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Julia for Data Science [Video]

This is the code repository for Julia for Data Science [Video]. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

Julia is an easy, fast, open source language that if written well performs nearly as well as low-level languages such as C and FORTRAN. Its design is a dance between specialization and abstraction, providing high machine performance without the sacrifice of human convenience. Julia is a fresh approach to technical computing, combining expertise from diverse fields of computational and computer science.

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.

What You Will Learn

  • 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
  • Apply different algorithms from decision trees and other packages to extract meaningful information from the iris dataset
  • 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

Instructions and Navigation

Assumed Knowledge

To fully benefit from the coverage included in this course, you will need:
NA

Technical Requirements

This course has the following software requirements:
NA

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