I am currently pursuing a degree in Information Science with a Concentration in Data Science and Analytics. This field aligns with both my personal and career aspirations because it requires both structured problem-solving and creativity, two skills I have cultivated through both STEM and arts courses. Throughout my academic journey, I’ve struggled to pinpoint exactly what I want to do within this industry. At different points, I was convinced I wanted to pursue information security and disaster preparedness, only to make a sudden pivot to coding and data analytics. From utterly despising the idea of artificial intelligence (AI) to later find myself advocating for machine learning solutions in social science and criminology projects I’ve worked on. Thinking, “I could never be an engineer”, to actively pursuing the calculus I need to possibly give myself that future. While I wish I had known my exact path from the start, what I do know is that I see a future for myself in this field.

With my degree, I hope to pursue a career as either a data scientist or a machine learning researcher. I love working with data to derive actionable insights and solutions, and I am particularly fascinated by how machine learning models can refine and enhance data analysis and decision-making. In this essay, I aim to explore the distinctions between data science and machine learning, explain my relevant experience and several potential career applications, before outlining what steps I have taken with USF to achieve these goals.

Data science and machine learning are closely related fields, but they serve distinct purposes within the data industry. Data science is an interdisciplinary field that focuses on extracting insights from data using a combination of statistics, programming, and domain expertise; encompassing topics like data mining, visualization, analytics, and predictive modeling (Mathur, 2023). Machine learning, on the other hand, is a subset of AI that enables models to recognize patterns, learn from data, and improve performance over time without being explicitly programmed. While data science often uses machine learning as a tool to enhance analysis, machine learning itself is primarily focused on building and optimizing models that can interpret more volumes of data than any human ever could in their lifetime (Mathur, 2023). Based on my research into the difference between these two fields, I have come to the realization that my primary interest lies in data science rather than machine learning engineering. I do enjoy applying machine learning models as part of my data science process, and will continue using them often and enthusiastically, but I’m not sure I want to focus solely on their development and optimization like Machine Learning engineers primarily do.

My hands-on experience in both data science and machine learning has reinforced both my interest in these fields, but also my desire to pursue data science over machine learning. As an RA Machine Learning Lab Tech, I have actively assisted professors in implementing machine learning solutions to approach a variety of research problems. Several projects have involved BERT topic modeling to analyze social media responses to significant events, such as reactions to the Tucker Carlson-Putin interview and an ongoing study examining the social media discourse surrounding the Brian Thompson assassination. I am also currently contributing to an AI chatbot project designed to help senior citizens improve their cybersecurity awareness that uses LLM technology and Retrieval Augmented Generation (RAG). Beyond academic research, I have also gained experience through backend querying and data analysis for businesses, including a business analytics company and a vacation rental company. While these roles initially started as volunteer or minimally compensated positions, they provided me with valuable industry exposure and practical experience in data processing, analytics, and machine learning implementation. My experiences with both Machine learning/AI dominant work and Data science/analysis dominant work has shown me I love working with people who know how to optimize these models more than optimizing them myself.

As I continue to refine my career goals, I hope to find roles that combine data science, machine learning, and domain-specific problem-solving, such as improving search and recommendation algorithms, enhancing digital library systems, or improvement strategy development within lower stakes industries, like entertainment or media. Part of the reason I moved away from the information security field was my stress over the high stakes environment it exists in, and my lack of confidence in handling critical information systems like that. I think I would feel more comfortable pursing a career in industries were people’s lives weren’t depending on my analyses or insights (like insurance or fintech).

Platforms recommended to me by my professors, like Datacamp, have been vital in helping me map out relevant skills and certifications, ensuring I build a well-rounded portfolio and develop the needed skills to excel in the data industry. I am particularly a big fan of the career tracks offered on Datacamp, which allow you to follow modular skill builders in a linear fashion, such as the “Data Scientist in Python Track” (Datacamp, n.d).  Before this assignment, I had not visited the USF Career Services website but exploring it for the first time gave me a better understanding of the resources available to students. I found it particularly helpful that it had different sections depending on whether you are looking for jobs, internships, or ways to gain experience. Similarly, I have not yet attended any Career Services events, mostly because my BSIS classes were all primarily online; however, I plan to attend the upcoming engineering and computing fair on February 12^th as I am currently commuting to campus this semester for my Calculus class. I am particularly excited about this experience as one of my LinkedIn connections will be there, and she reached out to me about meeting in person sometime that day which I am definitely looking forward to.

In conclusion, I’m not entirely sure yet how I plan to break into the data science industry, but I’m actively exploring my options. While I know what sort of topics interest me in data science, and that I am concerned about ethical data use, so I want to avoid industries that are notoriously exploitive or high stakes (Medical insurance, generative AI), I’m still figuring out the best path forward. I hope that attending the career fair on February 12 will give me more insight into industry opportunities and help me better understand the steps I need to take to start my career, as well as career titles I can search for to find jobs that relate to my areas of interest. I think I want to start by looking at industries such as the music industry, entertainment, and/or gaming and expand from there into museums, libraries, and archives, before moving into traditional tech and business industries. The process of researching data science throughout the course of this essay has definitely helped settle my indecisiveness regarding my preferred path forward in the industry, and I look forward to exploring my options in the weeks and months ahead!

Works Cited 

Datacamp. N,d. Data Scientist in Python. Datacamp. https://app.datacamp.com/learn/career-tracks/data-scientist-in-python

Mathur, G. (2016, July 7). Data science vs. machine learning: What’s the difference? IBM. https://www.ibm.com/think/topics/data-science-vs-machine-learning