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Rafael Irizarry

A typical data analysis project may involve several parts, each including several data files and different scripts with code. Keeping all this organized can be challenging.

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A typical data analysis project may involve several parts, each including several data files and different scripts with code. Keeping all this organized can be challenging.

Part of our Professional Certificate Program in Data Science, this course explains how to use Unix/Linux as a tool for managing files and directories on your computer and how to keep the file system organized. You will be introduced to the version control systems git, a powerful tool for keeping track of changes in your scripts and reports. We also introduce you to GitHub and demonstrate how you can use this service to keep your work in a repository that facilitates collaborations.

Finally, you will learn to write reports in R markdown which permits you to incorporate text and code into a document. We'll put it all together using the powerful integrated desktop environment RStudio.

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What's inside

Learning objectives

  • How to use unix/linux to manage your file system
  • How to perform version control with git
  • How to start a repository on github
  • How to leverage the many useful features provided by rstudio

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by Rafael Irizarry, who is recognized for their work in biostatistics
Provides foundational knowledge in using Unix/Linux for managing data files
Introduces learners to version control using git, a valuable tool in data science
Facilitates collaboration through GitHub repository management
Emphasizes practical application in data analysis projects
Leverages the capabilities of RStudio for data analysis and report writing

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Reviews summary

Comprehensive data tools overview

Learners say this course offers a comprehensive overview of foundational data science tools, with a particular focus on RStudio, Git, and GitHub. Although the course's ease of use is praised, reviewers recommend the course primarily for beginners or those needing a refresher.
Easy to follow for beginners
"Very easy but useful review of the Data Science Software Tools: Rstudio, Git, GitHub."
Exceptional Git/GitHub instruction
"I found the Git/GitHub sections very useful: after having struggled a long time using this tool due to rather unclear tutorials, this is the first concise but very clear explanations of the workings of Git/GitHub and how to properly use it."

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 Data Science: Productivity Tools with these activities:
Organize Your Notes and Resources
Compile and organize notes, assignments, and quizzes to enhance understanding and facilitate review.
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  • Gather all relevant course materials
  • Categorize and sort materials for easy retrieval
  • Create a system for ongoing note-taking
Git Tutorial for Beginners
Complete a guided tutorial to familiarize yourself with git, enabling effective version control.
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  • Learn the fundamentals of version control and Git
  • Explore common Git commands (commit, pull, push)
  • Practice creating branches and resolving conflicts
  • Understand the workflow of a Git repository
Practice Unix Commands
Review basic Unix commands to strengthen foundational knowledge and enhance proficiency.
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  • Review basic file and directory commands (ls, cd, mkdir, rm)
  • Practice using shell pipes and redirection
  • Explore advanced command options and arguments
  • Implement Unix commands in scripts
Four other activities
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Show all seven activities
GitHub Workshop
Attend a workshop to enhance GitHub knowledge, facilitating collaboration and project management.
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  • Create and manage repositories on GitHub
  • Collaborate on projects with other GitHub users
  • Integrate GitHub with other tools and technologies
Mentor Newcomers with Git
Share knowledge and expertise by mentoring others, reinforcing understanding and fostering community growth.
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  • Attend online forums or join coding communities
  • Offer guidance and support to those seeking assistance with Git
  • Create tutorials or resources to facilitate learning
Build a Personal Website with R Markdown
Create a personal website using R Markdown, combining data, text, and code for effective communication.
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Show steps
  • Design and structure the website layout
  • Incorporate R code and visualizations
  • Publish the website online
Contribute to R Markdown Documentation
Make valuable contributions to the R Markdown documentation, enhancing the community and expanding knowledge.
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Show steps
  • Identify areas for improvement or expansion
  • Research and gather relevant information
  • Write and submit your contribution

