Enrico van de Laar
Enrico has been working with data in all kinds of formats and sizes for over 15 years.
He is a Data & Advanced Analytics Consultant for Data Masterminds, where he helps organizations take their first steps in the world of Advanced Analytics.
Enrico is a Data Platform MVP since 2014 and a frequent speaker on various data related events all over the world. He authored the book “Pro SQL Server Wait Statistics” and blogs about technologies like Microsoft SQL Server and Azure Machine Learning on his blog at www.dotnine.net.
PRECON: Applied Data Science for BI Professionals and DBAs
Data plays an ever-increasing role in today’s society and provides a wealth of possibilities for you and the organization you work for. As a BI professional and/or DBA you are probably used to working with data every day, but what if business requirements can no longer be met through traditional solutions? In those cases, more-and-more organizations look to data science to provide a solution.
But how and where do you start with things like exploratory data analysis or machine learning? Thankfully as a BI professional or DBA you have a head start when it comes to data, and through this full day workshop you will learn the skills you need to take your first steps in the area of data science!
What you will learn during the day:
- Understand the basic concepts of data science processes and why its different than traditional approaches
- Get more familiar with statistical terms and techniques and learn why they are important
- Making a start with programming in the R language
- How to use R and Azure Machine Learning to build your first machine learning models
- Operationalizing your models through various methods like in-database R using SQL Server 2016
Moving advanced analytics to your SQL Server databases
Traditionally advanced analytical solutions, like machine learning, require you to bring your relational data to the machine learning model. Your model would then perform a prediction and return the results. While the process described above is reliable, it involves moving your data between the database where it is stored and the location where your model resides. This also means an increase in the complexity of your analytical solutions. For instance, how do you trigger the scoring of new data as soon as it enters the database? Or, how can you design this process for real-time scoring?
With the release of SQL Server 2016 Microsoft integrated a solution to the questions above, in-database analytics, allowing you to bring the analytics to your data instead of the other way around. Through in-database analytics we can design, train and score models directly from SQL Server without moving data out and back into the database. This creates a huge advantage, especially when working with real-time predictions, but how do you implement in-database analytics in your environment?
In this session we are going to explore the various methods available inside SQL Server 2016 & 2017 to perform in-database analytics. From building and storing our models directly inside SQL Server, to performing real-time scoring on data as soon as the data is stored inside a table.
After this session you will be able to understand the advantages and disadvantages of the various in-database analytics methods and you will be ready to start building your first in-database models!