[ COVER OF THE WEEK ]
Fake data Source
[ LOCAL EVENTS & SESSIONS]
- Aug 08, 2019 #WEB Thinkful Webinar | Getting Started in Data Science
- Aug 14, 2019 #WEB Intro to Python: Fundamentals
- Aug 10, 2019 #WEB Google AI Workshop: Machine learning with Tensorflow
[ AnalyticsWeek BYTES]
[ FEATURED COURSE]
[ FEATURED READ]
Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… more
[ TIPS & TRICKS OF THE WEEK]
Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.
[ 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]
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[ QUOTE OF THE WEEK]
The world is one big data problem. – Andrew McAfee
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income.