Dec 28, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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[ AnalyticsWeek BYTES]

>> Ensuring Data Quality in the Big Data Age by jelaniharper

>> Measurement and Meaning of Customer Loyalty by bobehayes

>> Measuring The Customer Experience Requires Fewer Questions Than You Think by bobehayes

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 Gang statistics for second quarter of 2017 have been compiled by San Bernardino County DA’s Office – Fontana Herald-News Under  Statistics

 53% Of Companies Are Adopting Big Data Analytics – Forbes Under  Big Data Analytics

 BaseHealth gets $8.5M to help providers find ‘invisible patients … – MobiHealthNews Under  Health Analytics

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Intro to Machine Learning


Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most stra… more


The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World


In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more


Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.


Q:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
* Tidy data: (Hadley Wickam paper)
column names are values, not names, e.g. 26-45…
multiple variables are stored in one column, e.g. m1534 (male of 15-34 years’ old age)
variables are stored in both rows and columns, e.g. tmax, tmin in the same column
multiple types of observational units are stored in the same table. e.g, song dataset and rank dataset in the same table
*a single observational unit is stored in multiple tables (can be combined)
* Data-Type constraints: values in a particular column must be of a particular type: integer, numeric, factor, boolean
* Range constraints: number or dates fall within a certain range. They have minimum/maximum permissible values
* Mandatory constraints: certain columns can’t be empty
* Unique constraints: a field must be unique across a dataset: a same person must have a unique SS number
* Set-membership constraints: the values for a columns must come from a set of discrete values or codes: a gender must be female, male
* Regular expression patterns: for example, phone number may be required to have the pattern: (999)999-9999
* Misspellings
* Missing values
* Outliers
* Cross-field validation: certain conditions that utilize multiple fields must hold. For instance, in laboratory medicine: the sum of the different white blood cell must equal to zero (they are all percentages). In hospital database, a patient’s date or discharge can’t be earlier than the admission date
2. Clean the data using:
* Regular expressions: misspellings, regular expression patterns
* KNN-impute and other missing values imputing methods
* Coercing: data-type constraints
* Melting: tidy data issues
* Date/time parsing
* Removing observations



Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

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If we have data, let’s look at data. If all we have are opinions, let’s go with mine. – Jim Barksdale


#BigData @AnalyticsWeek #FutureOfData #Podcast with @MichOConnell, @Tibco

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MichOConnell, @Tibco


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Data production will be 44 times greater in 2020 than it was in 2009.

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