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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.
Read more
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.
Read more
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:
- PH525.1x: Statistics and R for the Life Sciences
- PH525.2x: Introduction to Linear Models and Matrix Algebra
- PH525.3x: Statistical Inference and Modeling for High-throughput Experiments
- PH525.4x: High-Dimensional Data Analysis
Genomics Data Analysis:
- PH525.5x: Introduction to Bioconductor
- PH525.6x: Case Studies in Functional Genomics
- PH525.7x: Advanced Bioconductor
This class was supported in part by NIH grant R25GM114818.
Register for this course and see more details by visiting:
OpenCourser.com/course/bl35nn/statistics
What's inside
Learning objectives
- Random variables
- Distributions
- Inference: p-values and confidence intervals
- Exploratory data analysis
- Non-parametric statistics
- Random variables
- Distributions
- Inference: p-values and confidence intervals
- Exploratory data analysis
- Non-parametric statistics
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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 for life sciences
According to learners, this course offers a strong foundation in Statistics and R, specifically tailored for applications in the Life Sciences. Many find the content comprehensive and the R programming examples highly practical for applying statistical concepts to real-world data. While the course is praised for its relevance to biological research and data analysis, several students note that the pace can be challenging, particularly as the difficulty ramps up quickly. It is generally recommended to have some prior background in either statistics or R to navigate the material smoothly. The problem sets and labs are often highlighted as valuable components for reinforcing learning through hands-on practice.
Better with some prior R or stats knowledge.
"I recommend having some prior experience with either R or statistics."
"If you are a complete beginner in both, be prepared for a significant challenge."
"While it starts simple, a basic foundation helps a lot in the later parts."
Good code examples connect theory to practice.
"The R programming examples were clear and easy to follow."
"I liked how the course showed exactly how to implement the stats concepts in R."
"The code demonstrations made complex ideas understandable."
Assignments and labs are hands-on.
"The problem sets were very useful for practicing and understanding the concepts."
"I really appreciated the hands-on coding exercises in R."
"Working through the labs helped solidify my learning immensely."
Directly applicable to biological data.
"The examples and problem sets were highly relevant to life sciences research."
"I can immediately apply what I learned to my own biological data analysis."
"Focusing on biological data made the learning much more engaging and relevant for me."
Thorough coverage of statistics and R basics.
"The course provided a very thorough foundation in both statistics and R."
"I felt I gained a solid understanding of the core statistical concepts and how to apply them."
"It covers all the necessary basics to start analyzing biological data."
Pace is fast, difficulty increases quickly.
"The course ramps up in difficulty very quickly, which was challenging."
"The pace is quite fast, especially if you are new to both R and statistics."
"I found it difficult to keep up with the speed of new concepts being introduced."
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
Show steps
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'
Show steps
Deepen your understanding of statistical concepts and techniques by reviewing a comprehensive textbook that covers the topics covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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.
Review prerequisites in probability
Show steps
Strengthen your foundational knowledge in probability, which is crucial for understanding statistical concepts and inference techniques.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review your notes or textbooks on probability.
- Take practice problems and quizzes to test your understanding.
Review 'Introduction to Statistical Learning'
Show steps
Deepen your understanding of statistical concepts and techniques by reviewing a comprehensive textbook that covers the topics covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
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
Show steps
Complete practice problems and drills on basic concepts like distributions and inference to strengthen your foundation in statistics.
Show steps
Show steps
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
Show steps
Engage with interactive tutorials that demonstrate R programming techniques specific to statistical analysis to enhance your proficiency.
Show steps
Show steps
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
Show steps
Engage with peers who are also taking the course to discuss topics, share insights, and provide support to improve comprehension.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
Reinforce your understanding by helping others learn the basics of statistics and R programming, solidifying your grasp of foundational concepts.
Show steps
Show steps
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
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 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 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.
Biostatistician
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.
Epidemiologist
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.
Public Health Analyst
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 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.
Actuary
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.
Market Research Analyst
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.
Business Analyst
Business Analyst
Business analysts use their skills in statistics, programming, and business to analyze data and solve problems in business.
Software Engineer
Software Engineer
Software engineers use their skills in computer science and programming to design, develop, and maintain software applications.
Data Engineer
Data Engineer
Data engineers use their skills in computer science and data management to design, build, and maintain data pipelines.
Database Administrator
Database Administrator
Database administrators use their skills in computer science and database management to design, build, and maintain databases.
Web Developer
Web Developer
Web developers use their skills in computer science and web design to design, develop, and maintain websites.
Mobile Developer
Mobile Developer
Mobile developers use their skills in computer science and mobile development to design, develop, and maintain mobile applications.
For more career information including salaries, visit:
OpenCourser.com/course/bl35nn/statistics
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.
Save
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.
Delves deeper into statistical learning algorithms and their theoretical foundations. Reading it alongside the course can enhance understanding of advanced topics.
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.
Save
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.
Provides a solid foundation in statistical inference, covering concepts such as point estimation, hypothesis testing, and confidence intervals. It complements the course's focus on practical applications.
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 machine learning using R. It covers topics such as supervised learning, unsupervised learning, and model evaluation. A useful resource for those seeking an introduction to machine learning within R.
Save
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.
Save
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.
Save
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.
Save
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.
Provides a clear and concise introduction to statistical methods using R. It covers the basics of statistical inference, as well as more advanced topics such as linear regression and analysis of variance.
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.
This textbook provides a practical introduction to probability and statistics, with a focus on applications in engineering and science, and is commonly used as a textbook in academic institutions.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bl35nn/statistics
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Rating
3.5
Effort
2 to 4 hours per week for 4 weeks
Level
Intermediate
Via
edX
Institution
Harvard University
Instructors
Rafael Irizarry
Michael Love
Language
English
Transcripts
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This course belongs to a
collection
called
Data Analysis for Life Sciences. It's comprised of four courses:
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