[ COVER OF THE WEEK ]
Tour of Accounting Source
[ LOCAL EVENTS & SESSIONS]
- Nov 17, 2018 #WEB Machine Learning & Data Science Training
- Dec 13, 2018 #WEB When not to use Blockchain? Evaluating Suitability of Applying Blockchain.
- Nov 20, 2018 #WEB Data-driven Marketing Blueprint for Real Estate: Workshop & Discussion
[ AnalyticsWeek BYTES]
[ NEWS BYTES]
[ FEATURED COURSE]
[ FEATURED READ]
This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … more
[ TIPS & TRICKS OF THE WEEK]
Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.
[ 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]
Without big data, you are blind and deaf and in the middle of a freeway. Geoffrey Moore
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.