Refine your data science skills with the heavy armory of tools provided by Julia
This video course walks you through all the steps involved in applying the Julia ecosystem to your own data science projects. We start with the basics and show you how to design and implement some of the general purpose features of Julia. Is fast development and fast execution possible at the same time? Julia provides the best of both worlds with its wide range of types, and our course covers this in depth. You will have organized and readable code by the end of the course by learning how to write Lisp style macros and modules.
The course demonstrates the power of the DataFrames package to manage, organize, and analyze data. It enables you to work with data from various sources, perform statistical calculations on them, and visualize their relationships in different kinds of plots through live demonstrations.
Julia for Data Science takes you from zero to hero, leaving you with the know-how required to apply
Who this course is for
This course is the perfect fit for data science practitioners looking to contribute to the development of this new, fast, technical programming language. The course assumes basic knowledge of high-level dynamic languages such as MATLAB, R, Python, and Ruby.
What you will learn from this course
- Get to grips with the basic data structures in Julia and learn about different development environments
- Organize your code by writing Lisp-style macros and using modules
- Manage, analyze, and work in depth with statistical data sets using the powerful DataFrames package
- Perform statistical computations on data from different sources and visualize those using plotting packages
- Apply different algorithms from decision trees and other packages to extract meaningful information from the iris dataset
- Gain some valuable insights into interfacing Julia with an R application