Principles, Statistical and Computational Tools for Reproducible Data Science from edX

Today the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.

To meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery.

This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project.

We’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.

Consider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure - and success - stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research, and to encourage and integrate with similar environments for your collaborators and colleagues. We look forward to seeing you in this course and the research you do in the future!

What's inside

Learning objectives

Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.
Fundamentals of reproducible science using case studies that illustrate various practices
Key elements for ensuring data provenance and reproducible experimental design
Statistical methods for reproducible data analysis

Computational tools for reproducible data analysis and version control (git/github, emacs/rstudio/spyder), reproducible data (data repositories/dataverse) and reproducible dynamic report generation (rmarkdown/r notebook/jupyter/pandoc), and workflows.
How to develop new methods and tools for reproducible research and reporting
How to write your own reproducible paper.

Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research.
Fundamentals of reproducible science using case studies that illustrate various practices
Key elements for ensuring data provenance and reproducible experimental design
Statistical methods for reproducible data analysis
Computational tools for reproducible data analysis and version control (git/github, emacs/rstudio/spyder), reproducible data (data repositories/dataverse) and reproducible dynamic report generation (rmarkdown/r notebook/jupyter/pandoc), and workflows.
How to develop new methods and tools for reproducible research and reporting
How to write your own reproducible paper.

Syllabus

Module 1: Introduction to Reproducible Science

Module 2: Fundamentals of Reproducible Science

Definitions and Concepts

Factors affecting reproducibility

Module 3: Case Studies in Reproducible Research

Module 4: Data Provenance

Project Design

Journal Requirements

Repositories

Privacy and Security

Module 5: Computational Tools for Reproducible Science

R and Rstudio

Python, Git, and GitHub

Creating a repository

Data sources

Dynamic report generation

Workflows

Module 6: A optional deeper dive into Statistical Methods for Reproducible Science

Prediction Models

Coefficient of determination

Brier score

Area Under the Curve (AUC)

Concordance in survival analysis

Cross-validation

Bootstrap

Simulations

Clustering

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops fundamentals and foundational skills for reproducible science in data science

Develops strong statistical methods, computational tools, and statistical tools for reproducible science in data science

Teaches foundational methods and tools for reproducible research in data science

Develops skills, knowledge, and tools that are core to current industry practices in data science

Taught by instructors with extensive experience and recognition in the field, including Curtis Huttenhower, John Quackenbush, Lorenzo Trippa, and Christine Choirat

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 Principles, Statistical and Computational Tools for Reproducible Data Science with these activities:

Course Materials Compilation

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Strengthens your grasp of course concepts by organizing and reviewing key resources.

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Gather lecture notes, slides, handouts, and assignments.
Organize the materials into a logical structure, such as by topic or module.
Review the materials regularly to reinforce your understanding.
Create summaries or mind maps of key concepts for quick reference.

Statistics Review

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Sharpen your statistical skills, ensuring you have a solid foundation for the course's data analysis and modeling components.

Browse courses on Statistics

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Review basic statistical concepts, such as descriptive statistics, probability, and inference.
Practice solving statistical problems using real-world datasets.
Take practice quizzes or mock tests to assess your understanding.
Consider using online resources or textbooks for additional support.

Study Group Discussions

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Fosters collaboration, improves understanding through peer-to-peer learning, and provides support for completing assignments and projects.

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Form a study group with classmates.
Meet regularly to discuss course material, work on assignments together, or practice solving problems.
Take turns leading discussions and presenting your findings.
Use online collaboration tools for effective communication outside of meetings.

Five other activities

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RStudio Tutorials

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Builds proficiency in R's syntax and packages, essential tools for reproducible data science.

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Go through the RStudio tutorials on data manipulation, visualization, and statistical modeling.
Complete the exercises at the end of each tutorial.
Apply what you've learned to your own data analysis projects.

Read The Pragmatic Programmer

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Provides context and best practices for data science and programming in general, including reusable software, testing, and debugging.

View The Pragmatic Programmer: Your Journey To... on Amazon

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Read all 26 chapters.
Complete all of the exercises.
Briefly summarize your key takeaways and favorite parts of the book.

Data Science Blog Post

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Enhances your communication skills and reinforces your understanding of reproducible data science by sharing your knowledge with others.

Browse courses on Data Science

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Choose a topic related to data science or reproducible research that you're passionate about.
Conduct thorough research to gather relevant information.
Write a well-structured and engaging blog post, explaining the concepts clearly.
Edit and proofread your post carefully before publishing it.
Share your post on social media and engage with readers in the comments section.

Kaggle Competitions

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Boosts your data science skills by exposing you to real-world datasets and competitions.

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Choose a Kaggle competition related to your interests.
Download the competition data and explore it.
Develop and train your machine learning model.
Submit your model and check your score.
Analyze your results to identify areas for improvement.

Personal Data Science Project

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Provides hands-on experience in applying your reproducible data science skills to a project of your choice.

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Define your project goals and objectives.
Gather and clean your data from various sources.
Explore your data using statistical techniques and visualizations.
Build and evaluate machine learning models to solve your problem.
Communicate your findings and insights in a clear and concise report.

