Student Goals

When asked about their area of concentration, students responded:

  • Aerospace Engineering - 15

  • Computational Engineering - 13

  • ML / Data Science - 3

  • Mechanical Engineering - 2

  • Biomedical / Medicine - 2

  • Mathematics - 2

  • Sports Analytics - 1

  • Software - 1

  • Cloud - 1

  • Space Physics - 1

  • Fluids - 1

When asked what their goals were for the course, students responded (many of these were edited for typos and readability):

Programming

  • Get more experience with programming larger projects

  • Build software skills and become conversational with my CS friends who use terminology I don’t always understand

  • Further my understanding of programming

  • Learn about more real-world applications of coding and how they are applied

  • Improve my programming skills

  • Work on better user-interface programs

  • Become more comfortable working with software and back-end programming

Software Engineering

  • Learn skills related to virtualization as well as DevOps (containerization, continuous integration)

  • Gain a more applicable understanding of software engineering

  • Learn and gain experience on creating software systems and technologies

  • Learn advanced computing concept in software engineering

  • Get a better understanding of software engineering principles

  • Get familiar with software engineering and design

  • Learn what software engineering actually entails

Technologies / Applications

  • Learn how to develop a program onto a web application

  • Reinforce cloud foundation

  • Learn more about distributed systems

  • Learn how to use different tools in practical projects

  • Containers and Virtualization

  • Understanding cloud computing components relevant to machine learning ops, e.g., docker

  • Have a better understanding of how computers, clouds, and software work

  • Get more comfortable using software frameworks and understanding the components

  • Get more comfortable with docker containers

  • Learn the tools to apply and share the theoretical algorithms I know, or I will learn

  • Learning api’s and user interface so I can use it for my lra club

  • Become more familiar with GitHub and PowerShell

  • Become better at solving real world problems that have a purpose using computer programming

  • Get more comfortable using Linux

  • Getting more comfortable with virtualization and containers for code deployment

Professional Development

  • Build a larger project portfolio

  • Create some projects under my belt to showcase (plus gain the ability to make more projects)

  • Learn databases and algorithms enough to start interview prep for related job roles

  • Mitigate my use for AI in code

  • Learn other fundamental skills for industry work for MSCS

  • Build my portfolio with code that reflects both my proficiency in computer programming and my passion for aerospace-related subjects

  • Be able to create my own projects/softwares start to finish

  • Create exciting projects to showcase on Github for companies and recruiters

  • Incorporate essential topics that show up consistently in job interviews

  • Learn software tools/skills for my career

  • Get a good grade in this class

  • Learn a lot and have fun

  • Create and undergo personal projects and create creative ideas

  • Apply what I learn here for my career and hopefully in the aerospace industry

  • Have a very strong project to put on my resume

  • Create a portfolio showcasing the work I have done to show employers.

  • Learn enough to actually apply to the industry

  • Learn skills and have portfolio projects that I can use to get an internship

  • Start working with and understanding real world projects

  • Have some things in Git to show to employers

  • Build a cool project

Data Science / Research

  • Learn to work with complex datasets and turn them into meaningful, creative projects

  • Build full-scale simulations and predictive models from large data sets (csv’s/JSON’s)

  • Learn more about data analysis/exploration

  • Understanding supercomputing clusters better