Download Agile Data Science: Building Data Analytics Applications by Russell Jurney PDF

By Russell Jurney

Mining significant information calls for a deep funding in humans and time. how will you make certain you're development the perfect versions? With this hands-on booklet, you'll research a versatile toolset and method for development potent analytics functions with Hadoop.

Using light-weight instruments akin to Python, Apache Pig, and the D3.js library, your group will create an agile setting for exploring information, beginning with an instance program to mine your personal e-mail inboxes. You'll study an iterative process that permits you to fast swap the type of research you're doing, looking on what the knowledge is telling you. All instance code during this e-book is offered as operating Heroku apps.

Create analytics functions by utilizing the agile immense facts improvement methodology
Build price out of your facts in a sequence of agile sprints, utilizing the data-value stack
Gain perception through the use of numerous info constructions to extract a number of positive factors from a unmarried dataset
Visualize info with charts, and reveal varied facets via interactive reports
Use ancient facts to foretell the long run, and translate predictions into action
Get suggestions from clients after every one dash to maintain your venture on course

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Additional info for Agile Data Science: Building Data Analytics Applications with Hadoop

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Hadoop and work queues make this possible. In addition to mapping well to technologies like Hadoop, which enables us to easily scale our processing, imperative languages put the focus of our tools where most of the work in building analytics applications is: in one or two hard-won, key steps where we do clever things that deliver most of the value of our application. Compared to writing SQL queries, arriving at these clever operations is a lengthy and often exhaustive process, as we employ techniques from statistics, machine learning, and social network analysis.

Derived data that lives in the basement of your product is unlikely to shape up. It is better to create entity pages to bring entities up to a NoSQL | 25 “consumer-grade” form, to incrementally improve these entities, and to progressively combine them than to try to expose myriad derived data in a grand vision from the getgo. While mining data into well-structured information, using that information to expose new facts and make predictions that enable actions offers enormous potential for value creation.

It is well and good to define a schema up front, but in practice, much pro‐ cessing will be required to extract all the entities in that schema. So our initial schema might look very simple, like this: { "type":"record", "name":"RawEmail", "fields": [ { "name":"thread_id", "type":["string", "null"], "doc":"" }, { "name":"raw_email", "type": ["string", "null"] } ] } We might extract only a thread_id as a unique identifier, and then store the entire raw email string in a field on its own. If a unique identifier is not easy to extract from raw records, we can generate a UUID (universally unique identifier) and add it as a field.

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