Agile Data Science: Building Data Analytics Applications by Russell Jurney

By Russell Jurney

Mining giant information calls for a deep funding in humans and time. how are you going to determine you're development the proper types? With this hands-on booklet, you'll examine a versatile toolset and technique for development powerful analytics functions with Hadoop.

Using light-weight instruments resembling Python, Apache Pig, and the D3.js library, your crew will create an agile atmosphere for exploring info, beginning with an instance program to mine your individual e mail inboxes. You'll examine an iterative method that allows you to speedy swap the type of research you're doing, reckoning on what the knowledge is telling you. All instance code during this ebook is accessible as operating Heroku apps.

Create analytics purposes through the use of the agile mammoth information improvement methodology
Build price out of your info in a sequence of agile sprints, utilizing the data-value stack
Gain perception through the use of numerous information constructions to extract a number of good points from a unmarried dataset
Visualize info with charts, and divulge assorted elements via interactive reports
Use ancient facts to foretell the long run, and translate predictions into action
Get suggestions from clients after each one dash to maintain your undertaking on target

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Extra resources for Agile Data Science: Building Data Analytics Applications with Hadoop

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Figure 2-3 shows a data pipeline to calculate the number of emails sent between two email addresses. 26 | Chapter 2: Data Figure 2-3. Simple dataflow to count the number of emails sent between two email ad‐ dresses While this dataflow may look complex now if you’re used to SQL, you’ll quickly get used to working this way and such a simple flow will become second nature. Data Perspectives To start, it is helpful to highlight different ways of looking at email. In Agile Big Data, we employ varied perspectives to inspect and mine data in multiple ways because it is Data Perspectives | 27 easy to get stuck thinking about data in one or two ways that you find productive.

0 is available for download Prescriptions Ready at Walgreens How Logical Plan Generator works? Re: server-side SVG-based d3 graph generation, and SVG display on IE8 neil kodner (@neilkod) favorited one of your Tweets! Now that we’ve got data, we can begin processing it. Data Processing with Pig Perl is the duct tape of the Internet. —Hassan Schroeder, Sun’s first webmaster Pig is the duct tape of big data. We use it to define dataflows in Hadoop so that we can pipe data between best-of-breed tools and languages in a structured, coherent way.

We can always go back to the mother source. Extracting and Exposing Features in Evolving Schemas As Pete Warden notes in his talk “Embracing the Chaos of Data”, most freely available data is crude and unstructured. ” Therein lies the opportunity in mining crude data into refined information, and using that infor‐ mation to drive new kinds of actions. Extracted features from unstructured data get cleaned only in the harsh light of day, as users consume them and complain; if you can’t ship your features as you extract them, you’re in a state of free fall.

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