Talks

All the ways to break into data science and analytics

In this session, we will explore all the various ways to pivot into the data science field. We will go over some common scenarios, and will inspect different pieces of advice found on the Internet.

R in Power BI

Venue: SQL Saturday #759 Event URL: http://www.sqlsaturday.com/759/Sessions/Details.aspx?sid=78517 Slides: https://taraskaduk.github.io/rpowerbi.html In this session, we will touch on R as the language for data science, and then will dive right into how to use R within the Power BI environment, and why to use it in a first place.

R and Power BI - Better Together

Venue: Jacksonville Power BI User Group URL: https://www.meetup.com/Jacksonville-Power-BI-User-Group-PUG/events/247568258/ R is one of the leading languages in the world of data science. It has a growing following and many applications, including data transformation, machine learning, statistical modeling, data visualization, web applications, and even building websites. R is widely used in academia, science and business. Some of the large companies like Microsoft, Facebook, Airbnb, Etsy, Stitch Fix, IBM and Uber use R for data science exclusively, or in tandem with Python.
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SASS: Simplify, Standardize, Automate and Scale your enterprise reporting

Venue: Jacksonville Power BI User Group URL: https://www.meetup.com/Jacksonville-Power-BI-User-Group-PUG/events/243129812/ In this session, we will talk about how to run Power BI reporting on enterprise level with minimal resources (i.e. lean), and introduce you to a SASS framework: simple, standard, automated and scalable.

Migrate your enterprise reporting into Power BI: process canvas, pitfalls & best practices

Venue: SQL Saturday #649 URL: http://www.sqlsaturday.com/649/Sessions/Details.aspx?sid=6717 In this session, we will discuss how to efficiently migrate your retrospective analytics from where it is now to Power BI, and do it in a way that saves you time in the future. Some of the topics include: How to create a solution that is simple, automated, standardized and scalable How to avoid common pitfalls and “death by a thousand reports” How to most efficiently distribute the reports afterwards