10 Commandments of Data Science: 2023 Edition

For effective and ethical work in data science

Data science is a powerful tool for uncovering insights from vast amounts of data. As with any profession, there are certain rules that should be followed in order to do it effectively and ethically.

I recently brainstormed a list of “commandments” you might say apply to data science. The following is a list of the 10 Commandments of Data Science which aim to provide guidance on how to make the most out of this technology.

1. Concentrate on resolving the issue at hand, rather than relying on various tools and techniques.
2. Although the data you need may not be easily accessible, with a bit of elbow grease and dedication you can gather it or get it ready to go. Bear in mind that 80% of your time will likely have to be devoted to collecting and refining the raw data.
3. Don’t underestimate the impact that Excel, SQL, and other “elementary” have on data analysis — they are often among the most potent instruments.
4. Not every problem requires the complexity of neural networks. For many tasks, simple models such as Linear or Logistic Regression are more than sufficient.
5. Traditional methods are not always the ideal solution for practical problems. To succeed, you must be willing to take risks and think outside of the box in order to discover new approaches that can make a difference.
6. It’s simply impossible to memorize everything. Fortunately, in the workplace, there are plenty of resources available at our fingertips such as Google and Stack Overflow for assistance when needed. Also ask for help from others IRL in-person!
7. Harness the power of data visualization and hone your ability to present key insights in a straightforward manner. This skill set will prove invaluable when interacting with non-technical personnel and business stakeholders alike.
8. Master PowerPoint and storytelling — if you cannot move people with your narrative, then they might never understand the value of all your hard work.
9. Staying up-to-date with the innovations in Data Science is essential or else you risk becoming outmoded quickly. Make sure to constantly learn and stay ahead of the curve!
10. Spend your energy and resources on finding a solution instead of relying heavily on tools, technologies, and models.

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