text analytics

TEXT ANALYTICS + NLP

mining and analyzing text data for pattern and knowledge discovery

Thurs, Oct 17 | GSU Buckhead Campus, Tower Place 200 | 7:30 AM - 4:00 PM

Location: Registration in Lobby | Breakfast in 1201 | Workshop in Room #601

NLP Basics

learn the basics of NLP and supervised/unsupervised approaches in machine learning

INDUSTRY APPLICATION

get an edge in your industry by applying text analysis in a variety of business use cases

FUNDAMENTALS

use feature engineering, dimension reduction, visualization, and statistical inference in Python (with NLTK, scikit-learn, and seaborn)

OVERVIEW

In this immersive 1-day certificate workshop, students will learn actionable steps to began leveraging your text data, with real-world case studies and code to reuse when developing your own applications.

This workshop provides a hands-on introduction to text analytics. We will use three classic business use cases as our guides: rating movies at Rotten Tomatoes, preventing bullying on Twitter, and pricing beer based on the label. Along the way, you will learn about natural language processing, feature engineering, dimension reduction, visualization, and statistical inference in Python (with NLTK, scikit-learn, and seaborn).

Our workshops are fun and personalized in a small class setting and taught by leading experts in the field. This workshop is 1-day certificate course with a hands-on approach that assures you'll be able to apply what you learned right away.

EXPERIENCE LEVEL

Basic Coding Experience
%

AUDIENCE

Data Scientist
%

JOE SUTHERLAND, PHD

TEXT ANALYTICS INSTRUCTOR

Joe Sutherland specializes in Computational Social Science and Text Analytics, applying 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. Additionally, his academic and popular publications study political behavior, elections, political methodology, natural language processing, and machine learning.

Sutherland’s goal is to make the methods and software developed in his course of doctoral research at Columbia University beneficial to as broad of an audience as possible. His recently published open-source computer vision software has seen incredible adoption by researchers developing text corpora (it has been forked and starred more than 99.9% of projects on GitHub).

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