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[ COVER OF THE WEEK ]
Trust the data Source
[ LOCAL EVENTS & SESSIONS]
- Aug 12, 2019 #WEB Hadoop vs Spark – Demystifying the Difference (Online Webinar)
- Sep 10, 2019 #WEB Webinar – Introduction to Power BI for Business Professionals
- Aug 27, 2019 #WEB IndyPy Bytes: From Tkinter to Bottle
[ AnalyticsWeek BYTES]
>> The 4 Common Challenges of Predictive Analytics by analyticsweek
>> Inside CXM: New Global Thought Leader Hub for Customer Experience Professionals by bobehayes
>> What Motivates People to Take Free Surveys? by analyticsweek
[ FEATURED COURSE]
Deep Learning Prerequisites: The Numpy Stack in Python
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[ FEATURED READ]
Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition
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[ TIPS & TRICKS OF THE WEEK]
Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.
[ DATA SCIENCE Q&A]
Q:How frequently an algorithm must be updated?
A: You want to update an algorithm when:
– You want the model to evolve as data streams through infrastructure
– The underlying data source is changing
– Example: a retail store model that remains accurate as the business grows
– Dealing with non-stationarity
Some options:
– Incremental algorithms: the model is updated every time it sees a new training example
Note: simple, you always have an up-to-date model but you cant incorporate data to different degrees.
Sometimes mandatory: when data must be discarded once seen (privacy)
– Periodic re-training in batch mode: simply buffer the relevant data and update the model every-so-often
Note: more decisions and more complex implementations
How frequently?
– Is the sacrifice worth it?
– Data horizon: how quickly do you need the most recent training example to be part of your model?
– Data obsolescence: how long does it take before data is irrelevant to the model? Are some older instances
more relevant than the newer ones?
Economics: generally, newer instances are more relevant than older ones. However, data from the same month, quarter or year of the last year can be more relevant than the same periods of the current year. In a recession period: data from previous recessions can be more relevant than newer data from different economic cycles.
Source
[ VIDEO OF THE WEEK]
Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast
Subscribe to Youtube
[ QUOTE OF THE WEEK]
Torture the data, and it will confess to anything. Ronald Coase
[ PODCAST OF THE WEEK]
#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership
Subscribe
[ FACT OF THE WEEK]
Bad data or poor data quality costs US businesses $600 billion annually.