USING BIG DATA FOR ADVERTISING AND MARKETING

NDA terms are in progress. Full post to follow.

INTRODUCTION

With the rising popularity of the general public's need to be constantly "connected" and an ever-increasing number of digital interactions, an unthinkable amount of data is being generated every second of every day (think credit card transactions, geospatial location data, satellite imagery, Internet of Things (IoT), app "check-ins," Facebook likes/dislikes, etc).  A mobile advertising company has amassed data ranging from geospatial location data to retail purchases/purchase frequency to hobbies/interests on households across the USA. Our job was to sift through the data to segment the customer base in order to gain insights into specific behavioral patterns and trends.

Data Set

The data set provided was approximately 150 million observations with over 950 features.  The data was sectioned into parquets and uploaded to an Amazon Web Services (AWS) S3 bucket.  An AWS instance was set up through Databricks, and the AWS S3 bucket containing the parquets was linked to our Databricks account.

 

Andre Toujas
Andre Toujas
“Why?” The question “why” has always intrigued Andre. That simple question is what made Andre pursue his degree in mechanical engineering, and answering “why” is what drives him as a data scientist. Two of his four NYCDSA projects were for real companies: Zillow and Sito Mobile. Through his project work with actual data sets, Andre is familiar with implementing the entire machine learning pipeline to answer real, business-related questions. Andre also realizes how important communication is to a data scientist, and he plans to leverage his project and communication skills he developed through his 10+ years of service as an engineering consultant program manager: delivery of technical presentations to non-technical audiences, leading cross-functional project teams, completing projects within schedule/budget constraints, etc.
Yuxuan Hu
Yuxuan Hu
Graduated from NYU with a master’s degree in Management and Systems, and a specialization in Database Technologies. Data enthusiast, business forecaster, and workforce strategist with strong skills in Data Mining, Modeling and Visualization. Proven data analytics experience in advertising, healthcare, real estate, education, consulting and technology industries.
Shivakumar Ranganathan
Shivakumar Ranganathan
Shivakumar Ranganathan (Kumar) earned a PhD in mechanical engineering from the University of Illinois at Urbana-Champaign. He has a wide range of interests in Engineering, Medical Implants, Education and Data Science.
Wei Liu
Wei Liu
Wei is a current undergraduate student of Petroleum Engineering at Penn State University. Wei's research of applying machine learning to perform numerical simulation gets him fascinated with data science. His dedication and fast learning skills are leading him to explore data science in different fields.

Leave a Reply

Your email address will not be published. Required fields are marked *