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Schedule

Opening: Problems in Data Analytics

Date: 10/12/2018
Time: 8:30am - 11:30pm
Location: REPF 200
Register Here!

Interested in data science? Curious about some of the problems currently plaguing modern data driven applications? This session will take you through OUDALab and what we do. We cover everything from security and recommendations, to human interactivity and visualization. Should you be interested in collaborating or getting involved with us and our ever-evolving research, reach out to any one of the students below or attend this session and pitch us your ideas!

Presentations

Christan Grant - Keynote
Jasmine DeHart - VIPER
Austin Graham - Human over-the-loop Analytics
Shine Xu - Simpson’s Paradox
Keerti Banweer - Tweet Geolocation

Tutorial 1: Back to Basics

Date: 10/26/2018
Time: 10am - 12pm
Location: REPF 200
Lead By: Keerti Banweer
Register Here!

Python has grown to be one of the top languages for data science for its ease of installation and simple syntax, enabling the quick prototyping of complex systems. In this session, we will present to you rapid-fire basics of Python and how they play into the various packages often used in data science! Packages that will be covered include Numpy, SciKit Learn, and Tensorflow.

Presentations

Austin Graham - Python Basics
Keerti Banweer - Python Data Packages

Tutorial 2: Real World Analytics

Date: 11/09/2018
Time: 10am - 12pm
Location: CEC 100
Lead By: Jasmine DeHart
Register Here!

The tools provided with Python enable a scientist to quickly build and deploy analytics applications. But how do these processes come together to form the advanced intelligent systems we are familiar with? In this session we will provide you with sample data, and guide you through the process of analytics from data cleaning to the evaluation of a solution. Do not worry, there is no need for any background knowledge in machine learning; we will teach you what you need to know!

Presentations

Jasmine DeHart - Data Pipelines with SciKit Learn
Austin Graham - Data Pipelines with Tensorflow

Tutorial 3: Bigger Than Me

Date: 11/30/2018
Time: 10am - 12pm
Location: REPF 200
Lead By: Austin Graham
Register Here!

Modern analytics are done at massive scales. Companies like Google, Facebook, or Amazon analyze millions of data points at a time to deliver us the tools we use every day. This data takes more memory than you’re average laptop can handle. How do they do it? What are the tools and how do they work together? In this session, we will take you through Hadoop, Spark, and Tensorflow, and how they communicate to form large scale data pipelines.

Presentations

Austin Graham - Distributing Data with Hadoop
Keerti Banweer - Distributed Processing with Spark

Contacts

Austin Graham

Email: austin.p.graham-1@ou.edu

Jasmine DeHart

Email: dehart.jasmine@ou.edu

Keerti Banweer

Email: keerti.banweer@ou.edu