Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
A Refresher on Continuous Versus Discrete Input Variables
 How many times have I heard that the most...
Podcast: Why Deep Learning Could Expedite the Next AI Winter
  Welcome to the next episode of The Machine Learning...
PAW Preview Video: Evan Wimpey, Director of Strategic Analytics at Elder Research
 In anticipation of his upcoming presentation at Deep Learning...

Data scientist

Balance the Five Analytic Dimensions

 So many data scientists select an analytic technique in hopes of achieving a magical solution, but in the end, the solution simply may not even be possible due to other limiting factors. It is important for organizations working with analytic capabilities to understand the various constraints of implementation most real-world applications will encounter. When developing

Key Traits that Every Data Scientist Needs

 In the past decade, Big Data solutions have become the most impactful breakthrough in the data science industry. There has never been a better time to pursue a data career to help organizations wade through the Big...

The Making of a Modern Data Scientist

 The world is churning out 2.5 quintillion bytes of data every day, with 90% of it created in the past two years. Most of this staggering volume comes from apps, social media sites, YouTube and other video...

Stop Hiring Data Scientists Until You’re Ready for Data Science

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Analytics 2015: ‘BI is dead: Predictive is the new black’

 2015 will be the year that predictive analytics obtains significant market gains in everything from the industrial Internet to consumer devices. Here’s what Simon Arkell, CEO, Predixion Software, expects predictive analytics to look like in 2015: Predictive...

Connecting the Experts with the Data Scientists

 “Can Machines Think?” was the cover of Time magazine a generation ago. The impetus was a face-off between world chess champion Gary Kasparov and IBM’s “Deep Blue” chess machine. The first match was won by us humans,...

It’s Already Time to Kill the “Data Scientist” Title

 What does it mean today to say your are—or want to be, or want to hire—a “data scientist?” Not much, unfortunately. The job title has almost as much ambiguity as the term “Big Data.” If you really...

Data Scientist: Evolution of the Business Analyst

 The business/data analyst role is evolving into a new role due in part to the new technology of big data. The data scientist role has emerged because of the increase in breadth and depth of data being...

“Data Scientist” catches “Statistician”, surpasses “Data Miner”

 The rapidly rising term “Data Scientist” caught up with “Statistician” and surpassed “Data Miner” on Google Trends. However, Statistics remains a lot more popular than “Data Science”, which begs the question: What do Data Scientists do? Clearly,...

Python Displacing R As The Programming Language For Data Science

  R remains popular with the PhDs of data science, but as data moves mainstream, Python is taking over. While R has traditionally been the programming language of choice for data scientists, it is quickly ceding ground...

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