Career center

Learners who complete Principles, Statistical and Computational Tools for Reproducible Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

To be successful as a Data Scientist, one must be familiar with using computational and statistical tools to analyze and interpret data. This course provides learners with a comprehensive foundation in these tools, while also establishing the importance of reproducibility. These concepts are crucial for a Data Scientist, as they allow for the accurate and reliable analysis of data, which is essential for making informed decisions. Additionally, a Data Scientist must be able to clearly communicate their findings. This course places a strong emphasis on reproducible reporting, and as such, learners will be well-equipped with the skills necessary for communicating their research effectively.

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Statistician

Statisticians play a vital role in analyzing and interpreting data. This course provides a thorough overview of the statistical methods used in reproducible data science, including prediction models, cross-validation, and bootstrapping. These methods are essential for Statisticians, as they allow for the accurate and reliable analysis of data, which is essential for making informed decisions. Furthermore, Statisticians must be able to clearly communicate their findings. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their research.

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Computational Biologist

Computational Biologists use computational and statistical tools to analyze and interpret biological data. This course provides a solid foundation in these tools, while also highlighting the importance of reproducibility. These concepts are essential for Computational Biologists, as they allow for the accurate and reliable analysis of data, which is critical for making informed decisions in the field of biology. Additionally, Computational Biologists must be able to effectively communicate their findings. This course places a strong emphasis on reproducible reporting, ensuring that learners develop the skills necessary for communicating their research clearly and concisely.

See salaries and explore the career path for Computational Biologist

Bioinformatician

Bioinformaticians use computational tools to analyze and interpret biological data. This course provides a comprehensive overview of the computational tools used in reproducible data science, including Git, GitHub, RStudio, and Python. These tools are essential for Bioinformaticians, as they allow for the accurate and reliable analysis of data, which is critical for making informed decisions in the field of biology. Furthermore, Bioinformaticians must be able to clearly communicate their findings. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their research.

See salaries and explore the career path for Bioinformatician

Data Analyst

Data Analysts use computational and statistical tools to analyze and interpret data. This course provides a solid foundation in these tools, while also emphasizing the importance of reproducibility. These concepts are crucial for Data Analysts, as they allow for the accurate and reliable analysis of data, which is essential for making informed decisions. Additionally, Data Analysts must be able to effectively communicate their findings. This course places a strong emphasis on reproducible reporting, ensuring that learners develop the skills necessary for communicating their research clearly and concisely.

See salaries and explore the career path for Data Analyst

Research Scientist

Research Scientists use computational and statistical tools to analyze and interpret data. This course provides a comprehensive overview of the computational and statistical tools used in reproducible data science, including R, Python, and Git. These tools are essential for Research Scientists, as they allow for the accurate and reliable analysis of data, which is critical for making informed decisions in various fields of research. Furthermore, Research Scientists must be able to clearly communicate their findings. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their research.

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Software Engineer

Software Engineers use computational tools to design, develop, and maintain software systems. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Software Engineers, as they allow for the efficient and reliable development of software systems. Additionally, Software Engineers must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Machine Learning Engineer

Machine Learning Engineers use computational and statistical tools to develop and deploy machine learning models. This course provides a comprehensive overview of the computational and statistical tools used in reproducible data science, including R, Python, and Git. These tools are essential for Machine Learning Engineers, as they allow for the accurate and reliable development and deployment of machine learning models. Furthermore, Machine Learning Engineers must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers use computational tools to design, build, and maintain data pipelines. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Data Engineers, as they allow for the efficient and reliable development and maintenance of data pipelines. Additionally, Data Engineers must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Database Administrator

Database Administrators use computational tools to design, build, and maintain databases. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Database Administrators, as they allow for the efficient and reliable development and maintenance of databases. Additionally, Database Administrators must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Systems Administrator

Systems Administrators use computational tools to design, build, and maintain computer systems. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Systems Administrators, as they allow for the efficient and reliable development and maintenance of computer systems. Additionally, Systems Administrators must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Network Administrator

Network Administrators use computational tools to design, build, and maintain computer networks. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Network Administrators, as they allow for the efficient and reliable development and maintenance of computer networks. Additionally, Network Administrators must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Security Analyst

Security Analysts use computational tools to design, build, and maintain computer security systems. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Security Analysts, as they allow for the efficient and reliable development and maintenance of computer security systems. Additionally, Security Analysts must be able to clearly communicate their design decisions and code. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Forensic Analyst

Forensic Analysts use computational tools to analyze and interpret digital evidence. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Forensic Analysts, as they allow for the efficient and reliable analysis and interpretation of digital evidence. Additionally, Forensic Analysts must be able to clearly communicate their findings. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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Penetration Tester

Penetration Testers use computational tools to test the security of computer systems and networks. This course provides a solid foundation in computational tools, including Git, GitHub, and Python. These tools are essential for Penetration Testers, as they allow for the efficient and reliable testing of the security of computer systems and networks. Additionally, Penetration Testers must be able to clearly communicate their findings. This course emphasizes reproducible reporting, ensuring that learners develop the skills necessary for effectively communicating their work.

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