Nov 30, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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>> Getting a 360° View of the Customer – Interview with Mark Myers of IBM by bobehayes

>> The Blueprint for Becoming Data Driven: Data Quality by jelaniharper

>> May 04, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

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 Why Google’s Artificial Intelligence Confused a Turtle for a Rifle – Fortune Under  Artificial Intelligence

 Microsoft Workplace Analytics helps managers understand worker … – TechCrunch Under  Analytics

 Storytelling – Two Essentials for Customer Experience Professionals – Customer Think Under  Customer Experience

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A Course in Machine Learning


Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more


Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking


Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… more


Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.


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



@AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

 @AngelaZutavern & @JoshDSullivan @BoozAllen discussed Mathematical Corporation #FutureOfData

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What we have is a data glut. – Vernon Vinge


#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp


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140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

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