[ COVER OF THE WEEK ]
[ AnalyticsWeek BYTES]
>> 2018 Trends in Machine Learning: Intelligent Decision-Making by jelaniharper
>> A Big Data App That Helps You Find A Parking Spot by analyticsweekpick
>> When Buying a Company, Use Customer Feedback to Improve Due Diligence by bobehayes
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[ NEWS BYTES]
Cybersecurity trends for 2018 – CSO Online Under cyber security
Ways Artificial Intelligence Can Empower Field Service And Enhance Customer Experience – Forbes Under Customer Experience
Data software partners copycat AWS customer game – SiliconANGLE News (blog) Under Big Data Analytics
More NEWS ? Click Here
[ FEATURED COURSE]
Intro to Machine Learning
[ FEATURED READ]
Data Science from Scratch: First Principles with Python
[ TIPS & TRICKS OF THE WEEK]
Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.
[ 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
- 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 can’t 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
- 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.
[ WORK WITH TAO]