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Computer Science and Electrical Engineering Courses:

CSCE 121 - Intro to Programming Design Concepts
CSCE 222 - Discrete Structures for Computing
ECEN 248 - Intro to Digital System Design
ECEN 214 - Electrical Circuit Theory
CSCE 221 - Data Structures and Algorithms
ECEN 314 - Signals and Systems
ECEN 325 - Electronics
ECEN 350 - Computer Architecture and Design
CSCE 313 - Intro to Computer Systems

As an undergrad computer engineering student, I have yet to contribute as much as I would like to the field of Computer Science. Until recently I was not sure what industry field I would like to dive into and begin building my career upon. Last semester I attended the first inaugural datathon, a hackathon event but for data science. While I had a basic idea of what data science was thanks to some family friends that worked in that field, I did not think it was an area that I was suited for. However, at the event, I was introduced to so many new things from how to clean up and understand huge amounts of data to understanding the concept behind neural networks like Keras. As someone who is naturally curious about something that doesn't automatically click, I went to work untangling and figuring out what the massive amount of information in front of me meant. After the 24 hour event, I would like to imagine that I learned plenty about data science but in reality, I had barely begun to scratch the surface. Under the guidance of one of the mentors at the datathon, I started to learn more through online courses like Udemy and LinkedIn learning as well as reading articles published by data scientists already in the field. I am currently learning more about different neural network libraries in python such as TensorFlow as well as the above mentioned Keras. My goals and aspirations are now to learn more about this field whether it be self-taught or through an internship shortly so that I may enter the industry. There, I can learn the in's and out's of extracting meaningful data from the large pools and then following it up with building models and algorithms to deliver visual and informative findings to peers that may be in the associated field or those outside of it.