Archive for September, 2015

bigdata_fashion_featureMy long affiliation with LA’s preeminent fashion mart – The New Mart, has been a fruitful one. This collection of over 70 high-end fashion showrooms is managed by a forward-thinking team that allowed me to engage methods of statistical learning to increase the reach of their many clothing lines through use of social media data sources. I built some cool technology to yield a weekly “Fashion top 10” that serves to drive The New Mart’s social media effort. Using sentiment analysis coupled with data sources like Twitter, Facebook, Instagram and fashion blogs, spreading brand awareness is approached in a strategic and focused manner.

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insideBIGDATA Guide to Retail

Posted: September 9, 2015 in Projects, Uncategorized

insideBIGDATA_Guide_RetailI’d like to announce the availability of a new technology guide that I was contracted to research, develop and write — “insideBIGDATA Guide to Retail” sponsored by Dell and Intel. This guide is directed toward line of business leaders in conjunction with enterprise technologists with a focus on the above opportunities for retailers and how Dell can help them get started. The guide also will serve as a resource for retailers that are farther along the big data path and have more advanced technology requirements.

I was excited about writing this guide since I spend a lot of my time as a practicing data scientist in the fashion industry where I build machine learning solutions to enhance brand awareness.

You can download a copy of the guide HERE.

MachineLearning_book_cover_smallI’m very proud (and relieved) to announce that my year-long+ book project is finally done! “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R” will be available later this year from Technics Publications. The book provides an introduction to the entire data science process, highlighting the ways that machine learning can be used to solve business problems. Both supervised and unsupervised statistical learning techniques are included. The R statistical programming language is used throughout. Here is the table of contents:

Introduction

Chapter 1: Machine Learning Overview

Chapter 2: Data Access

Chapter 3: Data Munging

Chapter 4: Exploratory Data Analysis

Chapter 5: Regression

Chapter 6: Classification

Chapter 7: Evaluating Model Performance

Chapter 8: Unsupervised Learning

The book is perfect for newbies just entering the data science field who wish to quickly get up to speed with the technology. I plan to use the book for the introductory courses I teach for corporations and universities. You can pre-order the book on Amazon HERE.