[ COVER OF THE WEEK ]
[ LOCAL EVENTS & SESSIONS]
- Jan 24, 2019 #WEB Webinar: A Buyer’s Journey in SaaS World by DELL Principal PM
- Jan 06, 2019 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Amsterdam, Netherlands
- Jan 06, 2019 #WEB Free Webinar on Big Data with Scala & Spark – Live Instructor Led Session | Limited Seats | Newark, NJ
[ AnalyticsWeek BYTES]
[ NEWS BYTES]
[ FEATURED COURSE]
[ FEATURED READ]
In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more
[ TIPS & TRICKS OF THE WEEK]
Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.
[ DATA SCIENCE Q&A]
Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: cherry picking, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it
Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as its based on most HIV negative
-Prevalence estimates will be unaccurate
[ VIDEO OF THE WEEK]
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[ QUOTE OF THE WEEK]
What we have is a data glut. Vernon Vinge
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.