[ COVER OF THE WEEK ]
Complex data Source
[ LOCAL EVENTS & SESSIONS]
- Sep 16, 2018 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Hyderabad, India
- Sep 16, 2018 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Anaheim, CA
- Sep 12, 2018 #WEB Dubai Workshop – Develop a Successful AI Startup Company Today!
[ AnalyticsWeek BYTES]
[ NEWS BYTES]
[ FEATURED COURSE]
Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more
[ FEATURED READ]
Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… more
[ TIPS & TRICKS OF THE WEEK]
Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.
[ DATA SCIENCE Q&A]
Q:How do you take millions of users with 100s transactions each, amongst 10ks 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):
* Total baskets (unique days)
* Total items
* Total spent
* Unique product id
* 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?
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 its obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)
[ VIDEO OF THE WEEK]
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[ QUOTE OF THE WEEK]
Big Data is not the new oil. – Jer Thorp
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.