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Tutorials: Sept. 25

Computational servers will be provided for the tutorials.  Participants will need to bring their own laptop computer to access these servers (a web browser is  all that is needed for this access). 

The listed times are tentative.

9:00 - 10:00 am

Introduction to Python for Data Analysis

Topics: basics of Python coding

Oklahoma Memorial Union Ballroom

Aditya Narasimhan

Sudhindra Gopal Krishna

10:00 am - noon

Introduction to Scikit-Learn and Pandas

Topics: representation and visualization of data, regression, classification, model testing, hyper-parameter selection

Oklahoma Memorial Union Ballroom

Monique Shotande

Keerti Banweer

12:30 - 3:30 pm

Deep Learning and Convolutional Neural Networks

Topics: Keras, deep neural networks, convolutional neural networks for 2D & 3D data, interpretation of deep models

Split: Rawl Engineering Practice Facility Room 200 (primary)

Oklahoma Memorial Union Heritage Room (overflow)

Ryan Lagerquist
4:00 - 7:00 pm  

Dataiku for Solving Prediction Problems

Rawl Engineering Practice Facility Room 200


Key Tutorial Details

Server Details


User name: choose your own

Password: *****

Code for the Tutorials

  • Introduction to Python: Code and the GitHub  
  • Scikit-Learn: Code  and the GitHub 
  • Deep Learning and Convolutional Neural Networks: Code and the GitHub