[ COVER OF THE WEEK ]
Data interpretation Source
[ LOCAL EVENTS & SESSIONS]
- Sep 25, 2018 #WEB [Webinar] OrÃgenes de la tecnologÃa Blockchain: Bitcoin
- Sep 16, 2018 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Portland, OR
- Sep 16, 2018 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Orange County, CA
[ AnalyticsWeek BYTES]
[ NEWS BYTES]
[ FEATURED COURSE]
[ FEATURED READ]
Illuminating and very timely . . . a fascinating and sometimes alarming survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more
[ TIPS & TRICKS OF THE WEEK]
Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.
[ DATA SCIENCE Q&A]
Q:What is the difference between supervised learning and unsupervised learning? Give concrete examples
A: * Supervised learning: inferring a function from labeled training data
* Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
* Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
* Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
* Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
* Unsupervised learning: we lack a response variable that can supervise our analysis
* Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
* Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.
[ VIDEO OF THE WEEK]
Subscribe to Youtube
[ QUOTE OF THE WEEK]
Numbers have an important story to tell. They rely on you to give them a voice. Stephen Few
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.