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[ COVER OF THE WEEK ]
Data interpretation Source
[ AnalyticsWeek BYTES]
>> 7 Design Tips for Pixel-Perfect Operational Reports by analyticsweek
>> Guest Service Technology â The Primary Focus of US Hotel & Resort Investment in 2021 by analyticsweekpick
>> Michael Canic(@MichaelCanic) on Leading with ruthless consistency. Work 2.0 Podcast #FutureofWork #Work2dot0 #Podcast by v1shal
[ FEATURED COURSE]
Learning from data: Machine learning course
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[ FEATURED READ]
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[ TIPS & TRICKS OF THE WEEK]
Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.
[ 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. 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 cant 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 patients date or discharge cant 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
Source
[ VIDEO OF THE WEEK]
@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast
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[ QUOTE OF THE WEEK]
What we have is a data glut. Vernon Vinge
[ PODCAST OF THE WEEK]
Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team
Subscribe
[ FACT OF THE WEEK]
IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer will reach 450 billion per day.