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.
![Solving Complex SQL Interview Questions [How to organize lengthy code solutions]](http://i.ytimg.com/vi/vLjAG9eXkcU/mqdefault.jpg)
![Solving Complex SQL Interview Questions [How to organize lengthy code solutions]](/img/tr.png)
I have high respect for people like you who are so generous in sharing knowledge in this field of Data Science. Thank you so much.
Thank you so much for the kind words. And glad you enjoyed these videos!
Seriously second that!!! Thank you so much, Nate!
Don’t understand how this channel doesn’t have millions of subs. thank you so much!
my new fave youtuber rn. thanks sir. hopefully more of this kind of video
Thank you and you're welcome. Sure, we will continue to produce more videos like this.
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.
tysm 4 the video! next step is building confidence as I'm learning sql/python; you've put me in a better direction
Thank you! I'm glad that I've helped in some way =)
This channel deserves more like and subscribe! Great content Nate!
A lot of details to go through. Gives a clear picture. Loved it. Thanks.
Thanks for watching! I hope that at least you have some resources you can explore to improve your skills.
Thank you, Nate!
Amazing Content, you're helping a lot of people, inclusive me.
Thanks for the clarity!
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
Excellent Video, Loved it. Thank You !!
Thanks so much for watching!
Thank you. I appreciate the content. Really liked it.
Thanks for watching! If there's any other topic you'd like me to cover, please let me know!
I wish I could come across your channel much earlier. Thank you.
Thanks for your clear instruction and resources
Thanks for watching! Appreciate the comments you've made throughout the other vids as well.
Thanks. Great video. I will suscribe
Thanks for watching! if you have any requests for topics, let me know and I'll see if I can cover them.
Thanks for your helpful tips
My pleasure 😊
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 so much for letting me know. I changed what I could find but yea, I sometimes rush through writing the description...
Thank you!
Can you talk about how Data Visualization, information retrieval (data mining )and Data Warehousing plays into the framework you provided?
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.
awesome
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.
@@stratascratch Thank you! priceless advices. will continue following you and learn.
thank you
Thanks for watching!
I have a job so I can't make a perfect schedule. Could you give me some advise?
Thanks
Thanks for watching. Happy to also cover any topics you have in mind.
Thanks for your guidance. May I ask, based on your experience in the industry now, how prevalent is the use of AutoML ?
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.
@@stratascratch yes it does alot ! last question , what autoML do they use? super thanks!
@@richarda1630 I believe they use AWS Sagemaker with Autopilot (aws.amazon.com/sagemaker/autopilot/). Hope that helps!
@@stratascratch Yes it does! thanks so much once again :D
Thank you sir!
Thank you for watching the video! Let me know if you have other suggestions for topics for me to cover.
@@stratascratch Yes Sir. :)
Is udemy a good sources? I was thinking doing their class.
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.
Thank you
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