Jan 31, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Fake data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> A case for Computer Algorithms and Recursion in Data Science Education by vinny

>> Simplifying Data Warehouse Optimization by analyticsweekpick

>> What’s a CFO’s biggest fear, and how can machine learning help? by wwcheng

Wanna write? Click Here

[ NEWS BYTES]

>>
 Using Big Data to Give Patients Control of Their Own Health – Singularity Hub Under  Big Data

>>
 Big Data Analytics in Banking Market 2025: Global Demand, Key Players, Overview, Supply and Consumption Analysis – Honest Facts Under  Big Data Analytics

>>
 Manual intervention is hindering the customer experience – Chain Store Age Under  Customer Experience

More NEWS ? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

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If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:How to efficiently scrape web data, or collect tons of tweets?
A: * Python example
* Requesting and fetching the webpage into the code: httplib2 module
* Parsing the content and getting the necessary info: BeautifulSoup from bs4 package
* Twitter API: the Python wrapper for performing API requests. It handles all the OAuth and API queries in a single Python interface
* MongoDB as the database
* PyMongo: the Python wrapper for interacting with the MongoDB database
* Cronjobs: a time based scheduler in order to run scripts at specific intervals; allows to bypass the “rate limit exceed” error

Source

[ VIDEO OF THE WEEK]

#HumansOfSTEAM feat. Hussain Gadwal, Mechanical Designer via @SciThinkers #STEM #STEAM

 #HumansOfSTEAM feat. Hussain Gadwal, Mechanical Designer via @SciThinkers #STEM #STEAM

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[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

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[ FACT OF THE WEEK]

The Hadoop (open source software for distributed computing) market is forecast to grow at a compound annual growth rate 58% surpassing $1 billion by 2020.

Sourced from: Analytics.CLUB #WEB Newsletter

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