From Academia and Physics to Data Science, how Magda Guglielmo upskilled to Data Science in 24 weeks

From Academia and Physics to Data Science, how Magda Guglielmo upskilled to Data Science in 24 weeks

Stay Informed With Our Weekly Newsletter

Receive crucial updates on the ever-evolving landscape of technology and innovation.

By clicking 'Sign Up', I acknowledge that my information will be used in accordance with the Institute of Data's Privacy Policy.

Within 6 months, Magda Guglielmo was able to successfully up-skill with the Institute of Data Data Science and AI Part-time program. Having a strong career background in Academia, she felt the limitations of her existing skills and looked to open up further career opportunities outside of the academic world. She has a strong passion and interest in solving complex issues through Artificial Intelligence modelling. This coupled with her naturally curious mindset helped her excel throughout the program and in her current research position with the University of Sydney.

Magda gained valuable practical and interpersonal training from the Institute of Data’s professional lead trainers and dedicated Job Outcomes team.

Learn about her career journey:

Can you describe your career and industry experience going from physics to data science & AI? 

“I have a background in Physics. My degree is from “Federico II” – the University of Naples. I got my PhD in Astronomy from the University of Sydney and I have worked at the university ever since. During my time at the University of Sydney after my PhD, I covered different roles from Researcher (post-doctoral research fellowship) to technical support and teaching staff (including unit coordination and teaching material creation).”

What were your career goals before upskilling in data science? How has your outlook changed?

“What I love about being a researcher is the challenge, and the thrill you get when solving a problem. Everything starts with a question; the answer is hidden in the data, and the models you build. In the beginning, I thought that only research was giving me this excitement. Universities are great places to grow your skills through collaborations and compelling challenges. Therefore, I wanted to pursue a career in Academia.

I came to the realisation that working within companies can offer everything I am looking for. I do not want to stop once the surface is scratched, I want to produce and make something that others can use not just for pure academic interest. This awareness expanded my horizons.”   

Why did you choose to join the Part-Time Program in particular? What appealed to you?

“As a researcher, I always used tools related to my research. I wanted to learn something that was instead needed in the real world. I started to look for a course that could help me to refine my existing skills and acquire the confidence I needed to move towards a more company orientated position. The Part-Time program Institute of Data offered was perfect for me, as it was offering everything I was looking for. The fact it was part-time helped a lot in balancing my work, my family and my interest.”

Can you describe when you knew that switching from Physics to Data Science was the right career path for you?

“I am moved by a genuine interest in the field. However, before attending the course at the Institute of Data, I had only foggy ideas about data science. This course offers an insight into what “data science” really is. It helped me understand which skills I need to transition into the field and what I need to improve on.”

Your Capstone Presentation also achieved top results. How did you prepare for this presentation?

“I shaped my presentation to convey my interest and knowledge in the topic, tailoring the slides to the target audience: are they data scientists? Are they clients? The audience must always be in your mind as it helps you choose the content of the slides. If they are clients, they don’t care about code attempts or data cleaning techniques. You can go directly to the point: what the results are? How much do you save? Tailored attention to detail is also important. Owing to my academic background, I make sure that the visual content is suitable to the audience: Make sure that your plots are readable and easy to understand, and if you can, make them pretty. 

Of course, duration is important. Never make it too long, but make sure that everyone can follow. For example, one or two slides for the project background is enough. Keep that part short and focus on what you did.”

How did you determine the focus of your final Capstone project during the course? What did it teach you and how did you find the process?

“My final Capstone project focused on a movie recommendation system based on a neural network. I had never written anything resembling a neural network before, and I can say it was challenging. Overall, it was a good experience. I have learned new Python packages, and I have brought my exploratory data analysis skills to a new level.”

What are some tips you would give someone preparing for their Capstone project during the data science & AI program?

“Choosing a Capstone project is not easy. My suggestion is to start with something you are really passionate about. This will help you understand the data, their quality and define the context of your project during the presentation. Something that really helped me was to keep in mind the project aim: write your problem statement or questions down to not lose focus.  Finally, remember that you are wearing a data scientist hat; everything you do must have a business value.”

Now that you’re trained with in-demand data science skills, what’s your future career plan? 

“After years at university, I envision myself in a more commercial environment where I can transfer my skills and apply state of the art model techniques. I keep track of the tools and techniques that are popular right now. I want to improve my skills in Tableau (it is a must), and I am learning PowerBI. From a modelling point of view, I am fascinated by convolutional neural networks. I play a bit with those in my spare time. Next on my list is cloud computing.”

Tell us about your current job role as an Associate Researcher! What does your day to day involve? What tools, techniques or processes do you use currently?

“As a researcher, I don’t have a fixed daily schedule as some tasks require more time than others. My current role involves data sourcing, modelling and model assessments. Often in a cycle, I obtain the data, run the model, compare the given results with other experiments, and start over if the quality is not high enough.  Each of these stages requires coding, statistical analysis and visualisation (the fun part). For data sourcing, I used different techniques depending on how the data is distributed. Most of the time, I just write an API to the database – automatically retrieve all the data I need. I then perform EDA to assess data quality. The final stage, model assessment, requires more statistical tools (e.g. correlation analysis, time series analysis, etc.)”

Would you recommend upskilling to fellow professionals? Who would be suited to become trained in data in your opinion?

“In any field, it is essential to stay up to date with the latest technologies and tools. I think that upskilling is crucial for professionals. Learning new skills not only improves your chance to lift your career, but it also can make your working day much more manageable. I think that everyone with a curious mind can become a data scientist. In my opinion, one of the most beautiful things that successful data scientists have is their curiosity (or rather their ability to question the data at any stage). The coding and math skills can come with perseverance.”

In your opinion, how did the program change your perspective on what is required to be a modern data professional?

“I like the program because it is taught by professional data scientists that share their experience and know-how. Their perspective on data science let me realise that there are more skills involved than just coding. I’ve never focused on how vital soft skills are in this field, for example, effective communication. Before the data, a data scientist meets the clients. Knowing how to interact with them is an essential skill that helps you gain more insight into the problem, timeline, etc. During the program, you will have a lot of support and practice in this direction.”

What advice would you give a professional that wants to kick-start their career in an industry that’s new to them?

“Build upon your strengths. Focus on the value that your experience is bringing to the industry.”

Connect with Magda on LinkedIn here.

If you are interested in up-skilling and transitioning your career to the Data Science field, schedule a consultation with a Data Industry Career Consultant today.

Share This

Copy Link to Clipboard