Stategic Thinking…
Even though I have spent a significant amount of time over the past two years learning and teaching data science concepts, I find that I am often using other skillsets to solve problems. As engineer, many of the tasks involve optimizing processes and predicting events from a theoretical perspective. Machine learning is very helpful for repetitive tasks or determining human behavior since data exists where this information can be revealed but finding data of optimized processes or rare events is very sparse. My go to skillset is often simulations, algorithms, and linear programming.
Out of curiosity, I asked several peers what skills they use in their data science careers and in general, they had similar responses to articles I have read online. My findings indicate that most professionals fall into a few categories. The most common data scientist is the type that typically uses tools like PySpark, SQL, and machine learning libraries to do most of their work. The second most common type is the professional that has more data engineering tasks that emphasizes cloud technologies as part of their day-to-day work. The third most common type was a couple people who do a lot of software engineering tasks that involve creating databases, dashboards, and front-end work. The last group does the least amount of analysis work but does spend time tracking application up-time and user problems.
I was very surprised that essentially no one does simulations or linear programming or even works on efficiency problems. It is probably due to the industries that they work in. The only online references I found to these topics were a few universities that include a decision scientist course in their curriculum. Otherwise, these skillsets are often found in role-specific master’s programs like supply chain management or power systems engineering. I find this to be very unfortunate.
The main benefit of knowing these other methods is how it affects your strategic thinking skills. In many cases the risks and limitations of a decision can be predicted to within reason or at least contingency planning can be put in place in advance. What really makes it a beautiful process is incorporating six sigma practices that encourage collaborative decision making and investment in making decisions successful. I am sure I will return to this subject in the future as I develop examples related to transportation and logistics and process monitoring.
Update: This article was updated on 8/9/2023 after realizing the article was incomplete.
