Sep 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Complex data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> On Ingesting Kate Crawford’s “The Trouble with Bias” by analyticsweek

>> Customer Loyalty 2.0 Article in Quirk’s Marketing Research Review by bobehayes

>> Traditional Retailers, It’s 15 Minutes to midnight. How to Strike Back at Your Online Competitors. #Retail Transformation, #Retail Analytics by tony

Wanna write? Click Here

[ NEWS BYTES]

>>
 Mass Incarceration’s Complex Statistics – CityLab Under  Statistics

>>
 Most hospitals won’t reach HIMSS Analytics Stage 7 until 2035, study finds – Becker’s Hospital Review Under  Analytics

>>
 Elder abuse statistics are state’s worst for the ‘quiet epidemic’ – Staunton News Leader Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

image

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]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

image

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 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)

Source

[ VIDEO OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

Subscribe 

iTunes  GooglePlay

[ 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.

Sourced from: Analytics.CLUB #WEB Newsletter

Leave a Reply

Your email address will not be published. Required fields are marked *