If you don’t know what Git is and how you can link it with RStudio to use it in your projects, don’t worry, just continue reading. You will also complete a project at the end to apply and demonstrate your newly acquired skills.If you work as a Data Scientist you need to know what Git is and work with it.
Next, you will become familiar with Git workflows involving branches and pull requests (PRs) and merges. You will start with an overview of Git and GitHub, followed by creation of a GitHub account and a project repository, adding files to it, and committing your changes using the web interface. Further in the module, you will develop the essential conceptual and hands-on skills to work with Git and GitHub. While there are many distributed versioning systems, Git is amongst the most popular ones. In addition, Distributed Version Control Systems (DVCS) have become critical tools in software development and key enablers for social and collaborative coding. You will learn about the different R visualization packages and how to create visual charts using the plot function. This module will start with an introduction to R and RStudio. R is a statistical programming language and is a powerful tool for data processing and manipulation. You will demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers. Towards the end the course, you will create a final project with a Jupyter Notebook. 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. This course gives plenty of hands-on experience in order to develop skills for working with these Data Science Tools. You will understand what each tool is used for, what programming languages they can execute, their features and limitations. Work with Jupyter Notebooks, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio.
You will become familiar with the Data Scientist’s tool kit which includes: Libraries & Packages, Data Sets, Machine Learning Models, Kernels, as well as the various Open source, commercial, Big Data and Cloud-based tools. This course teaches you about the popular tools in Data Science and how to use them. In order to be successful in Data Science, you need to be skilled with using tools that Data Science professionals employ as part of their jobs.
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