Scala World 2016 took place in the UK in September – Here Martin Odersky, the creator of the Scala programming language gives the keynote. And link to other videos Read More
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
Renee (Teate) just got back from PydataDC, where she gave this presentation on “Becoming A Data Scientist”, which intends to summarize and share what she has learned from her podcast series of 13 interviews with Data Scientists in the field. Read More
Renee Teate Interviews Debbie Berebichez, Chief Data Scientist of Metis for the “Becoming A Data Scientist” series. Watch Interview
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
Video (33:41) with Databricks co-founder and CTO Matei Zaharia presenting the changes in Apache Spark 2.0 and the general availability (GA) of Databricks Community Edition at Spark Summit 2016. Afterwards, Michael Armbrust demos some new features found in Spark 2.0 on Databricks Community EditionGo To Video
Take a look! Here are our Top 10 New York-based Data Science roles @ our clients.
Our Clients are a set of fantastic companies with established data science teams providing an opportunity work on big challenges, make an impact, and grow. See More
Open source software tools have become all the rage, especially around big data and that is a GOOD thing. It allows for many players to work off of the same code base to build more add-on tools and it’s cheap and easy for the masses to get set up and use them. Hadoop, R, Cassandra, Mongo DB, Neo4i and HBase are among the most popular, but there are many more.
I have accumulated 3 lists that are very popular. Please let me know if you see things missing and I’ll attempt to create one large master list and post it on the site. Read More…
Big Data Engineers! A ton of video talks from last months “Scala Days – Berlin 2016” went up recently. Take a look. Watch Videos
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