From PyData Amsterdam, April 2017. Giovanni Lanzani gives a talk on the Data Science Process and where things can go wrong.
“Edward”: A library for probabilistic modeling, inference, and criticism Abstract https://www.meetup.com/NYC-Machine-Learning/events/236943279/ http://www.zentation.com/viewer2/webcast/NAPNgdDUBF/Dustin-Tran—Spotify-Talk-(2017-01-19) Probabilistic modeling is a powerful approach for analyzing empirical information. In this talk, I will provide an overview of Edward, a software library for probabilistic modeling. Formally, Edward is a probabilistic programming system built on computational graphs,supporting compositions of both models and inference […]
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…
The challenge of data modeling is to understand how to work with complex data in order to standardize, structure and optimize data to gets accurate insights quickly. Watch this video by Product Manager, Evan Castle of Sisence, to learn everything you need to know about data modeling. Watch Video
Booz Allen Hamilton has created a wonderful 114 page FREE ebook (pdf) called “The Field Guild to Data Science”. I recommend you give it a look see for the latest on what is going on in the space. READ MORE
Preparing data for analysis can incredibly time consuming. In this talk at the Open Data Science Conference in SF last week Nina Zumel & John Mount from Win-Vector, LLC with “Preparing Data for Analysis using R” Workshop. READ MORE
Our friends at Data Science Central have a large set of Machine Learning/Data Science Resources and Articles. Take a look Read More
Amid the big data boom, the in-memory database market will enjoy a 43 percent compound annual growth rate (CAGR) – leaping from $2.21 billion in 2013 to $13.23 billion in 2018, predicts Markets and Markets, a global research firm Gartner.
What’s driving that demand? Simply put, in-memory databases allow real-time analytics and situation awareness on “live” transaction data – rather than after-the-fact analysis on “stale data,” notes a recent Gartner market guide. Here are 19 in-memory database options mentioned in that Gartner market guide. Read More
SPOTLIGHT | Read about Domino Data Labs and how its platform can both vastly improve your data science team’s speed and capability converting data models to production code, but also save you big time on expensive engineers in short supply…more! Read More
Mode’s public side has been called the “Github for Data Science”, especially for sharing open source SQL queries and data sets. The corporate version has been a hit with fast growing, data-centric companies who prefer MODE over building their own analytics tools.
Read about the NEW Mode workflow and features launched this weekend and how data analysts are using it READ MORE