Issue #03    Web Version
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[  COVER OF THE WEEK ]

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Data Mining  Source

[ LOCAL EVENTS & SESSIONS]


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[ AnalyticsWeek BYTES]

>> How Similar Are UX Metrics in Moderated vs. Unmoderated Studies? by analyticsweek

>> Nov 09, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Australian businesses failing to explore bigger data by analyticsweekpick


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[ NEWS BYTES]

>>  Data center developer needs temporary water system – Fauquier Now Under  Data Center

>>  Nigerian law enforcement agency to relocate its servers following data center fire – DatacenterDynamics Under  Data Center

>>  What Will Your Customer Experience? – Forbes Under  Customer Experience


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[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem... more


[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data's growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think... more


[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.


[ DATA SCIENCE Q&A]

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. <15-25, >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
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[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

 @AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz


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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]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

 George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast


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[ FACT OF THE WEEK]

Poor data can cost businesses 20%–35% of their operating revenue.


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*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
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