How do I become invaluable as a data science professional?


Would you like to become more valuable to your company? Many people want to know how they can move up the corporate ladder. In order to do that, you must learn to contribute more value. In the following article, we intend to present you with information on how to go from good to great in data science.

1) Improve at Handling Messy Data

Learning skills such as identifying which data is important is certainly necessary. Learning how to spot patterns and anomalies is also an important skill to have. These skills are something that you should already have down pat.

Bad data quality is one of the toughest challenges, though, facing the industry. It’s important to learn how to eliminate the messiness more efficiently. This area of data science takes up two-thirds of most data science professional’s time. If you can master that, then you will be on top.

2) Focus on Techniques instead of Tools

This industry is crowded with dozens and dozens of tools trying to grab your attention. Quite honestly, you could learn a new tool in weekend and even supplement any existing knowledge on the job. Learning tools should not be your focus. A more valuable skill to have would be to know how to apply more techniques to your industry.

3) Master the Art of Applying Techniques to Real-World Problems

More than eighty percent of ideas fail. From picking the wrong business problem to picking an incorrect approach to the real problem, there are many reasons for why business ideas fail. One of the most valuable skills to have is understanding how to solve business problems with the proper data science techniques.

4) Learn How to do More than Just Analytics

Many businesses often only focus on data science skills. Every data science team must have 5 skills in order to succeed. These skills are the following: translating domain knowledge, designing the information systems, performing analytics with machine learning algorithms, developing the data engineering systems, and managing projects. Without these skills, a data science team will be lackluster at best.

Leave a comment

Your email address will not be published. Required fields are marked *