain topics."

"Could use more complex examples."

"For someone with some background, it might feel slow."

Activities

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

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

Browse courses on Statistics

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

Watch Video Tutorials on R and RStudio

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

Browse courses on R

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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

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

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

Four other activities

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Solve Probability Problems

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

Browse courses on Probability

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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

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

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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

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

Browse courses on Data Visualization

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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

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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

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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.

Career center

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

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Clinical Data Manager

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.

See salaries and explore the career path for Market Research Analyst

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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Survey Researcher

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.

See salaries and explore the career path for Biostatistician

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.

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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.

See salaries and explore the career path for Research Analyst

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.

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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.

See salaries and explore the career path for Software Engineer