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Rafael Irizarry and Michael Love

This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.

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This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up two Professional Certificates and are self-paced:

Data Analysis for Life Sciences:

Genomics Data Analysis:

This class was supported in part by NIH grant R25GM114818.

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What's inside

Learning objectives

  • Random variables
  • Distributions
  • Inference: p-values and confidence intervals
  • Exploratory data analysis
  • Non-parametric statistics

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Introduces concepts and implementation of R programming in statistical data and statistical analysis with plenty of examples
Provides a solid foundation in statistical inference for understanding and computing p-values and confidence intervals
Covers exploratory data analysis techniques for exploring new data sets and determining appropriate approaches
Describes robust statistical techniques as alternatives when data do not fit assumptions required by standard approaches
Emphasizes reproducible research by teaching how to analyze data with R scripts
Offers two Professional Certificates: Data Analysis for Life Sciences and Genomics Data Analysis, providing a structured learning path

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Reviews summary

Statistics and r course overview

Learners say Statistics and R is a good course for beginners who want to learn the basics of statistics and the R programming language. The course is well-organized and the material is presented in a logical manner. The instructor is knowledgeable and engaging. However, some learners found the exercises to be challenging and the deadlines to be inflexible.
Good value for the price.
"It is a tremendous value for the price ;-)"
Knowledgeable and engaging instructor.
"The instructor was very good."
"The instructor is teaching everything slowly and gradually."
Well-organized course.
"The material was presented in a logical manner."
Inflexible deadlines.
"There is a fair amount of puzzling things out for yourself, but that's probably a good thing, too."
"all the quizzes were due about 4 months after the course actually started, which allows flexibility for students."
Exercises can be challenging.
"Course is not organised well, neither instructor doesn't explain material in depth."
"The way this course is taught feels pretty sloppy; it is easy to feel lost."
"I began to get lost in the part about t-tests."

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 Statistics and R with these activities:
Review prerequisites in probability
Strengthen your foundational knowledge in probability, which is crucial for understanding statistical concepts and inference techniques.
Browse courses on Probability
Show steps
  • Review your notes or textbooks on probability.
  • Take practice problems and quizzes to test your understanding.
Review 'Introduction to Statistical Learning'
Deepen your understanding of statistical concepts and techniques by reviewing a comprehensive textbook that covers the topics covered in the course.
Show steps
  • Obtain a copy of the textbook and schedule time for reading.
  • Read each chapter thoroughly and take notes.
  • Solve practice problems and exercises to reinforce your understanding.
Work through basic statistics drills
Complete practice problems and drills on basic concepts like distributions and inference to strengthen your foundation in statistics.
Show steps
  • Identify a reliable source of practice problems, such as online platforms or textbooks.
  • Start with simple problems and gradually increase the difficulty.
  • Solve the problems independently and check your answers.
  • Review your mistakes and seek clarification if needed.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore R programming tutorials
Engage with interactive tutorials that demonstrate R programming techniques specific to statistical analysis to enhance your proficiency.
Show steps
  • Find online tutorials or courses that cover R programming for statistical analysis.
  • Follow the tutorials step-by-step and practice the code examples.
  • Experiment with different R functions and apply them to real-world data.
Join a study group or online forum
Engage with peers who are also taking the course to discuss topics, share insights, and provide support to improve comprehension.
Show steps
  • Identify a study group or online forum related to the course.
  • Participate actively in discussions and ask questions.
  • Help other students understand concepts and troubleshoot problems.
Analyze a simple biological dataset
Carry out an end-to-end data analysis project using R to solidify your understanding of the concepts and techniques covered in the course.
Show steps
  • Identify a small biological dataset of interest.
  • Explore the data and identify research questions.
  • Apply statistical methods to analyze the data and answer the questions.
  • Visualize the results and draw conclusions.
Create a data visualization project
Enhance your data analysis skills by creating a data visualization that communicates insights effectively, solidifying your understanding of both the data and the techniques used.
Show steps
  • Choose an interesting dataset to explore.
  • Use R to analyze the data and identify key insights.
  • Design a visualization that effectively conveys the insights.
  • Present your visualization and explain your findings.
Become a mentor or tutor for beginners
Reinforce your understanding by helping others learn the basics of statistics and R programming, solidifying your grasp of foundational concepts.
Show steps
  • Identify opportunities to mentor or tutor beginners.
  • Prepare lesson plans and materials to support your learners.
  • Provide guidance, answer questions, and encourage your learners.

