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Introduction to Julia Programming

Gain hands-on proficiency with Julia for data analytics, visualization, and introductory machine learning.

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Audience: Developers / Architects

Duration: 3 days

Format: Lectures and hands-on labs (50% lecture, 50% lab)

Overview

Julia is fast becoming a popular language of choice for scientific computing and machine learning. It boasts high performance, ease of use, and a friendly syntax. This course introduces the Julia language, tools, and programming techniques.

Objective

Gain hands-on proficiency with Julia for data analytics, visualization, and introductory machine learning.

What You Will Learn

  • Julia vs other languages
  • Julia language features
  • Julia development environment
  • Data Types / Variables / Functions
  • Reading and processing data files
  • Data visualization
  • Meta programming
  • Profiling and performance evaluation
  • Machine learning introduction

Course Details

Audience: Developers / Architects

Duration: 3 days

Format: Lectures and hands-on labs (50% lecture, 50% lab)

Prerequisites:
  • Programming experience with Python or Java

Setup: A modern laptop · Unrestricted Internet · Julia development environment (instructions provided)

Detailed Outline

  • Scientific computing ecosystem
  • Julia vs. other languages
  • Features of Julia
  • Julia development environment
  • Lab: Up and running with Julia
  • REPL environment
  • Variables and types
  • Logical and arithmetic expressions
  • Variable scope
  • Lab
  • Arrays
  • Loops
  • Control flow
  • Lab
  • Function syntax
  • Using built-in functions
  • Writing User-Defined Functions (UDF)
  • Lab
  • Dataframes introduction
  • Packages to use
  • Loading data into data frames
  • Plots available in Julia
  • Creating basic plots
  • Plot packages
  • Lab
  • Loading data files
  • Analyzing and summarizing data
  • Statistics
  • Dealing with missing values
  • Lab
  • Constructors
  • Interfaces
  • Modules
  • Lab
  • Metaprogramming concepts
  • Macros
  • Code generation
  • Lab
  • Profiling code
  • Running benchmarks
  • Best practices for performance
  • Lab
  • Packages for ML
  • Linear regression intro
  • Lab: Linear regression in Julia

Ready to Get Started?

Contact us to learn more about this course and schedule your training.