Career center

Learners who complete Data Science: Productivity Tools will develop knowledge and skills that may be useful to these careers:
Data Scientist
As a Data Scientist, you will leverage your knowledge of Unix/Linux, Git, GitHub, and RStudio to effectively manage data, collaborate on projects, and produce high-quality data products. This course will provide you with the skills and tools you need to succeed in this in-demand field.
Data Analyst
As a Data Analyst, you will use your mastery of Unix/Linux, Git, GitHub, and RStudio to manage large datasets, track changes in your scripts and reports, and produce insightful visualizations. This course will help you build a strong foundation in these essential tools for data analysis.
Machine Learning Engineer
As a Machine Learning Engineer, you will use your expertise in Unix/Linux, Git, GitHub, and RStudio to build and deploy machine learning models. This course will provide you with the foundation you need to succeed in this rapidly growing field.
Software Engineer
As a Software Engineer, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to develop and maintain software applications. This course will help you build a strong foundation in the essential tools and technologies used by software engineers.
Data Engineer
As a Data Engineer, you will use your expertise in Unix/Linux, Git, GitHub, and RStudio to design and build data pipelines. This course will provide you with the skills and knowledge you need to succeed in this in-demand field.
Research Analyst
As a Research Analyst, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to gather and analyze data. This course will provide you with the skills and tools you need to succeed in this challenging and rewarding field.
Database Administrator
As a Database Administrator, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to manage databases. This course will provide you with the skills and knowledge you need to succeed in this critical role.
Project Manager
As a Project Manager, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to manage projects and collaborate with team members. This course will provide you with the skills and knowledge you need to succeed in this critical role.
Business Analyst
As a Business Analyst, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to analyze data and solve business problems. This course will provide you with the skills and knowledge you need to succeed in this in-demand field.
Product Manager
As a Product Manager, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to develop and manage products. This course will provide you with the skills and knowledge you need to succeed in this challenging and rewarding field.
Consultant
As a Consultant, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to solve problems and improve business outcomes. This course will provide you with the skills and knowledge you need to succeed in this challenging and rewarding field.
Marketing Analyst
As a Marketing Analyst, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to analyze data and develop marketing campaigns. This course will provide you with the skills and knowledge you need to succeed in this in-demand field.
Financial Analyst
As a Financial Analyst, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to analyze data and make investment recommendations. This course will provide you with the skills and knowledge you need to succeed in this challenging and rewarding field.
Technical Writer
As a Technical Writer, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to create and maintain technical documentation. This course will provide you with the skills and knowledge you need to succeed in this critical role.
User Experience Designer
As a User Experience Designer, you will use your knowledge of Unix/Linux, Git, GitHub, and RStudio to design and evaluate user interfaces. This course will provide you with the skills and knowledge you need to succeed in this rapidly growing field.

Reading list

We've selected 12 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 Data Science: Productivity Tools.
Provides a comprehensive guide to deep learning, a subfield of machine learning. It covers various deep learning architectures and algorithms, making it a valuable resource for data scientists interested in exploring this rapidly evolving field.
Provides a comprehensive overview of statistical learning methods. It covers various statistical models and techniques, making it a valuable resource for data scientists who want to deepen their understanding of statistical analysis.
Offers a thorough introduction to Git and provides step-by-step guidance on using it effectively. It valuable resource for data scientists seeking to improve their version control skills.
Provides a comprehensive guide to R Markdown, a powerful tool for creating dynamic reports and documents. Data scientists can use this book to learn how to incorporate text, code, and visualizations into their reports.
Provides a comprehensive guide to ggplot2, a powerful R library for data visualization. It covers various ggplot2 features and techniques, making it a valuable resource for data scientists who want to create effective and visually appealing data visualizations.
Covers advanced data analysis techniques using R. It provides a solid foundation for data scientists looking to expand their knowledge and skills in statistical modeling and machine learning.
Provides a concise and practical guide to using Git, a popular version control system. Many data scientists use Git to keep track of changes in their scripts and reports.
Offers a practical guide to data analysis using Python. It covers essential Python libraries and techniques, making it a useful resource for data scientists who want to enhance their Python skills.
Offers a practical introduction to R programming for data science. It covers essential R concepts and techniques, making it a useful resource for data scientists who want to enhance their R skills or transition from other programming languages.
Is tailored for bioinformaticians and provides a practical introduction to Unix. It covers essential Unix commands and concepts, making it a useful resource for data scientists working in the field of bioinformatics.
This comprehensive handbook covers various aspects of Unix and Linux system administration, including file and directory management. can serve as a reference for data scientists looking to enhance their understanding of Unix/Linux.
Provides a comprehensive overview of data science and its applications in business. It covers various topics relevant to data science, including data management, analysis, and visualization.

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