Jul 8, 2019
Jeffrey’s guest today is Matthew Renze. Matthew is a Data Science Consultant, author, and public speaker. Over the past two decades, Matthew has taught over 200,000 developers and IT professionals how to make better decisions with data science! His clients include small software startups to fortune 100 companies across the globe. He’s also a Microsoft MVP, an ASPInsider, a Pluralsight author, and an open-source software contributor. His focus includes data science, machine learning, and artificial intelligence.
In this week’s episode, Jeffrey and Matthew are discussing data science for developers. Matthew explains what data science is, what developers should be aware of, the powerful ways in which data science can be leveraged, real-world examples of how software developers can use data science, the difference between machine learning and data science, and what’s available right now for developers who want to use utilize data science today.
Topics of Discussion:
[:38] Be sure to visit AzureDevOps.Show for past episodes and show notes!
[:53] Where to find Jeffrey’s book, .NET DevOps for Azure.
[1:32] About today’s episode and guest.
[2:07] Jeffrey welcomes Matthew to the show!
[2:25] Matthew speaks about his career journey and how he has ended up where he is today.
[6:25] What is data science? And what should developers be aware of?
[9:13] The powerful ways in which data science can be used.
[11:22] Matthew provides some real-world examples of how software developers can use data science.
[14:16] What’s the difference between machine learning and data science? And how do they fit together?
[16:43] A word from Azure DevOps sponsor: Clear Measure.
[17:10] Matthew explains what software developers can do with what’s available today in data science.
[20:26] If developers want to utilize data science, would they need to design their own data repository?
[21:21] What are the common choices for storing the data you gather?
[22:49] Is data science just a further progression beyond Kimball methods of star schemas and data warehousing? Or is it something completely different?
[23:46] Matthew explains some of the common terms associated with data science.
[28:26] What does a DevOps pipeline look like for data science? What does it look like to deploy a database?
[30:06] Where does A.I. fit into all of this?
[34:03] Does Matthew see this use of data science as a whole different paradigm shift to thinking?
[36:36] Resources Matthew recommends listeners follow-up on after this week’s episode.
[37:40] Where to learn more about Matthew and his resources online!
Mentioned in this Episode:
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