HPE DSI 212
Data Science Courses
Scientific Programming with Python
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
Before taking this course, you should be familiar with the command line terminal and running programs on it, or you should have taken the 101 Introduction to cluster computing: Linux, shell scripting, queuing systems, and Cluster Architecture course.
Description
Python is a powerful, easy-to-learn programming language widely used in scientific computing, data analysis, automation, and high-performance computing (HPC). Its clear syntax, extensive standard library, and efficient high-level data structures make it well suited for rapid application development and computational research.
This training session introduces participants to the fundamentals of Python programming and its applications in scientific and research computing. Topics covered include Python syntax, core data types, conditional statements, loops, functions, file input/output, modules, object-oriented programming with classes, and exception handling.
The course also provides hands-on exposure to commonly used scientific Python libraries and tools for data processing, visualization, and computational analysis. Participants will learn to use libraries such as NumPy for numerical computing, SciPy for scientific computing workflows, Pandas for data manipulation and analysis, Seaborn for data visualization, Scikit-learn for introductory machine learning applications, and Python regular expressions for text and pattern processing.
Upon successful completion of this training series, participants will be able to:
- Understand and modify existing scientific Python programs
- Develop basic to intermediate Python applications for research and data analysis
- Use Python scientific libraries for numerical and data-driven workflows
- Perform data analysis, manipulation, and visualization
- Apply Python tools and workflows commonly used in high-performance computing environments
This course is intended for students, researchers, and professionals seeking practical programming skills for scientific computing, data analysis, and computational research.
This course is intended for students, researchers, and professionals seeking practical programming skills for scientific computing, data analysis, and computational research. The course is also a required component of both the Micro-Credential in Data Science and the Micro-Credential in Artificial Intelligence programs. For additional information about these programs, 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.