The Typical Day of a Data Scientist
Considering a career in data science? At its core, data science is an intricate mix of analytics, mathematics, systems, and algorithms. Data scientists specialise in uncovering consumer insights, making sense of complex behaviours, and identifying trends – all in the name of turning data sets into valuable business assets. There are many different types of data scientists working in consultancies, agencies and in-house roles across a wide range of industries.
But what do data scientists actually do?
To find out, we interviewed Seun Aremu, a data scientist who has been researching and working in the field for over 10 years. He previously specialised as a Perceptions Systems Research Engineer and is currently developing a machine learning framework dedicated to structuring asset data. Here’s what he had to tell us about the typical day of a data scientist and the industry in general.
What area of data science do you specialise in?
A lot of my work focuses on creating data footprints and digital representations of systems. Since my background is in mechanical engineering, I try and give mechanical systems a digital representation so I can take that data and make it useful. Predictive analytics and predictive maintenance is a big part of what I do. I work with lots of different systems, such as mining systems, turbo engines and manufacturing equipment.
How do you typically start your day?
I typically start my day by reading research papers to see what’s been done recently, or if anything new has happened. I try and read at least one paper a day. Then I’ll proceed with any work I was doing the day before.
If I’m starting new work, I’ll brainstorm and draw lots of diagrams before I even start coding or putting pen to paper. I do this to look at the technical background of what I’m working on so I can start mapping out a process for a solution.
What are some examples of your day-to-day tasks?
If I’m doing anything that involves coding at that time, I’ll usually get right into coding. I also do a lot of data analytics. I like to spend time just looking at the statistical property of the data I’m working on to try and gain some new insights I didn’t think of prior.
What do you like most about working with data?
There’s always something new you can learn from data. The cool thing about data is that you can give the same data set to 100 different people and I guarantee everyone will find some new way to use the data, or some new information they derive from the data. You could even use the same model and everyone will still find a way to elevate the solution or give you a different perspective.
What are some of the more challenging aspects of data science?
You have to be comfortable with the unknown when working with data. You have to trust your skills and trust your research because you’re going into the deep end without knowing where the bottom is.
One of the biggest hurdles I’ve faced in my research is showing that not all data is equal. So you shouldn’t use the same generic model or process for a machine that you would use for an image data set. I’m trying to reinforce that narrative so data philosophies can be adapted.
What’s a common misconception about data scientists?
That we’re all great coders. Another common misconception is that data models can solve everything. I think that’s something we definitely need to work on – making sure there’s a solid engineering and statistical foundation to allow us to do the work we should be doing.
If you think you’d be suited to a career in data science, consider enrolling in Monash Online’s Graduate Diploma of Data Science. Designed to enhance the creativity behind your data work, this course will help you develop a strong foundation of skills and knowledge.
Discover the exciting career and study opportunities on offer in the field of data science with Monash Online today.