Julia Programming Training - Hong Kong

Julia Programming Training Course Outline

Module 1: Introduction to Julia

• What is Julia Programming Language?
• Features of Julia
• Scope of Julia
• Getting Started

Module 2: Variables

• Introduction
• Allowed Variable Names
• Stylistic Conventions

Module 3: Integers and Floating-Point Numbers

• Integers
• Floating-Point Numbers
• Arbitrary Precision Arithmetic
• Numeric Literal Coefficients
• Literal Zero and One

Module 4: Mathematical Operations and Elementary Functions

• Arithmetic Operators
• Boolean Operators
• Bitwise Operators
• Updating Operators
• Vectorised ‘dot’ Operators
• Numeric Comparisons
• Operator Precedence and Associativity
• Numerical Conversions

Module 5: Complex and Rational Numbers

• Complex Numbers
• Rational Numbers

Module 6: Functions

• Argument Passing Behaviour
• Argument-Type Declarations
• return Keyword
• Operators are Functions
• Operators with Special Names
• Anonymous Functions
• Tuples
• Destructuring Assignment and Multiple Return Values
• Argument Destructuring
• Varargs Functions
• Optional and Keyword Arguments
• Evaluation Scope of Default Values
• Do-Block Syntax for Function Arguments
• Function Composition and Piping
• Dot Syntax for Vectorising Functions

Module 7: Control Flow

• Compound Expressions
• Conditional Evaluation
• Short-Circuit Evaluation
• Repeated Evaluation: Loops
• Exception Handling
• Tasks

Module 8: Methods

• Defining Methods
• Method Ambiguities
• Parametric and Redefining Methods
• Design Patterns with Parametric Methods
• Parametrically-Constrained Varargs Methods
• Function-Like Objects
• Empty Generic Functions
• Method Design and the Avoidance of Ambiguities

Module 9: Constructors

• Outer Constructor Methods
• Inner Constructor Methods
• Incomplete Initialisation
• Parametric Constructors
• Outer-Only Constructors

Module 10: Multi-Threading

• Starting Julia with Multiple Threads
• Data-Race Freedom
• @threads Macro
• Atomic Operations
• Per-Field Atomics
• Side Effects and Mutable Function Arguments
• @threadcall
• Caveats
• Safe Use of Finalisers

Module 11: Embedding Julia

• High-Level Embedding
• High-Level Embedding on Windows with Visual Studio
• Converting Types
• Calling Julia Functions
• Memory Management
• Working with Arrays
• Exceptions

Module 12: Code Loading

• Definitions
• Federation of Packages
• Environments

Module 13: Profiling

• Basic Usage
• Accumulation and Clearing
• Options for Controlling the Display of Profile Results
• Configuration
• Memory Allocation Analysis
• External Profiling

Module 14: Stack Traces

• Viewing a Stack Trace
• Extracting Useful Information
• Error Handling
• Exception Stacks and current_exceptions
• Comparison with Backtrace

Who Should Attend this Julia Programming Training?

This Julia Course is ideal for individuals looking to learn Julia, a high-performance programming language designed for numerical computing, data science, and scientific research. This course will be beneficial for the following professionals:

• Software Developers
• Data Scientists
• Data Analysts
• Machine Learning Engineers
• Researchers and Academics
• IT Professionals

Prerequisites of the Julia Programming Training

There are no formal prerequisites for this Julia Programming Training. However, a basic understanding of data analysis and application development can be beneficial.

Julia Programming Training Course Overview

The Julia Programming Training covers core concepts such as syntax, data types, and numerical computing. It introduces control flow and modular programming in a structured way. Delegates also learn data processing and visualisation using Julia libraries.

Delegates gain practical skills in writing efficient code and handling datasets. The course builds confidence in using packages and performing computations. It also supports accurate and effective data analysis in real scenarios.

This 2-Day Julia Course by The Knowledge Academy equips delegates with essential Julia skills. It enables immediate application in data science, research, and development roles. The training provides a hands-on experience with industry-relevant tools.

Julia Course Objectives

• Understand Julia syntax, data types, and core programming concepts
• Write clean and efficient code for data-driven and technical applications
• Perform data analysis and visualisation using Julia packages
• Apply control flow, functions, and modular programming effectively
• Use Julia for numerical computing and high-performance tasks
• Explore real-world applications in data science and research

After completing this Julia Course, delegates will receive a certification recognising their programming skills. This certification validates their ability to use Julia effectively and supports career growth in technical and data-focused roles.

What’s Included in this Julia Programming Training

• World-class Training Sessions from Experienced Instructors
• Julia Programming Certificate
• Digital Delegate Pack

You’ll also get access to the MyTKA Training Portal, which will be your go to hub for all your training.
Hands-On Labs: Included as part of our online instructor-led delivery, these labs provide real-world exercises in a simulated environment guided by expert instructors to enhance your practical skills.