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    zagros Posted 2017-01-29 21:08:28Z
    Python is fast becoming the preferred language for data scientists – and for good reasons. It provides the larger ecosystem of a programming language and the depth of good scientific computation libraries. If you are starting to learn Python, have a look at learning path on Python.Among its scientific computation libraries, I found Pandas to be the most useful for data science operations. Pandas, along with Scikit-learn provides almost the entire stack needed by a data scientist. This article focuses on providing 12 ways for data manipulation in Python. I’ve also shared some tips & tricks which will allow you to work faster.
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    zagros Posted 2016-06-17 02:52:39Z
    Microsoft Corp. snapped up LinkedIn Corp. for $26.2 billion in the largest acquisition in its history, betting the professional social network can rev up the tech titan’s software offerings despite recent struggles by both companies.The deal is Chief Executive Satya Nadella’s latest effort to revitalize Microsoft, which was viewed not long ago as left behind by shifts in technology. Mr. Nadella hopes the deal will open new horizons for Microsoft’s Office suite as well as LinkedIn, both of which have saturated their markets, and generally bolster Microsoft’s revenue and competitive position.
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    zagros Posted 2016-04-21 02:49:51Z
    Many of those who call themselves statisticians just won't admit that data science heavily relies on and uses (heretical, rule-breaking) statistical science, or they don't recognize the true statistical nature of these data science techniques (some are 15-year old), or are opposed to the modernization of their statistical arsenal. They already missed the train when machine learning became a popular discipline (also heavily based on statistics) more than 15 years ago. Now machine learning professionals, who are statistical practitioners working on problems such as clustering, far outnumber statisticians.
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    zagros Posted 2015-11-14 22:28:58Z
    About data scientists

    Rising alongside the relatively new technology of big data is the new job title data scientist. While not tied exclusively to big data projects, the data scientist role does complement them because of the increased breadth and depth of data being examined, as compared to traditional
    roles.

    So what does a data scientist do?

    A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.
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    zagros Posted 2015-06-25 22:25:28Z
    Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques.
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    zagros Posted 2015-04-07 20:25:37Z
    STEPHEN WALTHER:
    I spent the last couple of weeks writing sample code for ASP.NET 5/MVC 6 and I was surprised by the depth of the changes in the current beta release of ASP.NET 5. ASP.NET 5 is the most significant new release of ASP.NET in the history of the ASP.NET framework — it has been rewritten from the ground up.In this blog post, I list what I consider to be the top 10 most significant changes in ASP.NET 5. This is a highly opinionated list. If other changes strike you as more significant, please describe the change in a comment.
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    zagros Posted 2015-02-23 20:28:58Z
    Recently I had the opportunity to setup a multi-node hadoop cluster. The apache documentation is a little thin and I had to spend several hours trouble shooting issues and googling for help before I got it right.  The purpose of this tutorial is to save time for those setting up a hadoop cluster for the first time. If you are new to hadoop, you may read my tutorial on single node setup at Hadoop 2.x tutorial. If you have never setup hadoop before, it is a good idea to to do a single node setup the first time and then try the multi node setup.
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    zagros Posted 2015-01-30 22:13:08Z
    MLlib is a Spark subproject providing machine learning primitives:
    • initial contribution from AMPLab, UC Berkeley
    • shipped with Spark since version 0.8
    • 33 contributors
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    zagros Posted 2014-11-06 22:21:13Z
    import.io is a web-based platform for extracting data from websites without writing any code. The tool allows people to create an API using their point and click interface.
    Users navigate to a website and teach the app to extract data by highlighting examples of data from the page, learning algorithms then generalise from these examples to work out how to get all the data on the website. The data that users collect is stored on import.io’s cloud servers and can be downloaded as CSV, Excel, Google Sheets or JSON and shared. Users can also generate an API from the data allowing them to easily integrate live web data into their own applications or third party analytics and visualization software. For more technical users, import.io offers real-time data retrieval through JSON REST-based and streaming APIs, integration with several common programming languages and data manipulation tools, as well as a federation platform which allows up to 100 data sources to be queried simultaneously.
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    zagros Posted 2014-10-24 21:28:04Z
    In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
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