Prologue

Git is easy. Git is fun. Thanks Linus!

Introduction

During the next year you will learn the fundamentals of data science. Surviving the nine courses which make up the [Data Science Specialization][0001] offered by [Johns Hopkins University][jhu] requires a strategy.

To this end, the focus of the ten-course series including a capstone project is to provide the learner with:

1. an introduction to the key ideas behind reproducible research,
2. an introduction to the tools and techniques to transform raw data into a presentable report,
3. an opportunity to gain hands-on practice so you can learn the techniques for yourself, and
4. an appreciation of the mathematics & statistics involved in data science.

Core Courses

The courses comprising the Data Science Specialization are:

• Data Scientist's Toolbox
• R Programming
• Getting and Cleaning Data
• Exploratory Data Analysis
• Reproducible Research
• Statistical Inference
• Regression Models
• Practical Machine Learning
• Developing Data Products

Figure 1 Course dependency diagram

[0001]: https://www.coursera.org/specialization/jhudatascience/1?utm_medium= courseDescripTop [jhu]: http://www.jhu.edu