What Types of Questions Can Data Science Answer
Data science has enabled us to solve complex and diverse problems by using machine learning and statistic algorithms. Here we have enumerated the common applications of supervised, unsupervised and reinforcement learning techniques
on Sep 29, 2015 in Data Science, Use Cases
Data Lake vs Data Warehouse: Key Differences
We hear lot about the data lakes these days, and many are arguing that a data lake is same as a data warehouse. But in reality, they are both optimized for different purposes, and the goal is to use each one for what they were designed to do.
on Sep 29, 2015 in Data Lake, Data Warehouse, SAS, Tamara Dull
The 123 Most Influential People in Data Science
We used LittleBird algorithm to build a true Data Science influencer network by measuring how often influencers retweet other influencers. Top influencers include @hmason, @kdnuggets, @kaggle, @peteskomoroch, @mrogati, and @KirkDBorne.
on Sep 15, 2015 in About KDnuggets, Alex Salkever, Big Data Influencers, Data Science, Hilary Mason, Influencers, Kaggle, Kirk D. Borne, Silk.co
Big Data Monetization Lessons from Zillow
In the current tsunami of “Big Data” every business wants to get value out of the data. Here, we are sharing lessons learned by the new real estate websites who have brought together Big Data sets, home buyers, and home sellers.
on Sep 14, 2015 in Big Data, Data Monetization, Maps, Monetizing, Russell Walker, Zillow
Data Science Data Architecture
Data scientists are kind of a rare breed, who juggles between data science, business and IT. But, they do understand less IT than an IT person and understands less business than a business person. Which demands a specific workflow and data architecture.
on Sep 10, 2015 in Big Data Architecture, Data Management, Data Science, Olav Laudy
How to Balance the Five Analytic Dimensions
When developing a solution one has to consider data complexity, speed, analytic complexity, accuracy & precision, and data size. It is not possible to best in all categories, but it is necessary to understand the trade-offs.
on Sep 3, 2015 in Accuracy, Complexity, Precision
The one language a Data Scientist must master
Getting started with the data science, and wondering which language to pick up and technology to explore. But, that is secondary, every business is structured differently and to understand it and build on top of it, is the crux of data science.
on Sep 1, 2015 in Matt Reaney, Programming Languages, Python vs R
|
| 