[ COVER OF THE WEEK ]
Accuracy check Source
[ LOCAL EVENTS & SESSIONS]
- Nov 16, 2017 #WEB [Webinar] Errores que todos cometemos en proyectos Big Data
- Nov 14, 2017 #WEB Webinar – Story Telling Using the Latest Features from Microsoft Power BI
- Nov 22, 2017 #WEB SEO Insights & Analytics toâBoostâyourâRanking (End of year event)
[ AnalyticsWeek BYTES]
[ NEWS BYTES]
[ FEATURED COURSE]
Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more
[ FEATURED READ]
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… more
[ TIPS & TRICKS OF THE WEEK]
Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.
[ DATA SCIENCE Q&A]
Q:What is statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: how many times do I need to flip a coin to conclude it is biased?
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis
[ VIDEO OF THE WEEK]
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[ QUOTE OF THE WEEK]
You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. Alvin Tof
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
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