NYU Data Science newsletter – June 9, 2015

GROUP CURATION: N/A

… That core remains in place and should be the foundation of a competitive team going forward but, after a first-round exit, the Isles also ought to recognize that further improvement is required.

Artificial Intelligence might be the most exciting field humans ever worked on and stimulates people across the board. Part of its fascination surely comes from the fact that artificial intelligence is somewhat mysterious and therefore allows for opinions and speculations. “Is a human-level AI really possible in the near future?”, “How could a computer even think?” and “Are we humans unique with our emotions and consciousness?” are some FAQs concerning artificial intelligence.

Changes and breakthroughs are coming fast these days and a lot of what is commonly known as impossible has already be done or does not play a role at all. So what’s really in the way to a human-level AI? Let’s start easy.

I’m about to graduate very soon with a master’s in physics, but I’m interested in an entry-level data scientist or quant role using lots of stats/machine learning. However, I’ve gotten very few callbacks. I also just received a call from a recruiter for a software engineer position. I told them I wanted to apply to the data scientist position instead, but they told me that role needs 5+ yrs of experience and also experience working with very large data sets, which I lack. A similar response occurred from another company that called me last week

I’ve worked on some personal projects using R and Python, just doing basic stats/machine learning. I’ve also taken a course in Bayesian stats and worked on a project using MCMC. I know the basics of ML techniques such as random forest, SVM, K-means clustering, Collaborative Filtering, etc. I also just learned python. I would say I’m strongest with C++ since I use it for my Master’s thesis project

So how can I get some practice working on very large data sets? Or should I just give up on data scientist roles, and look for data analyst roles instead?

Over the past few months, I became increasingly aware that I wasn’t be as open with my research as I could be. Sure, all my articles are openly available, including preprints of some of my work. But I also advocate for sharing code and data, and until now, I hadn’t done either. That changes today.

Want my data? You can have it. Want my code? You can have that, too. Want step-by-step instructions on how to analyze my data and reproduce the tables and figures in my manuscripts? You got it.

Libraries are proving that they’re more than mausoleums of old knowledge — they’re in a state of progressive reform, rethinking services and restructuring with data.

The United States government is a hacker’s paradise.

The Obama administration announced last week that hackers had stolen the personal information of more than four million past and present federal employees from the Office of Personnel Management. Analysts estimate that the data breach might affect roughly one percent of all Americans; it has already been described by the New York Times as the largest breach of federal data in history.

Cloudera, the leader in enterprise analytic data management powered by Apache Hadoop™, today announced Daniel Sturman’s appointment as Vice President, Engineering. A key engineering leader for some of the world’s biggest and “baddest” computer systems, Daniel Sturman moves over from Google to join Cloudera’s senior executive team to lead all development within the company, with the Apache Software community, and with Cloudera’s 1600+ global partners.

Anthony is a Senior Research Scientist with the Polar Science Center at the Applied Physics Laboratory, where he conducts research on the response of glaciers and ice sheets to changing climate. Anthony joined the eScience Institute in June 2015 and provides expertise in relational databases, geospatial data analytics, and development of lightweight cloud computing solutions for scientific research.

Bernease was most recently a Software Development Engineer at Amazon where she collaborated with operations research scientists and statisticians to add economic constraints and buying models to Amazon’s Inventory Planning and Control system. Previous to Amazon, Bernease worked on derivatives pricing and predictive modeling at the research arm of Morgan Stanley. Bernease earned her BS in Mathematics and Statistics from the University of Michigan.