[ COVER OF THE WEEK ]
Statistically Significant Source
[ AnalyticsWeek BYTES]
>> The Big List: 80 Of The Hottest SEO, Social Media & Digital Analytics Tools For Marketers by analyticsweekpick
>> Big Data – What it Really Means for VoC and Customer Experience Professionals by bobehayes
>> Your Relative Performance: A Better Predictor of Employee Turnover by bobehayes
[ NEWS BYTES]
>>
An Inconvenient Truth: 93% of Customer Experience Initiatives are Failing⦠– Customer Think Under Customer Experience
>>
Logility acquires Halo Business Intelligence to expand advanced … – Logistics Management Under Prescriptive Analytics
>>
Apache Hadoop 3.0 goes GA, adds hooks for cloud and GPUs – TechTarget Under Hadoop
[ FEATURED COURSE]
Pattern Discovery in Data Mining
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[ FEATURED READ]
The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t
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[ 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 better: good data or good models? And how do you define ‘good? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasnt of importance, organizations wouldnt spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important
How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets
Is there a universal good model?
– No, otherwise there wouldnt be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis
Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model
Source
[ VIDEO OF THE WEEK]
@AnalyticsWeek: Big Data at Work: Paul Sonderegger
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[ QUOTE OF THE WEEK]
It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to
[ PODCAST OF THE WEEK]
#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA
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[ FACT OF THE WEEK]
According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.