Get an overview of the data, questions, and tools that data analysts and data scientists work with. This is the first course in the Johns Hopkins Data Science Specialization.
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.
Upon completion of this course you will be able to identify and classify data science problems. You will also have created your Github account, created your first repository, and pushed your first markdown file to your account.
No prior backround required. Previous experience in programming is helpful.
This course consists of weekly video lectures, weekly quizzes, and a final peer-assessed project.
How do the courses in the Data Science Specialization depend on each other?
We have created a handy course dependency chart to help you see how the nine courses in the specialization depend on each other.
Will I get a Statement of Accomplishment after completing this class?
Free statements of accomplishment are not offered in this course. If you are not enrolled in Signature Track, participation and performance documentation will be reported on your Accomplishments page, but you will not receive a signed statement of accomplishment.
What resources will I need for this class?
For this course, all you need is an Internet connection and access to Github
How does this course fit into the Data Science Specialization?
This is the first course in the sequence. We recommend that you take this course before moving on to R Programming or any of the other courses in the specialization.
Russian subtitles for this course are courtesy of IBS.