Data has been a critical piece to effective software applications and products for decades, but in recent years the amount of data and the tools to learning from that data quickly with tools and services has been increasing rapidly. Because of that, it is becoming more important that every developer understand how data can help improve their solutions, what the data science lifecycle is, and how to actually implement it as part of their application.
If we're successful
The goal of this video series is to provide developers with something even better than a first step, a "Step 0" into the world of data science. While the age old argument about whether to learn high-level to low-level or low-level to high-level (or conceptual vs practical) isn't resolved in this series, we hope that it can be that very first step into the world of data science from both a conceptual and practical approach.
This is for you, our community of developers, and we plan to continue making more content resources with the launch of this series. The breadth-first conceptual explanations paired with the depth-first practical example is meant to spark curiosity and help give you the confidence to try something on your own. We hope that once you go through this series you come back and let us know where you want more explanation or a deeper dive.
What you will learn
In just under 1.5 hours, Sarah and Francesca introduce you to:
Weaved through the conceptual discussion, you will also find a practical example that demonstrates:
The series premise
True to their expertise, Sarah and Francesca each play a role in this video series.
Sarah is a developer who has built a bike sharing app. She is interested in making sure her bikes are available where and when people need them, and making more money . She's been gathering data on how many people rent bikes on which days and she knows that there is a way to use this data to improve her service, so she's reached out to Francesca.
Francesca is an expert in data science and machine learning. Also a developer, Francesca is the perfect person to guide Sarah through these new concepts and tools. Francesca provides high level, conceptual guidance and explanation, but also jumps in to collaborate with Sarah within her Azure Machine Learning resource to help with featurization.
This series isn't *only* 28-videos, it's also an ecosystem of content and resources that we hope will support you on your own data science journey.
We've created a GitHub repository with:
We also want to encourage you to continue learning and exploring through Microsoft Learn, where we're curated a collection of learning paths that you might find dive into the topics we didn't code, or offer new perspectives and contexts.
More videos coming
The month of July 2020 is Data month for the Microsoft Reactors global live streams on Twitch! For the middle two weeks of July, you will get to dive even deeper on these and similar concepts with Sarah, Francesca, and other Cloud Advocates and Microsoft employees!
|Date||Stream Topic||Speakers||Registration Link|
|July 13, 2020||A Developer's Introduction to Data Science||
Sarah Guthals, PhD
Francesca Lazzeri, PhD
|Register on Meetup|
|July 14, 2020||Coding Optional: Using Azure Machine Learning Designer||Cassie Breviu||Register on Meetup|
|July 15, 2020||Get More Instagram Likes with Machine Learning||Dmitry Soshnikov||Register on Meetup|
|July 16, 2020||From Raw Data to Well-Cooked Answers: A Financial Data Science Lifecycle||Sarah Guthals, PhD||Register on Meetup|
|July 20, 2020||Interview with an Expert: Azure Machine Learning||
Francesca Lazzeri, PhD
|Register on Meetup|
|July 21, 2020||Do It All with Azure Machine Learning||Francesca Lazzeri, PhD||Register on Meetup|
|July 22, 2020||How Automated Machine Learning Reduces Time to Insights||Amy Boyd||Register on Meetup|
|July 23, 2020||8 Machine Learning Examples||Sarah Guthals, PhD||Register on Meetup|
And if you've missed these streams, recordings of them will be available starting the Friday of each week on the Microsoft Reactor YouTube channel.
If you want to make any requests for future content or additional explanations on what we covered in this series, you can open a new issue on the GitHub repo.
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