[ COVER OF THE WEEK ]
[ LOCAL EVENTS & SESSIONS]
- Jul 12, 2018 #WEB Webinar – What is Machine learning? & How to get yourself into Machine Learning?
- Jun 09, 2018 #WEB Machine Learning & Data Science Training
- Jun 26, 2018 #WEB Project Management Professional (PMP) 4-days Classroom in Saint Paul
[ AnalyticsWeek BYTES]
[ NEWS BYTES]
[ FEATURED COURSE]
[ FEATURED READ]
What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… 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: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.
[ VIDEO OF THE WEEK]
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[ QUOTE OF THE WEEK]
Information is the oil of the 21st century, and analytics is the combustion engine. Peter Sondergaard
[ PODCAST OF THE WEEK]
[ FACT OF THE WEEK]
Every person in the world having more than 215m high-resolution MRI scans a day.