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[ COVER OF THE WEEK ]
Ethics Source
[ AnalyticsWeek BYTES]
>> How The Guardianâs Ophan analytics engine helps editors make better decisions by analyticsweekpick
>> May 23, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin
>> Data Matching with Different Regional Data Sets by analyticsweekpick
[ FEATURED COURSE]
Applied Data Science: An Introduction
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[ FEATURED READ]
Data Science from Scratch: First Principles with Python
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[ TIPS & TRICKS OF THE WEEK]
Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.
[ DATA SCIENCE Q&A]
Q:Provide a simple example of how an experimental design can help answer a question about behavior. How does experimental data contrast with observational data?
A: * You are researching the effect of music-listening on studying efficiency
* You might divide your subjects into two groups: one would listen to music and the other (control group) wouldnt listen anything!
* You give them a test
* Then, you compare grades between the two groups
Differences between observational and experimental data:
– Observational data: measures the characteristics of a population by studying individuals in a sample, but doesnt attempt to manipulate or influence the variables of interest
– Experimental data: applies a treatment to individuals and attempts to isolate the effects of the treatment on a response variable
Observational data: find 100 women age 30 of which 50 have been smoking a pack a day for 10 years while the other have been smoke free for 10 years. Measure lung capacity for each of the 100 women. Analyze, interpret and draw conclusions from data.
Experimental data: find 100 women age 20 who dont currently smoke. Randomly assign 50 of the 100 women to the smoking treatment and the other 50 to the no smoking treatment. Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke free for 10 years. Measure lung capacity for each of the 100 women.
Analyze, interpret and draw conclusions from data.
Source
[ VIDEO OF THE WEEK]
@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast
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[ QUOTE OF THE WEEK]
Torture the data, and it will confess to anything. Ronald Coase
[ PODCAST OF THE WEEK]
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[ FACT OF THE WEEK]
Decoding the human genome originally took 10 years to process; now it can be achieved in one week.