In recent years, enterprise technology has grown and developed enough to allow companies to fully industrialize data processing activities. The exponential growth of data generation inevitably leads us to think differently about how data is leveraged. From this boom, a new field has emerged: the data scientist. In this article, we take a deep dive into this profession. What does a day in the life of a data scientist look like? What does the job entail? And even more specifically, what value does it add to supply chain management?
To answer these questions, we spoke with Dr. Diadie Sow, a leading Data Scientist at QAD DynaSys. There are no doubts as to his academic qualifications. Diadie holds a Master degree of Applied Math with a concentration in Optimization and Game Theory from the Ecole Polytechnique X. He also has completed a second Master’s degree specializing in Optimization and Operations Research from The Sorbonne. He concluded his studies with a PhD in Computer Engineering at Montpellier University.
How did you get into Data Science considering this type of job has only really existed in recent years?
It’s true that while data science is still a young discipline, it is one that has grown exponentially across many industries with the increase in available data volumes. It is a very exciting area and is highly applicable in everyday life.
From a personal standpoint, I have always been a lover of math and its applications. I first became interested in data science during my second year of doctoral work. Through this research, I learned about the “Neural Networks Theory”. From there, my love for this specific discipline was born.
With a compulsory number of introductory course hours to be completed each year, I was perusing the course catalog and was immediately struck by the “Introduction to Data Science” class. From there, I was introduced to other interesting courses like “Machine Learning” taught by visiting Stanford professor, Andrew Ng, an expert in his field. This course was extremely interesting and provided me with the background information necessary to go further in-depth. I then started to solve real-life problems on Kaggle.com, which is a website where industries upload real, challenge datasets with the opportunity to earn money for the top performers. From there on, data science became a real passion for me, and I began spending night after night on Kaggle participating in challenges.
What would you say at a BBQ, for example, if someone asks what do you do for work?
Even my own colleagues working in different departments will often ask me this question! To keep it short and sweet, I usually respond by saying that data science relies on mathematical, statistical, and computer tools to sort, visualize, and enhance data. As the famous quote from Ronald Coase states, “if you torture the data long enough, it will confess”. Data scientists are responsible for exploiting and processing raw data in order to extract useful information from it.
“if you torture the data long enough, it will confess”
How has data science changed in the time you have been working in this field?
It is a discipline that is evolving very quickly with a large community of influential researchers. New algorithms are emerging every day to solve specific problems. Additionally, tech giants like Google, Facebook, Amazon, etc. now regularly provide free and open-source software libraries for data flow and differentiable programming like Tensorflow and Apache Spark, to name a few. These are readily available and have very few glitches, which is a vast improvement compared to when they first hit the market.
In the next episode of our series dedicated to Data Science, we’ll explore what being a Data Scientist implies daily.