Data Science

Identifying patterns in big data to predict and influence business outcomes gives businesses the foresight to be better at what they do.

  • The Eight-Fold Path of Data Science

    The Eight-Fold Path of Data Science

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  • Data Science topic page

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  • Journey to Cloud-Native: Continuous Delivery with Artificial Intelligence

    Journey to Cloud-Native: Continuous Delivery with Artificial Intelligence

    Teams building modern apps and microservices are using new techniques to ensure their quality as well as rapid deployment across build, test, and production cycles. This webinar will cover how you can

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  • How Curry Ball Will Impact March Madness Brackets

    How Curry Ball Will Impact March Madness Brackets

    402.It was April 13, 2016: the third time Stephen Curry had set the record for 3-pointers made in a single season, in just four years. He and his teammates on the Golden State Warriors were...

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  • Breaking Down the NBA 3-Point Shot, Data Science Style (Ep. 24)

    Breaking Down the NBA 3-Point Shot, Data Science Style (Ep. 24)

    In this Pivotal Insights episode, Pivotal Data Scientist Chris Rawles breaks down the impact of the three point shot on the style of play and shot selection in the NBA.

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  • Capitalizing on Customer Desire with Social Media Analytics

    Capitalizing on Customer Desire with Social Media Analytics

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  • Plotting Using an MPP Database

    Data visualization is the process of transforming and condensing data into an easily digestible graphic. It is crucial in helping data scientists understand their data and share their insights...

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  • Journey to Cloud-Native: Continuous Delivery with Artificial Intelligence

    Journey to Cloud-Native: Continuous Delivery with Artificial Intelligence

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  • Pivotal's Approach to Threat Detection

    Pivotal's Approach to Threat Detection

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  • Trilogy and Greenplum for Data Science TDD

    In this post I show how to use Trilogy, a new testing framework for SQL databases, with the open source Greenplum Database. The goal is to help you test drive your data science SQL code. The...

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  • Agile Development for Highly Scalable Data Processing Pipelines

    Recently, a client asked Pivotal’s Data Science team to help convert some aging T-SQL stored procedures used in their data processing pipeline into better code. The goals were to enable better...

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  • Operationalizing Data Science Models on the Pivotal Stack

    (Joint work by Srivatsan Ramanujam, Regunathan Radhakrishnan, Jin Yu, Kaushik Das ) At Pivotal Data Science, our primary charter is to help our customers derive value from their data assets, be...

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  • API First for Data Science

    Joint work by Dat Tran (Data Scientist) and Alicia Bozyk (Senior Software Engineer). Key Takeaways Think about wrapping up your data science model as an API as early as possible Cloud Foundry...

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  • Open Source Analytics Library MADlib Receives Site Relaunch, v1.4 Release

    Open Source Analytics Library MADlib Receives Site Relaunch, v1.4 Release

    MADlib, the open source analytics library shepherded by Pivotal data scientists and UC Berkeley researchers, gets a fresh coat of a paint with a major relaunch of the project’s website. Allowing...

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  • Case Study: Analyzing Retail/e-Commerce 318x Faster on HAWQ +MADlib

    Case Study: Analyzing Retail/e-Commerce 318x Faster on HAWQ +MADlib

    Retailers are using big data to improve business outcomes, and one of our customers has shown that Pivotal HD and HAWQ can increase retail related query and processing times over Hadoop tools by...

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  • Continuous Integration for Data Science

    Continuous Integration for Data Science

    This is a follow up post on Test-Driven Development for Data Science and API First for Data Science focusing on Continuous Integration. Motivation Last time we wrote about the importance of...

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  • Data Science How-To: Using Apache Spark for Sports Analytics

    Data Science How-To: Using Apache Spark for Sports Analytics

    In this post, Chris Rawles gives a hands-on tutorial for getting started with the recently released Spark 2.1 using data from the National Basketball Association (NBA).

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  • How to Use Temporal Features in an MPP Database

    How to Use Temporal Features in an MPP Database

    Learn how to create temporal features in an MPP database and how to set up the data for cross-validation and model building.

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  • Transforming Your Company into a Data Science-Driven Enterprise

    Transforming Your Company into a Data Science-Driven Enterprise

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  • Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field

    Data Science Revealed: A Data-Driven Glimpse into the Burgeoning New Field

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  • New Tools To Shape Data In Apache MADlib

    New Tools To Shape Data In Apache MADlib

    Apache MADlib includes new utilities that make it easier for data scientists to shape and transform data, and to evaluate the accuracy of predictive models. It is well known that data scientists...

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