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Three Problems All Data Scientists Experience

Recently, Mike Kim wrote an excellent post on overfitting, a common problem data scientists face when applying machine learning. Earlier, Paul Yacci and Aaron Sander [SM1] both tackled the topics of feature selection and feature creation. These are all key problems that data scientists encounter when building models that seek to describe and predict real-world outcomes based on observed data or hidden patterns in datasets.




When Data Scientists Visit: Booz Allen Hamilton and Kaggle Data Scientists Visit Hatfield Marine Center

On January 15-16, 2015, Hatfield Marine Science Center researchers hosted data scientists from Booz Allen Hamilton and Kaggle for a site visit. 

Day in the Life of a Data Scientist

Data Science was named Forbes Magazine's sexiest profession of 2014 as well as being the most trending STEM career in Ebony Magazine’s July issue. This has led to many wondering how they, too, can enter the data science profession. So, what is a typical day in the life of a data scientist? 

Going Beyond in Data Science

Being a data scientist is more than having a technical background; it's also about going beyond your tools and understanding what it really means to tackle complex data analysis problems. No matter if you are a seasoned big data expert or are just considering moving into the field, here are five things you ought to know. 

Feature Selection

Feature Selection is crucial to any model construction in data science. Focusing on the most important, relevant features will help any data scientist design a better model and accelerate outcomes.  

What should I do to be a Data Scientist?

I'm often asked "what are good things to do to get involved with data science?" In this post I'll share with you some key activities you can get started with today.  Whether it is photos on phones, links in a social network, or information around health, our society has an increased appreciation and enthusiasm about data. It's an exciting time as open data initiatives are met with open software tools that enable large-scale analysis. To actually get insight from data requires intellectual curiosity and a mix of skills and knowledge.

Support Vector Machines in Data Science

Support Vector Machines (SVMs) may not be as popular as Neural Networks within data science, but they act as powerful, useful algorithms. One of the difficulties of SVMs has been the computational effort required to train them. However LIBSVM, which has been used for over a decade, can fairly easily handle the 30,000 training points in the National Data Science Bowl competition’s data set. That makes SVMs a viable tool for you to use both in general, and for the purpose of competing in the National Data Science Bowl.  

Our Journey to an Analytics-Driven Culture

There has been a lot of news coverage lately around the topic of creating a data-driven culture within an organization. The fact of the matter is a data-driven culture is crippling. We tried to create a data-driven culture too, but ultimately found that our real transformation came by using data as inputs into a real Analytics driven-culture. A culture that values true experimentation, understands failure is the price of discovery, and actually makes use of analytic outputs for decision-making. 

Matt Barrett on the National Data Science Bowl

Modern computing has no shortage of tools for the data scientist. The open source community alters the landscape every six to 12 months, and competition keeps you on the bleeding edge. In my career as a data scientist, I use everything from scientific Python™ packages to the newest cloud computing architectures—and sometimes all within the same project, as the initial stages of data exploration and mining are often done in a different language than the final product implementation.
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