DSATL'18: Data Science Sandbox

October 18, 2018 | Atlanta, GA


Data Science 101
  • How to become a data scientist
  • Common Python libraries: NumPy, Pandas, SciKit Learn
  • Foundations of machine learning
  • Feature engineering and grid search
  • Interpret your model with LIME and Reason codes


Data Visualization
  • The goal of the workshop is to provide a thorough workflow in R and Shiny
  • Build user interfaces and learn deployment options
  • Build, visualize and test visualizations that focus on communication and results
  • Learn how to put Shiny apps into production


Text Analytics
  • Introduction to text analysis
  • How to represent text numerically
  • Descriptive inference from text
  • Foundations of supervised learning techniques
  • Foundations of unsupervised learning techniques


Deep Learning
  • Learn PyTorch for implementing cutting-edge deep learning algorithms
  • Understand PyTorch's Tensor library and neural networks
  • Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more
  • Train your neural networks for higher speed

Meet Our Best-in-Class Instructors

Lisa Su

Data Scientist
GE Digital

Lisa joined GE Digital as a data scientist after graduating with her Masters in Analytics from Georgia State University. At the GE Digital Data Science Team, she applies her skills in machine learning and statistics to transform company data into business insights.

Su's work specializes in the domain of sales and commercial operations for industrial technology and creating dynamic solutions for challenges facing the business.

Jonathan Regenstein

Director of Financial Services

Jonathan is the Director of Financial Services at RStudio and the author of Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Management (CRC Press).

He writes the Reproducible Finance blog series for RStudio and his code/apps can be seen at

Prior to joining RStudio, he worked at JP Morgan. He studied international relations at Harvard University and did graduate work in political economy at Emory University.

Joe Sutherland

Peachtree AI

Joe applies techniques from computer science to questions of substantive interest in political science and economics. His current research focuses on how to study political representation with text data, with academic and popular publications that focus on political behavior, elections, political methodology, natural language processing, and machine learning.

Sutherland aims to make the methods and software developed in the course of his research beneficial to as broad of an audience as possible.  Additionally, his recently published open-source computer vision software has seen incredible adoption by researchers developing text corpora (forked and starred more than 99.9% of projects on GitHub). 

Zsolt Kira, Ph.D.

Assoc. Dir.of Machine Learning
Georgia Institute of Technology

Zsolt is a Senior Research Scientist at the Georgia Tech Research Institute, where he leads a group of researchers as the Chief of the Machine Learning and Analytics branch. 

Dr. Kira serves as the Associate Director of ML@GT which is the machine learning center recently created at Georgia Tech.  He also recently accepted an offer to join Georgia Tech's School of Interactive Computing as an Assistant Professor in the College of Computing. 

Dr. Kiras' areas of research specifically focus on the intersection of learning methods for sensor processing and robotics, developing novel machine learning algorithms and formulations towards solving some of the more difficult perception problems in these areas.