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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/yojs5w/data
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
- 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
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
Essential data science productivity tools
According to students, this course is highly valuable for mastering essential data science productivity tools. Learners particularly appreciate its focus on organizing projects with Unix/Linux, effective version control using Git and GitHub, and practical report generation with R Markdown in RStudio. The course is seen as providing foundational skills necessary to streamline data analysis workflows, making it a strong asset for professionals and aspiring data scientists looking to enhance their efficiency and collaborative capabilities. Many would likely find the practical approach and clear explanations beneficial, especially for gaining competence in these crucial areas.
Introduces complex tools in an understandable way, beneficial for new learners.
"Even though I was new to Unix and Git, the course presented the concepts clearly and progressively."
"This course is a great entry point for anyone needing to get started with these essential data tools."
"It built my confidence in using version control and command-line tools effectively, which seemed daunting before."
Emphasizes hands-on application of productivity tools for data science.
"The hands-on exercises for Git and RStudio were extremely helpful in solidifying my understanding of the concepts."
"I really appreciate the practical approach, allowing me to apply what I learned immediately to my projects."
"The course provides a strong practical foundation for using these tools in real-world data science scenarios."
Covers core productivity tools like Unix, Git, and RStudio critical for data science.
"I finally grasp Unix commands and Git version control, which are non-negotiable skills in my field."
"The integration of RStudio with R Markdown made reporting so much easier for me; it's a game-changer."
"Learning Git and GitHub in a structured way was incredibly valuable; it demystified these essential tools."
Equips learners with essential tools to organize and streamline data analysis.
"This course taught me how to truly organize my data science projects efficiently."
"I can now streamline my analysis workflow, which saves a lot of time and hassle."
"Understanding these productivity tools helps prevent common errors in data handling."
Offers a wide introduction to various tools, not deep dives into each.
"While comprehensive in scope, I wished for more advanced examples or troubleshooting for Git."
"The course provides a good overview, but those looking for deep mastery might need additional resources."
"It covers a lot of ground quickly, so some topics felt a bit rushed if you didn't have prior exposure."
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
Show steps
Compile and organize notes, assignments, and quizzes to enhance understanding and facilitate review.
Browse courses on
Note-Taking
Show steps
-
Gather all relevant course materials
-
Categorize and sort materials for easy retrieval
-
Create a system for ongoing note-taking
Git Tutorial for Beginners
Show steps
Complete a guided tutorial to familiarize yourself with git, enabling effective version control.
Browse courses on
Git
Show steps
-
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
Show steps
Review basic Unix commands to strengthen foundational knowledge and enhance proficiency.
Browse courses on
Unix
Show steps
-
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
Expand to see all activities and additional details
Show all seven activities
GitHub Workshop
Show steps
Attend a workshop to enhance GitHub knowledge, facilitating collaboration and project management.
Browse courses on
GitHub
Show steps
-
Create and manage repositories on GitHub
-
Collaborate on projects with other GitHub users
-
Integrate GitHub with other tools and technologies
Mentor Newcomers with Git
Show steps
Share knowledge and expertise by mentoring others, reinforcing understanding and fostering community growth.
Browse courses on
Git
Show steps
-
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
Show steps
Create a personal website using R Markdown, combining data, text, and code for effective communication.
Browse courses on
R Markdown
Show steps
-
Design and structure the website layout
-
Incorporate R code and visualizations
-
Publish the website online
Contribute to R Markdown Documentation
Show steps
Make valuable contributions to the R Markdown documentation, enhancing the community and expanding knowledge.
Browse courses on
R Markdown
Show steps
-
Identify areas for improvement or expansion
-
Research and gather relevant information
-
Write and submit your contribution
Organize Your Notes and Resources
Show steps
Compile and organize notes, assignments, and quizzes to enhance understanding and facilitate review.
Browse courses on
Note-Taking
Show steps
Show steps
Show steps
- Gather all relevant course materials
- Categorize and sort materials for easy retrieval
- Create a system for ongoing note-taking
Git Tutorial for Beginners
Show steps
Complete a guided tutorial to familiarize yourself with git, enabling effective version control.
Browse courses on
Git
Show steps
Show steps
Show steps
- 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
Show steps
Review basic Unix commands to strengthen foundational knowledge and enhance proficiency.
Browse courses on
Unix
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all seven activities
GitHub Workshop
Show steps
Attend a workshop to enhance GitHub knowledge, facilitating collaboration and project management.
Browse courses on
GitHub
Show steps
Show steps
Show steps
- Create and manage repositories on GitHub
- Collaborate on projects with other GitHub users
- Integrate GitHub with other tools and technologies
Mentor Newcomers with Git
Show steps
Share knowledge and expertise by mentoring others, reinforcing understanding and fostering community growth.
Browse courses on
Git
Show steps
Show steps
Show steps
- 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
Show steps
Create a personal website using R Markdown, combining data, text, and code for effective communication.
Browse courses on
R Markdown
Show steps
Show steps
Show steps
- Design and structure the website layout
- Incorporate R code and visualizations
- Publish the website online
Contribute to R Markdown Documentation
Show steps
Make valuable contributions to the R Markdown documentation, enhancing the community and expanding knowledge.
Browse courses on
R Markdown
Show steps
Show steps
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 Analyst
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.
Data Scientist
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.
Machine Learning Engineer
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
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
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.
Database Administrator
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.
Research Analyst
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.
Business Analyst
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.
Project Manager
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.
Product Manager
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.
Marketing Analyst
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
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.
Consultant
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.
User Experience Designer
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.
Technical Writer
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.
For more career information including salaries, visit:
OpenCourser.com/course/yojs5w/data
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.
Save
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.
Save
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.
Save
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.
Save
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.
Save
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yojs5w/data
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Rating
5.0
Effort
1 to 2 hours per week for 8 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructor
Rafael Irizarry
Language
English
Transcripts
English
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This course belongs to a
collection
called
Data Science. It's comprised of nine courses:
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