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

We noticed your search to hire data scientists, and no doubt you realize there is more demand for this expertise than the available pool of talent. In addition, while the practice of data science is not new, the professionals calling themselves data scientists come from diverse backgrounds, differing levels of education and experience. Some will argue that econometrics is the best background for a data scientist, while others view statistics with computer science skills, or teach yourself programming it’s the creative mindset applied to business and knowledge of mathematical applications that are the most brilliant. Suffice it to say, data science has multiple approaches to solve a problem, and combined training in the discipline of econometrics, mathematics and statistics with computer science chops and field experience is going to provide a candidate with the best approach to solving your business problems over and over again. So, let’s agree it’s a strategic hire for your organization.

The Data Science Beagle has teamed up with our friends at Divergence Academy, and offers several programs available to assist you in your search, either fee based, contingent or sponsorship.

 

 

 

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