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[ COVER OF THE WEEK ]
Complex data Source
[ LOCAL EVENTS & SESSIONS]
- Sep 05, 2019 #WEB Thinkful Webinar | Becoming a Data Analyst Info Session
- Aug 20, 2019 #WEB Call for presenters: Government Advances Statistical Programming (GASP!) Workshp
- Aug 24, 2019 #WEB AWS Certified Solutions Architect – Associate Study Group
[ AnalyticsWeek BYTES]
>> The real-time machine for every business: Big data-driven market analytics by thomassujain
>> Data Virtualization: A Spectrum of Approaches â A GigaOm Market Landscape Report by analyticsweekpick
>> An Agile Approach to Big Data Management: Intelligent Workload Routing Automation by jelaniharper
[ FEATURED COURSE]
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[ FEATURED READ]
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
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[ TIPS & TRICKS OF THE WEEK]
Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.
[ DATA SCIENCE Q&A]
Q:What is better: good data or good models? And how do you define ‘good? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasnt of importance, organizations wouldnt spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important
How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets
Is there a universal good model?
– No, otherwise there wouldnt be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis
Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model
Source
[ VIDEO OF THE WEEK]
@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast
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[ QUOTE OF THE WEEK]
Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao
[ PODCAST OF THE WEEK]
#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS
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[ FACT OF THE WEEK]
The largest AT&T database boasts titles including the largest volume of data in one unique database (312 terabytes) and the second largest number of rows in a unique database (1.9 trillion), which comprises AT&T’s extensive calling records.
