Why Attend Teradata Universe

Get answers to your greatest challenges

Data Scientist
Your Challenges

You explore and analyze raw multi-structured data looking for new associations and insights.

You need to spend less time wrangling and moving data, and more on exploring faster, analytics that enable more iterations and less sampling…

…so you can test more hypotheses on more data for more accurate models and insights.

Why You Should Attend

Sessions directly related to your role
Open discussions and thought sharing
Meaningful peer networking
Hands-on workshops

Sessions for ANSWERS to your greatest challenges

Not Reinventing the Wheel: Using Teradata Vantage ML Functions with Python 
Now that you have your data cleaned and formatted, how do you bridge the gap to start using it for machine learning? Join us to discuss how we leveraged Teradata Vantage and Python to make machine learning models to predict how many people would attend Utah Jazz games. Breakout #1760

Algorithms in Action: Data Science Enables Brazilian Government Organization Business Outcomes
Identifying product tax impact has been a long-running and widespread problem for the Tax Department of Parana, especially due to reoccurring inaccurate identification of products in sales transactions. See how, using RACE methodology, Teradata helped it identify the core problem using advanced analytics. Breakout #1801

Data Science 101: Everything You Need to Know to Understand Data Science and Data Scientists
Expose yourself to typical data science activities, the methodologies and techniques used while walking through a project with some simple code examples. No previous programming or mathematical experience is necessary! Breakout #1792

Putting It All Together
Examine a complete data science workflow using Vantage using Jupyter Notebooks, Teradata's Python library, AppCenter, and MLE analytics. Review four quick and easy ways to make custom data transformations. Breakout #1794

EXCLUSIVE: Teradata Vantage™ education and exploration

  • Putting It All Together in Vantage
  • Strategies for Measuring, Monitoring, and Managing Your New Analytic Workloads on Vantage
  • Exploring the Power of Vantage for Path Analysis (Banking Case Study)
  • Predictive Analytics Using Vantage: An End-to-End Streaming Pipeline for Providing  Live, Actionable Insights (IoT)
  • Pathing Analytics for Healthcare Business Outcomes Using Vantage (The Centers for Medicare and Medicaid Services)
  • And more, related sessions below!

More sessions for Data Scientists

Vantage and R, a User’s Perspective: Building a Modern Analytics Workflow at Wells Fargo

Join a longtime R and Teradata Aster user to learn the about the benefits and opportunities he discovered in converting a complex analytical application from Teradata Aster to Vantage. 

Breakout #1806

Your Vantage, Your Language! Data Science In The Vantage External Languages Framework

Take part in a presentation that illustrates the Vantage external languages framework, as we showcase how you can perform the same sample analytical task in any of the R, Python and SQL languages, and take advantage of the Vantage scaled performance regardless which of these languages you prefer to program in.

Breakout #1874

R Is For Real: Power-Scaled R Analytics With Vantage

Explore more detail around our R offerings in the context of external languages in Vantage, tips and best usage for each option, and examples of how to make optimal use and achieve scaled performance with your R code.

Breakout #1873

Data Science From Scratch: Establishing a Data Science Workflow From Ideas into Answers

Join the Teradata Service Analytics Machine (SAM) data scientists, as we walk through our journey of interpreting the vision, making sense of the data, deriving the labels, experimenting with algorithms, and measuring performance.

Panel #1855

Analytics at Scale Using Python and R on Vantage

The standard data science process starts with identifying an interesting business question and then getting the data to determine the answer. In this paradigm, data is often downloaded from one or more servers and then analyzed on a laptop using familiar tools such as Python and R. This results in increased data wrangling associated with moving data as well as suboptimal processing from working on a laptop. Explore with us three approaches to scaling: using standard Python and R to extract from databases, accessing Python or R via an SQL script table operators, and accessing Vantage via Python using TeradataML or via R using tdplyr. 

Breakout #1900

Hear first-hand from customers

How they address the same challenges you’re experiencing, while empowering their  business with insights from all their data, all of the time.

Tracks you may be interested in:


Join us at Teradata Universe 2019
October 20–24 in Denver.

Register now