Essential Tools for Data Science Workflow
Master key productivity tools for data science: Unix/Linux, Git, GitHub, and RStudio to organize projects and create reproducible reports.
Master key productivity tools for data science: Unix/Linux, Git, GitHub, and RStudio to organize projects and create reproducible reports.
This practical course teaches essential productivity tools for modern data science workflows. Students learn to manage files and directories using Unix/Linux, implement version control with Git, collaborate through GitHub, and create reproducible reports with R Markdown in RStudio. The course focuses on organizing data analysis projects efficiently, tracking changes in scripts and reports, and facilitating collaboration in data science projects through proper tool usage and best practices.
4.3
(33 ratings)
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What you'll learn
Master Unix/Linux commands for efficient file system management
Implement version control effectively using Git
Create and maintain project repositories on GitHub
Utilize RStudio's features for enhanced productivity
Generate reproducible reports using R Markdown
Organize data analysis projects efficiently
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course equips data scientists with essential productivity tools and workflows. The curriculum covers fundamental skills in Unix/Linux file management, version control with Git, collaboration through GitHub, and report generation using R Markdown in RStudio. Students learn to organize complex data analysis projects, maintain version control of their work, and create reproducible reports. The course emphasizes practical applications and best practices for efficient data science project management.
Fee Structure
Instructor
32 Courses
Harvard Biostatistics Professor and Genomics Data Analysis Pioneer
Rafael Irizarry is a distinguished Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute. His expertise spans genomics, data analysis, and the R programming language. Irizarry's career has been marked by significant contributions to the field of genomics data analysis over the past two decades
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4.3 course rating
33 ratings
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