Database pros or anyone working with a data scientist, or wants to become a data scientist
Does R seem like an alien language to you? Does data science terminology seem overly confusing? Would you like to learn more about data science but are scared of the math? Fact is, you’re probably doing “data science” today. You don’t need to know a lot of R or python to be an effective.
In this 90 minute session we’ll focus on terminology, process, some tooling, what makes the best data for predictive models, and some real uses anyone can use today. The goal is to get you comfortable working with a data scientist so you can collaborate for better results. Finally, we’ll put it all together and create a predictive web service using Azure Machine Learning.
Why I Want to Present This Session:
Data science is the hot skill in the market, but it carries a lot of misconceptions. Traditional data professionals think that data scientists sit around all day tweaking model parameters. This isn’t true. Data scientists spend a lot of time struggling with data wrangling, which is a fancy way of saying ETL. This is where the traditional data professional can help.
Likewise, a lot of data scientists are coming out of college without a good grounding in relational data, which means they have a hard time communicating their data needs to us. Wouldn’t it be great if we understood each other a little better?
We need a lingua franca. Each side has a lot to learn about the other. I work with customers daily that have these problems. I’ve developed tricks that I think help the traditional data professional understand their data better and prepare it for “actionable intelligence” faster.
This is not a tool-based problem. SQL R Services won’t magically fix this problem. It’s a mindset change.
An older version of this session: https://aka.ms/davew_ds
2017/05 – This session was chosen by attendees for GroupBy June, but Dave withdrew it from contention after voting finished.
Latest posts by dwentzel (see all)
- So You Want to Be a Data Scientist? - November 23, 2016
- SQL R Services: Start Working WITH Your Data Scientists - November 22, 2016