[  COVER OF THE WEEK ]

[ AnalyticsWeek BYTES]

>> The Increasing Influence of Cloud Computing by Jelani Harper

>> Remote DBA Experts- Improve Business Intelligence with The Perfect Analytical Experts by Sujain Thomas

>> Which Machine Learning to use? A #cheatsheet by Vishal Kumar

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[ NEWS BYTES]

>>  AR, IoT, Siri and machine learning: Will iPhone X showcase changes to the user experience? – ZDNet Under  IOT

>>  Pi or production: Choosing a best-fit IoT gateway prototype – TechTarget (blog) Under  IOT

>>  What Fast Forward Labs Brings Cloudera – Datanami Under  Data Scientist

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[ FEATURED COURSE]

Applied Data Science: An Introduction

As the world's data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data sets—known as big data.... more

[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

If you are looking for a book to help you understand how the machine learning algorithms "Random Forest" and "Decision Trees" work behind the scenes, then this is a good book for you. Those two algorithms are commonly u... more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today's analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:How do you take millions of users with 100’s transactions each, amongst 10k’s of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight) * Transactions by date * Count of customers Vs number of items bought * Total items Vs total basket per customer * Total items Vs total basket per area 2.Create new features (per customer): Counts: * Total baskets (unique days) * Total items * Total spent * Unique product id Distributions: * Items per basket * Spent per basket * Product id per basket * Duration between visits * Product preferences: proportion of items per product cat per basket 3. Too many features, dimension-reduction? PCA? 4. Clustering: * PCA 5. Interpreting model fit * View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1. * Interpret each principal component regarding the linear combination it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)
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[ VIDEO OF THE WEEK]

 @AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

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[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

 Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

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[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

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