So what is the difference? “Data Science vs Machine Learning” “A simple distinction between Data Science vs Machine Learning for project managers and team leads.” by Charles Martin, PhD Data Science & Machine Learning Expert (YouTube Channel) OTHER: Artificial Intelligence Vs. Machine Learning Vs Data Science Vs Deep Learning My Quora Answer to: What […]
Watch a free webinar by Mark Meloon, an experienced Data Scientist who has first hand experience with the difficulties landing that first gig as a Data Scientist.
He has helped a lot of others navigate and position themselves to get in the door and start their lucrative careers. It worth listening to what he has to say. His advice is proven to work. He’s given talks a major bootcamps (e.g. Metis, Galvanize) and have made a name for himself advising “aspiring” data scientist.
In this free training will show you his proven step-by-step system for landing the job of your dreams, even if you don’t have prior experience or a Ph.D.!
[Continually Updated] MastersInDataScience.org has culled together a list of 23 of the best US Universities/Colleges that offer a Masters in Data Science. Read More
Domino Data Labs recently made the Gartner 2017 Magic Quadrant for Data Science Platforms. Here, Domino’s Chief Data Scientist, Eduardo Arino de la Rubia, does a great job making the very complex easily understandable. Learn more about the platform…
This book has been written in layman’s terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, they stick to intuitive explanations and visuals.
“This list of 500+ was started in 2012, updated in 2014 and also very recently according to the author. It was compiled by 101.datascience.community, and broken down by degree (master / bachelor / certificate / doctorate) and location (online / on-site.)” – Source Data Science Central Read More
At the Machine Learning Meetup in NYC, Dan Melamed gave a machine learning talk titled: “How To Learn From What Your Users Might Not See”. This talk will focus on contextual bandits and their applications.
In this tutorial, Dan will show how to learn from such data in a principled, efficient, and unbiased manner. The techniques that he will describe were largely responsible for a click-thru rate gain of over 25% on MSN.com. Watch Video
Machine Learning is at the core of data science and we see it’s applications all over now (i.e. recommender engines, etc.). As Pedro Domingos’s Professor of Computer Science U. Washington writes in the piece, “In reality, the main purpose of machine learning is to predict the future.” It’s important to be aware of the MYTHs associated with Machine Learning. Read More
In 2013, Airbnb had a small, centralized team of five data scientists serving the data needs of the company. Since then, they have grown to become one of the largest, most innovative startup teams with over 70 data scientists now serving separate business units. In addition to setting a consistently high bar on new hires and focusing on technical mentorship from peers, the structure of the organization has been key to successful growth. Read More
Kaggle is a community of almost 450K data scientists who have built nearly 2 million machine learning models to participate in its competitions. Data scientists come to Kaggle to learn, collaborate and develop the state of the art in machine learning. This talk will cover some of the lessons on winning techniques we have learned from the Kaggle community. Watch Video