Objective: This course provides a comprehensive introduction to Julia, a high-performance programming language ideal for technical computing. Students will learn Julia's syntax, data handling, and computational capabilities through hands-on coding exercises and applications in data science, machine learning, and numerical analysis.
Methods: Interactive lectures, coding exercises, and projects that focus on building proficiency in Julia's unique capabilities, such as its speed and ease for mathematical and scientific computing.
Skills Gained:
The modules listed below are those currently intended for delivery in the current academic intake of this course. These may be subject to change in future years as the University regularly revises.
Topics: Overview of the Julia environment, syntax basics, and REPL usage, exploring Julia’s data types, logical operators, scope rules.
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Topics: DataFrames.jl package, data manipulation, and basic data cleaning, visualization techniques, linear algebra applications.Descriptive statistics, data grouping, and processing techniques.
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Topics: Profiling tools, optimizing functions, and reducing computation time, using PyCall and RCall packages, exploring Julia’s applications in various industries.
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By the end of this course, students will be able to:
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Detailed study for foundational learning and development in coding.
Virtual labs, simulations, and real-world case studies.
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Foundation for advanced studies and careers in coding.
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