Chris Mahoney, who graduated from the Master of Data Science and Innovation (MDSI) course in 2022, shares how this degree prepared him for his current role as a Data Scientist at DB Schenker.
TD School / MDSI Alumni, Chris Mahoney. Photo supplied.
Unifying technical and business skills
The Master of Data Science and Innovation (MDSI) at UTS was an easy choice for Chris because the course goes beyond the requisite technical expertise (in both coding and mathematics), and focuses on translating intricate algorithms into actionable insights for businesses. Chris said his ability to articulate the practical applications, anticipated outcomes, and business benefits of data science models is pivotal to his success, especially in a non-technical work environment.
Building a robust skill set
Chris recounts a pivotal moment from the Advanced Data Science for Innovation subject (now called Advanced Machine Learning Application), where students were tasked with constructing, training, and deploying a machine learning model.
We had to decide which model to use, how to train it, how and where to deploy it, how the network integration would work, how the API endpoints would behave, how the model would work in a 'production-like' environment, and how the user would interact with it. Understanding the details and nuances of the end-to-end data science process was a steep learning curve. These skills have proved extremely beneficial for my career.
– Chris Mahoney, MDSI graduate
The value of work placements
Chris participated in two Innovation Lab subjects, where he collaborated with his current employer, to apply his skills to business-critical data. From developing advanced forecasting models to creating a Warehouse Digital Twin – these hands-on initiatives continue to prove the value of data science projects for the organisation and have showcased Chris’ adaptability and innovative mindset.
Advice for current students
Transitioning to the workforce, Chris learned that the reality is that data is rarely ever clean in the workplace. He advises students to “focus on the Exploratory Data Analysis (EDA) of your data first. This is where you can explore the data in as much detail and in as many dimensions as possible. Once you intimately understand your data and any issues it may contain – only then should you begin implementing some of the advanced machine learning models.”
I found working with and learning from students from varying backgrounds such as Medicine, Law, Engineering, and more, immensely beneficial. This degree is not just for IT, Maths or Science students. It’s open to everyone. The transdisciplinary nature of the course is what really distinguishes this degree. For me, this is what made my studies such an enjoyable and enriching experience.
Closing thoughts
Summing up his journey, Chris says, “It’s a combination of the emphasis on real-world application, diverse learning experiences, and practical projects that have provided me with the skills and mindset needed to thrive in a career I love.”
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