HPE DSI 221
Data Science Courses
Julia for Data Science
Credit: One digital badge, which can be used towards an appropriate HPE DSI micro-credential program
Lecture Contact Hours: 3 hours per week over five weeks (15 hours total)
Format: Synchronous online meetings on MS Teams
Prerequisites
There are no prerequisites for this course.
Description
This course provides an introduction to the Julia programming language for scientific computing, data science, machine learning, and high-performance computing (HPC) applications. Julia is a modern, high-level, open-source programming language designed to combine ease of use with high computational performance. It offers the simplicity and expressive syntax commonly associated with languages such as Python, R, MATLAB, SAS, and Stata, while delivering execution speeds comparable to lower-level compiled languages such as C, C++, and Java.
The course introduces participants to the core features of Julia and demonstrates how the language can be used to develop efficient, scalable, and high-performance computational workflows. Through hands-on exercises and practical examples, participants will learn how to write, execute, and optimize Julia programs for scientific and data-driven applications.
Topics covered include Julia syntax and language fundamentals, variables and data types, functions, control structures, arrays and matrices, multiple dispatch, package management, file input/output, and performance-oriented programming techniques. Participants will also explore Julia’s ecosystem for numerical computing, data analysis, visualization, and machine learning.
In addition, the course introduces Julia’s built-in support for parallel and distributed computing. Participants will learn foundational concepts for scaling Julia applications across multicore processors and distributed computing environments with minimal code modification. The course also highlights Julia’s suitability for modern HPC workflows and large-scale computational research applications.
Upon successful completion of the course, participants will be able to:
- Understand the fundamentals of the Julia programming language
- Write and execute Julia programs for scientific and technical applications
- Utilize Julia data structures and numerical computing capabilities
- Apply Julia packages for data analysis, visualization, and computational workflows
- Develop efficient and high-performance Julia applications
- Use Julia’s built-in parallel and distributed computing features
- Evaluate Julia as a platform for scalable scientific computing and machine learning applications
This course is intended for students, researchers, engineers, and professionals interested in scientific visualization, exploratory data analysis, engineering simulations, and interactive data analytics workflows. The course is also a required component of the Micro-Credential in Data Science. For additional information about these program, please contact HPE DSI at contact@hpedsi.uh.edu.
Mandatory Policies
Attendance: Regular class attendance, participation, and engagement in coursework are important contributors to student success. Grades (Pass/Fail) will not be assigned to students who fail to attend at least 12 hours of synchronous instruction. Absences may be excused as provided in the Excused Absence Policies included in the current UH Course Catalog.
Recording of Class: Students may not record all or part of class, livestream all or part of class, or make/distribute screen captures, without advanced written consent of the instructor. Classes may be recorded by the instructor. Students may use instructor’s recordings for their own studying and note-taking. Instructor’s recordings are not authorized to be shared with anyone without the prior written approval of the instructor.