In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
About This Course
In this course you will get an introduction to the main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
This course is part of multiple programs
This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
Data Science: Foundation using R Specialization
Data Science Specialization
What You’ll Learn
➤ Set up R, R-Studio, Github and other useful tools
➤ Understand the data, problems, and tools that data analysts use
➤ Explain essential study design concepts
➤ Create a Github repository
Skills You’ll Gain
➤ Data Science
➤ Github
➤ R Programming
➤ R Studio
What you will learn from this course
Module 1 – Data Science Fundamentals
In this module, we’ll introduce and define data science and data itself. We’ll also go over some of the resources that data scientists use to get help when they’re stuck
➤ Dismantling the logical fallacy that is AI
➤ Why legitimizing AI as a field incurs great cost
➤ Ethics overview: five ways ML threatens social justice
➤ Blatantly discriminatory models
➤ The trend towards discriminatory models
Module 2 – R and RStudio
In this module, we’ll help you get up and running with both R and RStudio. Along the way, you’ll learn some basics about both and why data scientists use them.
➤ Dismantling the logical fallacy that is AI
➤ Why legitimizing AI as a field incurs great cost
➤ Ethics overview: five ways ML threatens social justice
➤ Blatantly discriminatory models
➤ The trend towards discriminatory models
Module 3 – verion Control and GitHub
During this module, you’ll learn about version control and why it’s so important to data scientists. You’ll also learn how to use Git and GitHub to manage version control in data science projects
➤ GitHub and Git
➤ Linking GitHub
➤ Projects Under Versions Control
Module 4 – R Markdown, Scientific Thinking and Big Data
During this final module, you’ll learn to use R Markdown and get an introduction to three concepts that are incredibly important to every successful data scientist: asking good questions, experimental design, and big data.
➤ Types of Data Science Questions
➤ Experimental Design
➤ Big Data