Career center

Learners who complete Statistics and R will develop knowledge and skills that may be useful to these careers:
Statistician
Statisticians collect, analyze, interpret, and present data. They use their skills to solve problems and make informed decisions in a variety of industries, including healthcare, finance, marketing, and government. This course would be useful for aspiring statisticians because it provides a strong foundation in statistical concepts and methods. The course also covers the R programming language, which is essential for data analysis.
Data Scientist
Data scientists use their skills in statistics, programming, and data analysis to extract insights from data. They work in a variety of industries, including technology, healthcare, and finance. This course would be useful for aspiring data scientists because it provides a strong foundation in statistical concepts and methods. The course also covers the R programming language, which is essential for data analysis.
Quantitative Analyst
Quantitative analysts use their skills in mathematics, statistics, and programming to analyze financial data and make investment decisions. They work in investment banks, hedge funds, and other financial institutions. This course would be useful for aspiring quantitative analysts because it provides a strong foundation in statistical concepts and methods. The course also covers the R programming language, which is essential for data analysis.
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in specified populations. They use their skills in statistics and epidemiology to investigate the causes of disease and to develop strategies for prevention and control.
Biostatistician
Biostatisticians use their skills in statistics and biology to design and analyze studies that investigate the effects of medical treatments and interventions. They work in academia, government, and the pharmaceutical industry.
Public Health Analyst
Public health analysts use their skills in statistics and public health to collect, analyze, and interpret data on the health of populations. They work in government agencies, non-profit organizations, and research institutions.
Operations Research Analyst
Operations research analysts use their skills in mathematics, statistics, and programming to solve problems in business and industry. They work in a variety of industries, including manufacturing, transportation, and healthcare.
Market Research Analyst
Market research analysts use their skills in statistics and marketing to collect, analyze, and interpret data on consumer behavior. They work in a variety of industries, including advertising, retail, and manufacturing.
Actuary
Actuaries use their skills in mathematics, statistics, and finance to assess risk and uncertainty. They work in insurance companies, pension funds, and other financial institutions.
Business Analyst
Business analysts use their skills in statistics, programming, and business to analyze data and solve problems in business.
Data Engineer
Data engineers use their skills in computer science and data management to design, build, and maintain data pipelines.
Mobile Developer
Mobile developers use their skills in computer science and mobile development to design, develop, and maintain mobile applications.
Software Engineer
Software engineers use their skills in computer science and programming to design, develop, and maintain software applications.
Web Developer
Web developers use their skills in computer science and web design to design, develop, and maintain websites.
Database Administrator
Database administrators use their skills in computer science and database management to design, build, and maintain databases.

Reading list

We've selected 35 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 Statistics and R.
Deep Learning comprehensive guide to deep learning. It covers the theory and practice of deep learning methods, as well as their applications in a wide range of fields.
Reinforcement Learning comprehensive guide to reinforcement learning. It covers the theory and practice of reinforcement learning methods, as well as their applications in a wide range of fields.
Provides a comprehensive overview of statistical learning methods and their applications in various domains. It valuable reference for understanding the concepts covered in the course.
Bayesian Data Analysis comprehensive book on Bayesian statistics. It covers the theory and practice of Bayesian methods, as well as their applications in a wide range of fields.
Statistical Rethinking comprehensive guide to Bayesian statistics. It covers the theory and practice of Bayesian methods, as well as their applications in a wide range of fields.
Presents a detailed overview of statistical inference, including estimation, hypothesis testing, and confidence intervals. It valuable resource for understanding the theoretical foundations of statistical inference and its applications.
Provides a comprehensive overview of data analysis in the life sciences, with a focus on statistical methods and R programming. It covers topics such as exploratory data analysis, hypothesis testing, and regression.
Provides a comprehensive overview of causal inference in statistics. It covers topics such as graphical models, causal effects, and counterfactuals. This book is considered one of the foundational texts in the field of causal inference.
This single-volume book provides a comprehensive overview of statistics, covering topics from probability and inference to linear and nonlinear models. It valuable reference for students and practitioners with a strong background in mathematics.
Doing Bayesian Data Analysis comprehensive guide to Bayesian statistics. It covers the theory and practice of Bayesian methods, as well as their applications in a wide range of fields.
Bayesian Methods for Data Analysis comprehensive guide to Bayesian statistics. It covers the theory and practice of Bayesian methods, as well as their applications in a wide range of fields.
Introduces the R programming language with a focus on data science applications. It provides a good starting point for those new to R and enhances the course's hands-on programming exercises.
Classic text on statistical methods using S-PLUS, which similar programming language to R. It covers a wide range of topics, from basic concepts to advanced techniques.
Bayesian Analysis for the Social Sciences comprehensive guide to Bayesian statistics for social scientists. It covers the theory and practice of Bayesian methods, as well as their applications in a wide range of fields.
Bayesian Computation with R comprehensive guide to Bayesian statistics using R. It covers the theory and practice of Bayesian methods, as well as their applications in a wide range of fields.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in computer science. It covers topics such as probability distributions, random variables, hypothesis testing, and Bayesian inference.
Provides a practical introduction to deep learning using R. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. A valuable resource for those seeking an introduction to deep learning with R.
Provides a comprehensive overview of time series analysis using R. It covers topics such as time series decomposition, forecasting, and model evaluation. A valuable resource for those seeking an in-depth understanding of time series analysis.
This comprehensive textbook covers a broad range of statistical topics, including probability, inference, regression, and Bayesian statistics, providing a solid foundation for understanding statistical concepts.
Introduces the ggplot2 package for data visualization in R. It teaches best practices for creating informative and visually appealing graphs, enhancing the course's emphasis on data exploration.
Showcases real-world applications of Bioconductor, a software suite for bioinformatics analysis. Reading it can provide insights into the practical use of statistical methods in genomics research.
Focuses on statistical methods specifically designed for bioinformatics applications. It provides a deeper understanding of techniques used in genetic and genomic analysis.
Widely-used textbook for introductory statistics courses. It provides a clear and concise explanation of statistical concepts, with numerous examples and exercises.
Provides a practical introduction to data science, covering topics such as data mining, machine learning, and statistical modeling, with a focus on applications in business.
Provides a practical introduction to machine learning using R, covering topics such as data preprocessing, model building, and evaluation.
Provides a clear and concise introduction to probability theory, covering topics such as random variables, distributions, and conditional probability, providing a strong foundation for understanding statistics.
This free online textbook provides a comprehensive introduction to statistics, covering topics such as descriptive statistics, probability, and regression, and is particularly accessible for learners with limited mathematical background.
This online video series provides a beginner-friendly introduction to data science, covering topics such as data exploration, probability, and machine learning.
This humorous and accessible book provides a visual introduction to statistics, covering topics such as probability, inference, and regression, and is particularly useful for learners who prefer a non-technical approach.
This textbook provides a comprehensive introduction to econometrics, covering topics such as linear models, time series analysis, and panel data, and is commonly used as a textbook in academic institutions.

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