RSS Featured Blog Posts
  • Weekly Digest, October 22
    Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. Announcement…
    Vincent Granville
  • Embracing Conflict to Fuel Digital Innovation
    When talking to clients about their business goals, most business executives are pretty clear as to what they want to accomplish, such as reducing customer churn or reducing inventory costs or improving quality of care or improving product line profitability.  But these “one dimensional” business initiatives really don’t push the organization’s innovative thinking.  For example, […]
    Bill Schmarzo
  • How to Write an R Function to Match and Merge 2 Files (like VLOOKUP)
    Matching and merging 2 files is task I find myself doing all of the time. Historically, I've used VLOOKUP in MS Excel and just worked around any limitations. Finally, I bit the bullet and wrote an R Function that does the trick faster, and with more flexibility. …
    Ray Hall
  • R vs Python: Usability, Popularity, Pros & Cons, Jobs, and Salaries
    This article was written by Tanmoy Ray If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, […]
    Andrea Manero-Bastin
  • The Rise of Artificial Intelligence in Enterprise
    Depending on what news headline you have read, you may have perceived an Artificial Intelligence (AI) system as either an Alexa or Siri assistant that understands all your commands, a deep learning system that can recognize dog or a cat from image, a system that recommends personalized medicine, or an intelligent, overpowering machine that can […]
    Vimal N Suba

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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