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[ COVER OF THE WEEK ]
Data Mining Source
[ AnalyticsWeek BYTES]
>> The What and Where of Big Data: A Data Definition Framework by bobehayes
>> Do Attitudes Predict Behavior? by analyticsweek
>> Discussing #InfoSec with @travturn @hrbrmstr @thebeareconomist @yaxa_io – Playcast â Data Analytics Leadership Playbook Podcast by v1shal
[ FEATURED COURSE]
Process Mining: Data science in Action
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[ FEATURED READ]
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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[ 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:Explain Tuftes concept of ‘chart junk’?
A: All visuals elements in charts and graphs that are not necessary to comprehend the information represented, or that distract the viewer from this information
Examples of unnecessary elements include:
– Unnecessary text
– Heavy or dark grid lines
– Ornamented chart axes
– Pictures
– Background
– Unnecessary dimensions
– Elements depicted out of scale to one another
– 3-D simulations in line or bar charts
Source
[ VIDEO OF THE WEEK]
Subscribe to Youtube
[ QUOTE OF THE WEEK]
It’s easy to lie with statistics. It’s hard to tell the truth without statistics. Andrejs Dunkels
[ PODCAST OF THE WEEK]
@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast
Subscribe
[ FACT OF THE WEEK]
Every person in the US tweeting three tweets per minute for 26,976 years.