Helping Others Learn
Thank you Kate, for encouraging us to be datacated. I have been in the statistics and engineering field for thirty years. Learning in the 1980’s how to solve matrix algebra problems by hand gives me an appreciation for the computerized tools available for visualization of today. Prolific author Douglas Montgomery created volumes of books and articles giving me a foundation of statistical engineering. While those books and articles have been my base, I became stagnant in my learning. I dabbled in R ten years ago. Then, I used a hashtag #data on LinkedIn. I am inspired to learn again. Why? It is this window into the many generous connections. Here are the qualities to make a great data scientist.
- Encouragement. Entice others to explore the world around them. Be clear in communicating what you learn.
- Helping. Offering to look through someone’s code. We can learn more by helping others to be more concise in our visualizations.
- Open. So many industries are closed. The data field has been so receptive to beginners. My data connections appear approachable and genuine.
- Curious. A great data scientist will look at every graphic in a newspaper, even their child’s homework. Was this the best way to share the information? Is the analysis direct without misleading the audience?
- Competency. It is important to have solid data and statistics too. While color graphics and animated illustrations are catchy, the robust analysis is critical to be great.
- Communicator. If the audience of your analysis is unclear of the intended message, you have failed your reader. Clear communication is key to being great.
These are characteristics of great data scientist. May each of use strive to improve in each of these areas.
-Jerry Visser, Helping Others Learn