There are currently twelve courses that are offered in the Chromebook Data Science Curriculum.

Class Course Page Links
How to Use A Chromebook Intro
Google and the Cloud Google
File Organization and Projects File Org
GitHub GitHub
Introduction to R R
Data and Getting Data Data
Data Cleaning and Wrangling Data Wrangling
Data Visualization Data Vis
Basics of Data Analysis Data Analysis
Machine Learning Machine Learning
Data Products Data Products
Getting a Job in Data Science Data Science Jobs

Each course is sequential and requires absolutely no prior coding experience. If you know how to use an smartphone you’re more than qualified to take this course.

Here’s a more extensive breakdown of each course.


How To Use A Chromebook

This course will introduce you to using a Chromebook. The Introduction and Setup course might sound simple, but it will set up the infrastructure for success with the later, more challenging courses.


Google and the Cloud

The Google and the Cloud course introduces using Google’s in-built apps, which form the fundamental backbone of a Chromebook. We’ll go step by step through the process to integrating these apps together to form your productivity workflow.


File Organization and Projects

Projects are central to the role of any data scientist. These lessons will discuss how to organize projects and the files that are part of each project and will introduce you to Markdown, a simple way to compile text documents to a standard format.


GitHub

Github is the world’s most popular version control website. With GitHub and Markdown, they provide a powerful way for you to get your code out to the world. In this course, we will tour GitHub, discussing the basic features of the website, what a repository is, and how to work with repositories on GitHub.


Introduction to R

R is a simple to learn programming language that is powerful for data analysis. The R Basics course will teach you how to get started from ground zero. We will discuss what objects and packages are, introduce some basic R commands, and discuss RMarkdown, which you will use to write all your reports and to develop a personal website.


Data and Getting Data

Data is often misunderstood in both subject and application. The Data course will focus on understanding what data is, what the data you’ll encounter will look like, and how to analyze and use data. Additionally, we’ll start to discuss important ethical and legal considerations when working in data science, where to find data, and how to work with these data in RStudio.


Data Cleaning and Wrangling

This course will delve deeper into working with data. Much of what data scientists do involves getting data into a usable format and cleaning the data to make sure that analyses can be done properly. We’ll


Data Visualization

This course will cover the different types of visualation most commonly used by data scientists as well as how to make these different plots in R. We will cover how to make basic tables and figures as well as how to make interactive graphics.


Basics of Data Analysis

This course will discuss the various types of data analysis, what to consider when carrying out an analysis, and how to approach a data analysis project.


Machine Learning

Data Scientists are often asked to use data to make predictions. To learn this, we’ll discuss basic machine learning approaches and walk through how to carry these out in R.


Data Products

Sharing your results with others and visualizing them in a meaningful way is an important skill for a data sciensist to have. This course will delve deeply into the various ways that one can present their data and share their results in a visually appealing way.


Getting a Job in Data Science

After you learn all of these skills, it is still crucial that you learn the best ways to network and get a job in data science. This course will focus on so-called soft skills on how to give presentations, how to present yourself in the online community, how to network, and how to do data science interviews.