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⇫ Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark textAgile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark ⩚ PDF by Russell Jurney ⫆

⇫ Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark textAgile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark ⩚ PDF by Russell Jurney ⫆ ⇫ Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark textAgile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark ⩚ PDF by Russell Jurney ⫆ Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if theyre to succeed With the revised second edition of this hands on guide, up and coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit learn, and Apache Airflow Youll learn an iterative approach that lets you quickly change the kind of analysis youre doing, depending on what the data is telling you Publish data science work as a web application, and affect meaningful change in your organization.Build value from your data in a series of agile sprints, using the data value pyramidExtract features for statistical models from a single datasetVisualize data with charts, and expose different aspects through interactive reportsUse historical data to predict the future via classification and regressionTranslate predictions into actionsGet feedback from users after each sprint to keep your project on track Agile Data Science Analytics Solutions for Business This rigorous experiment driven design and analysis framework is Elder Research s Agile methodology AGILE DATA SCIENCE Predictive modeling a research task It difficult to know in advance which algorithms variables, when combined, will reveal the secrets data O Reilly Media Create analytics applications by using agile big development Build value from your series of sprints, stack Gain insight several structures extract multiple features single dataset Taking an Approach Science science powering around clock, Netflix powerful content recommendation engine virtual assistant Alexa But not all companies are equipped support large scale, always on projects doing so requires infrastructure that can handle Analytics Best Practices Brings Organization Project Team While it possible use working alone, approach designed help organize work team Any time members different aspects project there naturally be confusion, duplication efforts, or tasks RStudio well established practice modern software gained broad adoption as became ubiquitous business world As matures organization, perhaps we at similar crossroads What learn Babar Bhatti Medium post collection ideas others Science, combined with my thoughts how apply essence Improving Your Workflow Scrum R The complex while scientists 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2.0: Building Full-Stack Data Analytics Applications with Spark

 

    • Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark
    • 4.4
    • 648
    • Kindle
    • 352 pages
    • 1491960116
    • Russell Jurney
    • English
    • 08 February 2017

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