Course Summary
Learn how to use open-source tools for data science and gain practical experience with a variety of data science tasks. In this course, you will learn how to use open-source tools for data science, including R, Git, and Unix, and how to use them in conjunction with each other to build a complete data science workflow.Key Learning Points
- Gain practical experience with data science tasks using open-source tools such as R, Git, and Unix
- Learn how to build a complete data science workflow using open-source tools
- Discover the power of collaboration through version control with Git
Related Topics for further study
Learning Outcomes
- Develop a complete data science workflow using open-source tools
- Learn how to use Git for version control and collaboration
- Gain practical experience with data science tasks using R programming
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge
- Familiarity with command-line interfaces
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-Paced
- Video Lectures
Similar Courses
- Data Science Essentials
- Applied Data Science with Python
- Applied Machine Learning
Related Education Paths
Notable People in This Field
- Hadley Wickham
- Hilary Parker
Related Books
Description
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Skills Network Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
Outline
- Data Scientist's Toolkit
- Course Introduction
- Languages of Data Science
- Introduction to Python
- Introduction to R Language
- Introduction to SQL
- Other Languages
- Categories of Data Science Tools
- Open Source Tools for Data Science - Part 1
- Open Source Tools for Data Science - Part 2
- Commercial Tools for Data Science
- Cloud Based Tools for Data Science
- Libraries for Data Science
- Application Programming Interfaces (API)
- Data Sets - Powering Data Science
- Sharing Enterprise Data - Data Asset eXchange
- Machine Learning Models
- The Model Asset Exchange
- Practice Quiz - Languages
- Practice Quiz - Tools
- Practice Quiz - Packages, APIs, Data Sets, Models
- Graded Quiz
- Open Source Tools
- Introduction to Jupyter Notebook
- Getting Started with Jupyter
- Jupyter Kernels
- Jupyter Architecture
- Introduction to R and RStudio
- Plotting within RStudio
- Overview of Git/GitHub
- GitHub - Getting Started
- GitHub - Working with Branches
- Git and GitHub via command line (Optional)
- Branching and merging via command line (Optional)
- Contributing to repositories via pull request (Optional)
- Practice Quiz - Jupyter Notebook
- Practice Quiz - RStudio IDE
- Practice Quiz - GitHub
- Graded Quiz
- IBM Tools for Data ScienceÂ
- What is IBM Watson Studio?
- Watson Studio Introduction
- Creating an Account on IBM Watson Studio
- Jupyter Notebook in Watson Studio - Part 1
- Jupyter Notebook in Watson Studio - Part 2
- Linking GitHub to Watson Studio
- Other IBM Tools for Data Science
- IBM Watson Knowledge Catalog
- Data Refinery
- SPSS Modeler Flows in Watson Studio
- IBM SPSS Modeler
- SPSS Statistics
- Model Deployment with Watson Machine Learning
- Auto AI in Watson Studio
- IBM Watson OpenScale
- Practice Quiz - Watson Studio
- Practice Quiz - Other IBM Tools
- Graded Quiz
- Final Assignment: Create and Share Your Jupyter Notebook
- IBM Digital Badge
- Final Exam
Summary of User Reviews
Discover the best open-source tools for data science with this Coursera course. Students rate this course highly for its comprehensive and practical approach to learning. Many users found the hands-on projects to be particularly helpful.Key Aspect Users Liked About This Course
Hands-on projectsPros from User Reviews
- Comprehensive and practical approach to learning
- Great introduction to open-source tools for data science
- Instructors are knowledgeable and engaging
- Course materials are well-organized and easy to follow
- Good balance of theory and practice
Cons from User Reviews
- Some users found the course to be too basic
- Course content may not be relevant to all students' needs
- Some technical difficulties reported with the programming assignments
- The course could benefit from more interactive features
- Limited opportunities for peer interaction and feedback
Aije Egwaikhide
4.5 Raiting
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