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Welcome to the Data Scientist's Toolbox!
Welcome to Week 1 of the Data Scientist's Toolbox! This course is an introduction to the tools and ideas that you will see throughout the rest of the Data Science track.

We believe that the key word in Data Science is "science". Our course track is focused on providing you with three things: (1) an introduction to the key ideas behind working with data in a scientific way that will produce new and reproducible insight, (2) an introduction to the tools that will allow you to execute on a data analytic strategy, from raw data in a database to a completed report with interactive graphics, and (3) on giving you plenty of hands on practice so you can learn the techniques for yourself.

This course will focus primarily on getting you set up with the appropriate tools and accounts you will need for the rest of the track and on giving you a solid grounding in the key conceptual ideas. If you feel like the material is basic, that is ok, you will see much more in depth treatment of each topic in the subsequent courses in the track.

We are excited about the opportunity to attempt to scale Data Science education. We intend for the courses to be self contained, fast paced, and interactive. We intend to run them frequently to give people with busy schedules the opportunity to work on material at their own pace.

One important note is that as part of this class you will be required to set up a Github account. Github is a tool for collaborative code sharing and editing. During this course and other courses in the track you will be submitting links to files you publicly place in your Github account as part of your Course Projects. If you are concerned about preserving your anonymity you should set up an anonymous Github account and be careful not to include any information you do not want made available to peer evaluators.

Please see the course syllabus for information about the quizzes, the Course Project, due dates, and grading. Don't forget to say hi on the message boards. The community developed around these courses is one of the best places to learn and the best things about taking a MOOC!

Jeff Leek and the Data Science Track Team