Agile Data Science: Building Data Analytics Applications by Russell Jurney

By Russell Jurney

Mining enormous information calls for a deep funding in humans and time. how will you ascertain you're development definitely the right versions? With this hands-on ebook, you'll study a versatile toolset and technique for construction potent analytics functions with Hadoop.

Using light-weight instruments equivalent to Python, Apache Pig, and the D3.js library, your group will create an agile surroundings for exploring info, beginning with an instance program to mine your individual e-mail inboxes. You'll research an iterative technique that allows you to fast switch the type of research you're doing, looking on what the information is telling you. All instance code during this publication is obtainable as operating Heroku apps.

Create analytics functions through the use of the agile gigantic info improvement methodology
Build worth out of your information in a chain of agile sprints, utilizing the data-value stack
Gain perception by utilizing numerous information buildings to extract a number of beneficial properties from a unmarried dataset
Visualize information with charts, and reveal diversified features via interactive reports
Use old 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

Example text

Once we remove noncoding common stopwords (like of and it), this looks like Figure 2-10. Figure 2-10. Email body word frequency We might use this word frequency to infer that the topics of the email are plant and grass, as these are the most common words. Processing natural language in this way helps us to extract properties from semistructured data to make it more structured. This enables us to incorporate these structured properties into our analysis. A fun way to show word frequency is via a wordle, illustrated in Figure 2-11.

Our job as we process data, then, is to add fields to our schema as we extract them, all the while retaining the raw data in its own field if we can. 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.

While many choices are appropriate, we’ll use MongoDB for its ease of use, document orientation, and excellent Hadoop and Pig integration (Figure 3-7). With MongoDB and Pig, we can define any arbitrary schema in Pig, and mongo-hadoop will create a corresponding schema in MongoDB. There is no overhead in managing schemas as we derive new relations—we simply manipulate our data into publishable form in Pig. That’s agile! Figure 3-7. org/ display/DOCS/Quickstart. org/display/DOCS/Tutorial. I recommend completing these brief tuto‐ rials before moving on.

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