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Introduction to Julia Programming
Course Description
Overview
Julia (https://julialang.org/) is fast becoming a popular language of choice for scientific computing and machine learning. It boasts high performance, ease of use and easy to learn syntax.This course introduces Julia language, tools and programming.
Objectives
- 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
Audience
Prerequisites
- Programming experience with Python or Java
Topics
- 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
Self-Paced Training Info
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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