{"id":600,"date":"2015-11-29T17:55:50","date_gmt":"2015-11-29T17:55:50","guid":{"rendered":"http:\/\/dataaspirant.com\/?p=600"},"modified":"2016-07-22T17:36:52","modified_gmt":"2016-07-22T17:36:52","slug":"dataaspirant-nov2015-newsletter","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/dataaspirant-nov2015-newsletter\/","title":{"rendered":"dataaspirant-Nov2015-newsLetter"},"content":{"rendered":"
<\/div>

\"dataaspirant\"

Blog Posts:<\/h3>\n

1.\u00a0Great resour<\/span>ces for learning data mining concepts and techniques:<\/span><\/a><\/span><\/strong><\/p>\n

With today\u2019s tools, anyone can collect data from almost anywhere, but not everyone can pull the important nuggets out of that data. Whacking your data into Tableau is an OK start, but it\u2019s not going to give you the business critical insights you\u2019re looking for. To truly make your data come alive you need to mine it. Dig deep. Play around. And tease out the diamond in the rough.<\/p>\n

Read Complete Post on:<\/span>\u00a0blog.import.io<\/a><\/span><\/p>\n

2.\u00a0\u00a0Interactive Data Science with R in Apache Zeppelin Notebook:<\/a><\/span><\/strong><\/p>\n

The objective of this blog post is to help you get started with Apache Zeppelin notebook for your R data science requirements. Zeppelin is a web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with Scala(with Apache Spark), Python(with Apache Spark), SparkSQL, Hive, Markdown, Shell and more.<\/p>\n

Read Complete Post on:<\/span>\u00a0sparkiq-labs<\/a><\/span><\/p>\n

3.\u00a0How to install Apache Hadoop 2.6.0 in Ubuntu:<\/a><\/span><\/strong><\/p>\n

Let\u2019s get started towards setting up a fresh Multinode Hadoop (2.6.0) cluster.<\/p>\n

Read Complete Post on:<\/span>\u00a0pingax<\/a><\/span><\/p>\n

4.\u00a0Running scalable Data Science on Cloud with R & Python:<\/a><\/span><\/strong><\/p>\n

So, why do we even need to run data science on cloud? You might raise this question that if a laptop can pack 64 GB RAM, do we even need cloud for data science? And the answer is a big YES\u00a0<\/strong>for a variety of reasons. Here are a few of them.<\/p>\n

Read Complete Post on:<\/span>\u00a0analyticsvidhya<\/a><\/span><\/p>\n

5.\u00a0How to Choose Between Learning Python or R First:<\/span><\/a><\/strong><\/p>\n

If you\u2019re interested in a career in data, and you\u2019re familiar with the set of skills you\u2019ll need to master, you know that Python and R are two of the most popular languages for data analysis. If you\u2019re not exactly sure which to start learning first, you\u2019re reading the right article.<\/p>\n

When it comes to data analysis, both Python and R are simple (and free) to install and relatively easy to get started with. If you\u2019re a newcomer to the world of data science and don\u2019t have experience in either language, or with programming in general, it makes sense to be unsure whether to learn R or Python first.<\/p>\n

Read Complete Post on:<\/span> Udacity Blog<\/a><\/span><\/p>\n

LinkedIn\u00a0Posts:<\/h3>\n
    \n
  1. 5 Best Machine Learning APIs for Data Science<\/a><\/span><\/li>\n
  2. Big Data Top Trends In 2016<\/a><\/span><\/li>\n
  3. Big Data: 4 Things You Can Do With It, And 3 Things You Can’t<\/a><\/span><\/li>\n<\/ol>\n

    Videos:<\/h3>\n

    1. Machine Learning: Going Deeper with Python and Theano<\/a><\/span><\/p>\n

    2.\u00a0Current State of Recommendation Systems<\/a><\/span><\/p>\n

    3.\u00a0Pandas From The Ground Up<\/a><\/span><\/p>\n

    Library:<\/h3>\n

    TensorFlow Google Machine Learning\u00a0Library:<\/a><\/span><\/p>\n

    About TensorFlow:<\/p>\n

    TensorFlow\u2122 is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.<\/p>\n

    Get Started TensorFlow<\/a><\/span><\/p>\n

     <\/p>\n

    That’s all for November\u00a02015 newsletter. Please leave your suggestions on the newsletter in the comment section. To\u00a0get all \u00a0dataaspirant newsletters you can visit monthly newsletter<\/a><\/span> page. Do please Subscribe to our blog so that every month you get our newsletter in your inbox.<\/p>\n

    Follow us:<\/h2>\n

    FACEBOOK<\/a><\/span>|\u00a0QUORA<\/a><\/span>\u00a0|TWITTER<\/a><\/span>|\u00a0REDDIT<\/a>\u00a0<\/span>| FLIPBOARD<\/a><\/span> |LINKEDIN<\/a>\u00a0|<\/span>\u00a0MEDIUM| <\/span><\/strong><\/a>GITHUB<\/span><\/a><\/strong><\/span><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

    Blog Posts: 1.\u00a0Great resources for learning data mining concepts and techniques: With today\u2019s tools, anyone can collect data from almost anywhere, but not everyone can pull the important nuggets out of that data. Whacking your data into Tableau is an OK start, but it\u2019s not going to give you the business critical insights you\u2019re looking
    + Read More<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_jetpack_memberships_contains_paid_content":false},"categories":[3],"tags":[11,14,15,19,21,23],"aioseo_notices":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts\/600"}],"collection":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/comments?post=600"}],"version-history":[{"count":0,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts\/600\/revisions"}],"wp:attachment":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/media?parent=600"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/categories?post=600"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/tags?post=600"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}