Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.
The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.
Do you make any new year resolutions? Hit the gym more often? Lose that last 10 pounds? While personal resolutions often get a bad rap, setting professional goals at the start of the new year is not necessarily a bad idea. Check out one data scientist's new year resolutions for 2017.
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.
My exclusive interview with rock star Data Scientist Jeremy Howard, on his latest Deep Learning course, what is needed for success in Kaggle, how Enlitic is transforming medical diagnostics, and what Data Scientists should do to create value for their organization.
An analysis of NYC Open Data health inspections showing that craft beer bar kitchens in Manhattan are cleaner than the average establishment by a statistically significant margin. An encouraging finding for Dry January.
Big data craze inspires firms to save every possible bit of data, with the misconception that the more data you have, the better. Firms must keep data (for compliance purposes) or often aren’t sure what information they need to keep. This post looks at alternative data sources.
When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.
A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.
Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.
Data scientists at Foodpairing help brands cut down on the fuzzy front end of product development. The so-called Consumer Flavor Intelligence combines internet data and food science to create timely flavor line extensions.
Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.
To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.
We examine what Uber has done that drives success in many markets across the globe and why so many businesses are seeking an Uber-style solution to their business. We present a listing of lessons on what to do if you are seeking to Uber-ize your business model.