Rise Up and Meet the Data Science Competition

Rise Up and Meet the #DataScience Competition - 
#analytics #ai #bigdata

via @MktgSciences

  • So when a group of data scientists get together, it’s natural that a little good-natured competition breaks out, to see who can best solve a particular challenge, for both ever-lasing glory and cash prizes.
  • Here are nine on-going data science competitions that might interest data scientists:

    Impetus Technologies is giving away $20,000 in prize money to data science teams that can best utilize Spark Streaming within the construct of its StreamAnalytix product to build real-time streaming applications.

  • MZ is putting up $1 million in prize money for its Satori Challenge, which pits developers against one another in a competition to build the most impactful live data channel on Sartori, the name of MZ’s new open data platform.
  • CrowdAnalytix is putting up $4,500 in prize money this competition, which pits data scientists against each other to find out who can more accurately identify a theme from a group of images.
  • There’s no prize money in this DrivenData competition, just ever-lasting glory for those keen data types who can figure out how to identify which Tanzanian water pumps are working and which aren’t from telemetry data consisting of about 40 distinct variables.

Data scientists love challenges. So when a group of data scientists get together, it’s natural that a little good-natured competition breaks out, to see wh
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New Machine Learning Cheat Sheet by Emily Barry

Machine Learning Cheat Sheet

  • This blog about machine learning was written by Emily Barry.
  • Emily is a Data Scientist in San Francisco, California.
  • The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time.
  • This is by no means a comprehensive guide to machine learning, but rather a study in the basics for herself and the likely small overlap of people who like machine learning and love emoji as much as she do.
  • For more articles about machine learning, click here.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she…
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OracleVoice: Machine Learning Stands To Transform The Way We Communicate

OracleVoice: Machine learning stands to transform the way we communicate

  • With adaptive intelligent applications, organizations can also begin to offer personalized recommendations to customers by getting to the heart of their individual business needs, purchasing decisions, interests, and patterns.
  • New Oracle Cloud services can provide employees with a knowledge base that provides the insights they need to improve business results through a heightened understanding of the way their customers operate.
  • These adaptive intelligent applications can provide organizations—from finance professionals and HR recruiters to marketing and supply chain managers—with actionable business and customer insights to make more informed decisions, leading to more success for their customers.
  • While customer success has long been a central component in the DNA of many organizations, adaptive intelligent applications are helping business integrate customer success further into the DNA of the devices their clients and employees rely on daily.
  • Then, businesses can truly begin to transform the way they anticipate customers’ needs and provide unrivaled experiences and unmatched success.

With adaptive intelligent applications, organizations can offer personalized recommendations to customers by getting to the heart of their individual business needs, purchasing decisions, interests, and patterns.
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New Machine Learning Cheat Sheet by Emily Barry

Machine Learning Cheat Sheet

  • This blog about machine learning was written by Emily Barry.
  • Emily is a Data Scientist in San Francisco, California.
  • The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time.
  • This is by no means a comprehensive guide to machine learning, but rather a study in the basics for herself and the likely small overlap of people who like machine learning and love emoji as much as she do.
  • For more articles about machine learning, click here.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she…
Continue reading “New Machine Learning Cheat Sheet by Emily Barry”

Book: Evaluating Machine Learning Models

Book: Evaluating #MachineLearning Models #abdsc

  • If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming.
  • With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics.
  • In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection.
  • With this report, you will:

    Alice is a technical leader in the field of Machine Learning.

  • Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University.

Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data scien…
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Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning

Top 10 Facebook Groups for #BigData, #DataScience, and #MachineLearning

  • Social network members are tending to more eagerly learn about big data, data science and machine learning through groups.
  • Facebook groups for Big Data and Data Science, while still much smaller than LinkedIn Groups in this area are growing and provide an important new forum for discussion.
  • This post introduces ten largest Facebook groups in big data, data science and machine learning.
  • Though this group consists of 21,239 members and is maintained by two admins (Daniyal Bashir and Mustafa Ali Qizilbash), it is a closed group and limits discussion to only Big data.
  • The group administrators host fortnightly sessions on Hadoop and Big Data and all of them are live in their YouTube channel group has 14,549 members  and was created in 2013 by Min-kyung Kim (Chief Executive Officer at BICube CO.,LTD).


Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.

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Top 10 Machine Learning Projects on Github

Most Viewed 2016 #10: Top 10 #MachineLearning Projects on #Github  #KDN16

  • KDnuggets Home > News > 2015 > Dec > Software > Top 10 Machine Learning Projects on Github ( 15:n41 )
  • The following is an overview of the top 10 machine learning projects on Github .
  • The importance, and central position, of machine learning to the field of data science does not need to be pointed out.
  • The repo has no no software, but if you’re new to Python machine learning, it may be worth checking out.
  • Open source software is an important piece of the data science puzzle.


The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.

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