RSS Featured Blog Posts
  • An overview of feature selection strategies
    Introduction Feature selection and engineering are the most important factors which affect the success of predictive modeling. This remains true even today despite the success of deep learning, which comes with automatic feature engineering. Parsimonious and interpretable models provide simple insights into business problems and therefore they are deemed very valuable. Furthermore, in many occasions […]
    Burak Himmetoglu
  • Helping Non-Profit Organizations as a Data Scientist
    Data Scientists are considered to be highly technical professionals and are typically seen exercising their talent in conventional business industries. However, Data Science is a problem-solving field. Therefore, it can be applied in any field that uses set of data and determines patterns to make decisions. For this reason, Data Scientists have the ability to […]
    VAMSI NELLUTLA
  • Free Book: Introduction to Statistics
    Online Statistics Education: A Multimedia Course of Study.  Project Leader: David M. Lane, Rice University. Content: Introduction Graphing Distributions Summarizing Distributions Describing Bivariate Data Probability Research Design Normal Distributions Advanced Graphs Sampling…
    Capri Granville
  • Introduction to Deep Learning
    Guest blog post by Zied HY. Zied is Senior Data Scientist at Capgemini Consulting. He is specialized in building predictive models utilizing both traditional statistical methods (Generalized Linear Models, Mixed Effects Models, Ridge, Lasso, etc.) and modern machine learning techniques (XGBoost, Random Forests, Kernel Methods, neural networks, etc.).…
    Vincent Granville
  • The Fourth Way to Practice Data Science – Purpose Built Analytic Modules
    Summary:  Purpose Built Analytic Modules (PBAMs) such as those for Fraud Detection represent a fourth way to practice data science, a new model for the good use of Citizen Data Scientists, and a new market for AI-first companies.   It appears that data science has…
    William Vorhies

Data Scientist Interviews

What data scientist’s do all day at work

Ram Narasimhan of GE talks about the importance of curiosity and what makes his day

“What I do as a Data Scientist” Dan Mallinger

Excerpt from Data Scientist Interviews “I’m a data scientist with degrees in mathematical sciences and organizational psychology; I also have significant academic training in computer science and sociology. I’ve spent my career in statistics, analytics, and technology roles but almost entirely under business groups, which has framed much of my professional outlook. Today, I am the Director of Data Science for Think Big and have been with the company for four years.”

Interview: Michael Brodie – We Can’t Rely on Machines

Excerpt from Data Scientist Interviews “So yes, there is a lot of hype?But I actually think it is far more profound and powerful than most people are conceiving it at the moment. It has already changed a very large number of operating processes in health care, manufacturing, marketing and stock markets. How-ever, it is not as widely used as one might think. Big Data and Big Data Analytics are in their infancy with respect to operational deployment and our understanding of it.”

Crushed it! Landing a data science job by Erin Shellman

“Data science interviews are the worst because data science is interdisciplinary: code for “you have to know everything about all the disciplines.”  Depending on the company and the team, your interview might look like a software developer’s interview, or it might look a like a statistician’s interview, and the bad news is that virtually none of the material overlaps.  I recently spent a ton of time studying for interviews and I’ve got some hot tips to pass along if you’re thinking about a move soon.”

 

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