Amazon discover.bot

I recently got a new gig as Industry Analyst for Amazon’s new discover.bot business unit. I will be advising on AI chatbot technology and writing periodic articles assessing how bots are quickly evolving across a broad variety of industries. My first piece is now available as of October 30, 2019, “Should a Chatbot Reveal Itself?” I’m looking forward to following this dynamic and growing industry segment.

UCLA Extension – Intro to Data Science

I’m really excited to be teaching again! Starting April 2 and 4, 2019, I’m teaching two sections of the popular “Introduction to Data Science” course for UCLA Extension. In a previous life I taught at UNEX for 15 years, but after getting too busy as a data scientist, I had to walk away from teaching for a while. I’m happy to be returning to education now. I feel the time is right to get future data scientists up to speed to fill in the widening skills gap due to the accelerating rise in demand for data science professionals. Glad to be giving back!

[UPDATE: I am now teaching regularly for UCLA Extension, 2 classes per quarter. Due to COVID-19, all classes are virtual, but enrollment is great! My course GitHub repo is found HERE.]

Universal Standard Housing

I’m pleased to announced that I have secured a new consulting relationship with Los Angeles based Universal Standard Housing (USH), a leader and innovator in the affordable housing industry. I will be providing data science consulting services to help lead an exciting new development project. This is a great start-up company, with some very smart and dynamic people. I’m looking forward to a successful deployment of machine learning based real estate solutions.

Open Data Science

I would like to announce that I’m now working with a new up-and-comer in the industry – Open Data Science, most commonly known for their Open Data Science Conferences (ODSC) West, India, Europe, and East. More recently, the company is providing a wealth of technical resources such as articles dealing with data science tools, modeling (machine learning), AI, deep learning, data viz, data transformation, academic research, and so much more. I will be making regular contributions as a data scientist. My first contribution is “Tips for Linear Regression Diagnostics.”

[UPDATE 11/23/2020] : since my original announcement, I’ve made 80+ contributions for the ODSC blog. I just received excellent feedback from the company that my pieces were the most popular! You can read all my article HERE.

Data Science with R Master Class

I’d like to announce my new educational resource – a complete video series of instructional screencasts designed to get you up to speed with data science and machine learning using the R statistical environment. The materials are offered over on O’Reilly Safari Books Online as “Data Science with R Master Class.”

This video series will show you how to apply R to your data science projects. You will learn to perform the data science process including data acquisition, data transformation, exploratory data analysis, data visualization, and statistical learning algorithm usage. This course will provide you with the tools and techniques required to excel with statistical learning methods in tackling important data problem domains. The R statistical environment was chosen for use in this course because many data scientists use it exclusively for their project work. All of the code examples for the course are written in R. In addition, many popular R packages and data sets will be used. The R code for all modules of the class can be found in this GitHub repo.

I’ve patterned the materials to be consumed in parallel with my book “Data Science and Machine Learning: An Introduction to Statistical Learning Methods with R.”

FICO Artificial Intelligence Review

I was contracted by the FICO product marketing department to review their long term use of artificial intelligence (AI) for fraud, cybersecurity and compliance (AML) solutions. All three of these areas utilize machine learning and AI for anomaly detection. The flagship of this portfolio is the Falcon Fraud Platform, and is used by ~ 10,000 financial institutions to risk score approximately 9,000 payment card transactions/sec globally. I had a blast digging into all that FICO was doing with AI, and I came up with a summary document “5 Keys to Successfully Applying Machine Learning and AI in Enterprise Fraud Detection.” Here is a list of the 5 elements I focused on for the project:

• The role of supervised and unsupervised models in fraud detection (with a focus on behavior anomaly analytics)
• The importance of large data sets in model development and training
• What are predicting features and why is domain expertise necessary in their development
• The benefits of Specialized vs. Generic models in enterprise fraud (i.e., importance of expert features)
• The role of adaptive analytics and/or self-learning AI in enterprise fraud

You can download the report here: 5_Keys_Successfully_Applying_Machine_Learning_ AI

insideHPC Special Report: Riding the Wave of Machine Learning and Deep Learning

I’m very pleased to announce the availability of a new special technology report that I was contracted to research, develop and write – “insideHPC Special Report: Riding the Wave of Machine Learning and Deep Learning” sponsored by Dell EMC and NVIDIA. insideHPC is the sister publication (specializing in high performance computing) to insideBIGDATA where I serve as Managing Editor.

The report focuses on how many companies are moving decisively to develop capabilities based on AI, machine learning and deep learning. In time-honored business fashion, the motivation is a combination of fear and hope. Competitive pressures are spurring companies on, and there is a sense of urgency among many enterprise thought leaders about not falling behind.

You can download a copy of the report HERE.

insideBIGDATA Guide to Artificial Intelligence & Deep Learning

I’m very pleased to announce the availability of a new technology guide that I was contracted to research, develop and write – “insideBIGDATA Guide to Artificial Intelligence & Deep Learning” sponsored by NVIDIA.

This guide to artificial intelligence explains the difference between AI, machine learning and deep learning, and examines the intersection of AI and HPC. The guide includes a special section highlighting the results of a new insideBIGDATA audience survey to get readers thoughts about how they see AI, machine learning and deep learning for their own companies. The guide provides some of the survey results including numeric results, data visualization, and interpretation of the results.

You can download a copy of the guide HERE.

insideBIGDATA Guide to Healthcare & Life Sciences

I’m excited to announce the availability of a new technology guide that I was contracted to research, develop and write – “insideBIGDATA Guide to Healthcare & Life Sciences” sponsored by Dell EMC.

The healthcare and life sciences industries historically have generated vast amounts of data. These large volumes of data hold the promise of supporting a wide range of medical and healthcare tasks, including clinical analytics and decision  support, patient profiling, disease surveillance, regulatory and compliance requirements, scientific research, and many  others. Data in healthcare and life sciences is expected to grow exponentially in the coming years and will be beyond the  capability of the traditional methods of data management and data analytics. This new guide details this upward trajectory for enterprise thought leaders.

You can download a copy of the guide HERE.

Los Angeles Fashion Industry: A Data Science Perspective

Over the past year, I’ve been working on a new research project that culminated with a whitepaper in support of the Los Angeles fashion industry – “Los Angeles Fashion Industry: A Data Science Perspective.” The work was done in collaboration with LA’s premiere fashion mart, The New Mart. The paper is in response to numerous stories found in the local apparel industry trade press that held the sentiment that LA fashion had seen better days and was on a downward slide. We decided to use privately procured and publicly available data sets to find a single truth surrounding this matter. The whitepaper’s position is quite the opposite – LA fashion is a Los Angeles mainstay and continues on an upward trajectory. See for yourself by downloading the paper HERE.