This is exactly the video that I was looking for🤩🤩 I'm really impressed. Could you be able to recommend some credible videos on product management? It's hard to tell who's really sharing relevant content. I would appreciate it.
Really liked the video. Could you make another video covering how one should navigate Kaggle as a beginner and explain the code from people who score high in popular competitions. Thanks
Enjoy your videos, found them helpful even as a data scientist already. Hope you don't mind me commenting on some trivial details, but I did catch a few spelling errors that I wanted to bring to you attention. In this specific instance, it is "produce management principles" when you mean "product management principles".
Thanks for these suggestions. They are great, and it's something for us to consider. We'll see how this fits in our publication plan and try to come up with something interesting.
Thanks alot. Do you think being a DBA for about 3 years can translate in to a softer landing for anything beside SQL? Been looking around for a while on how to approach Data Science and your video really helped me.
Yes, I do think a DBA can definitely make it's way to data science. There's a current trend of data scientists that specialize in data engineering/pipeline building so your skills would be greatly appreciated here. A difference is that while DBAs mainly work in SQL, building pipelines for data scientists often include knowledge in python and Airflow (or similar automation technologies). Once you get good at python, you can start developing more statistical and mathematical skills. Another path is to become a data analyst, which mainly codes in SQL and python, but does not build models. This gives you the skills to solve business problems and work with stakeholders. The career trajectory here is to then become someone that deals with product/marketing analytics solving product related questions to drive growth for both the product and marketing teams. From here you can then build your skills in more statistics and math so that you can build ML models. Most "Data science" jobs do not build ML models so even if you go down the path as a data science engineer or in product/marketing analytics, you're on the level as most data scientists.
I've heard of autoML but have never used it. Unfortunately, I don't know too many teams that use it. I know of one team that uses autoML for forecasting work. They're not data scientists but forecasters by training so the use of autoML helps them since they're not super technical folks. I hope that helps.
I think it's a good starting point if you have no experience. But you definitely need to try platforms with real examples and exercises if you want to progress past the intermediate level.
