Principles, Statistical and Computational Tools for Reproducible Data Science

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Tools for reproducible data science will help you ensure that you can trust your own research results, reproduce them yourself, and communicate them.

Last updated on October 21, 2021 11:18 pm

Introduction

Tools for Reproducible Data Science: Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

About this course

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 you will learn from Principles, Statistical and Computational Tools for Reproducible Data Science?

  • 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

1. Introduction to Reproducible Science

2. Fundamentals of Reproducible Science

3. Case Studies in Reproducible Research

4. Data Provenance

5. Statistical Methods for Reproducible Science

6. Computational Tools for Reproducible Science

7. Conclusion to the Course

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

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$99.00

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  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • Data Analysis Data repositiories Data Science Data Science with 'Python' Data Science with 'R' GitHub Practical Statistics

Description

Introduction

Tools for Reproducible Data Science: Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.

About this course

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 you will learn from Principles, Statistical and Computational Tools for Reproducible Data Science?

  • 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

1. Introduction to Reproducible Science

2. Fundamentals of Reproducible Science

3. Case Studies in Reproducible Research

4. Data Provenance

5. Statistical Methods for Reproducible Science

6. Computational Tools for Reproducible Science

7. Conclusion to the Course

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Intermediate
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • Data Analysis Data repositiories Data Science Data Science with 'Python' Data Science with 'R' GitHub Practical Statistics

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

$99.00

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