Jupyter is a novel way to combine documentation with live code, which might run on powerful distributed systems like Apache Spark, Flink and Scalding. The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. This open source project supports interactive data science and scientific computing with over 40 programming languages. Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. Although Jupyter has its roots in big data, Jupyter is generally useful for all computing needs. This lecture starts with instructions for installing Jupyter on Mac and Ubuntu. This lecture then demonstrates how to install and work with the Jupyter-Scala kernel, so students can use Scala with the Jupyter Notebook as well as the more traditional console REPL. The information provided is also largely applicable to JupyterHub, multi-user server for Jupyter notebooks.
This course aims to cover the fundamentals of Python programming through real world examples, followed by a touch on Data Science. Python programming basics such as variables, data types, if statements, loops, functions, module, object and classes are very important and this course will try to teach these with a Console Calculator project. This course is based on my ebooks at SVBook.
The "Jupyter-Scala" lecture is not free and you are not enrolled in the course, or you are not logged in. Information about the lecture follows. Click on the About the Course and Course Outline tabs above to learn more about the course. You can enroll by clicking on the button on the upper right of this page. Jupyter is a novel way to combine documentation with live code, which might run on powerful distributed systems like Apache Spark, Flink and Scalding.