Tag Archive for algorithms

Finding the Best Machine Learning Model For Your Needs


by Angela Guess Alex Woodie recently wrote for Datanami, “Behind today’s powerful predictive applications are machine learning models that identify patterns in the data. But getting these models trained and tuned is not an easy process. Analytics giant SAS thinks it has a solution with a new offering called Factory Miner that allows you to…

How Google Stays on Top of Search


by Angela Guess Steven Levy of Medium.com recently wrote, “For its entire 17-year history, Google Search has always been evolving, a process that the company often marks with celebratory blog items and an occasional press event. (Though when it comes to quantifying the changes, Google reverts to its usual dogged stinginess — for years, it has described…

The Endless Search for Data Scientist Unicorns


by Angela Guess Elizabeth Dwoskin of The Wall Street Journal recently wrote, “For his Ph.D. in astrophysics, Chris Farrell spent five years mining data from a giant particle accelerator. Now, he spends his days analyzing ratings for Yelp Inc.’s online business-review site. Mr. Farrell, 28 years old, is a data scientist, a job title that…

When Big Data Goes Bad

Bad dog.

by Angela Guess Joshua Klein of Fortune recently wrote, “Big Data and the cloud are putting supercomputer capabilities into everyone’s hands. But what’s getting lost in the mix is that the tools we use to interpret and apply this tidal wave of information often have a fatal flaw. Much of the data analysis we do…

More Data + Simple Algorithms = The Best Data Models


by Angela Guess Garrett Wu of Data Informed recently wrote, “With all the buzz surrounding big data, the data management practitioner is constantly inundated with information regarding big data technologies. After identifying which big data problems an organization must solve, the next step is understanding the advantages and disadvantages of different approaches to address these…

Machine Learning and the NFL

Derrick Harris of GigaOM reports, “When it comes to using data to determine how to build a team or manage a game, the National Football League appears years behind its professional sports brethren.”