Guides and Research

CIOs: Do You Know Where Your Data Scientists Are?

Learn more about how to wrangle data science IT, increase data science ROI, and drive faster innovation in this guide.

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The Data Science Maturity Model

Identify capability gaps and direct future investment with this model.

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2017 Magic Quadrant for Data Science Platforms

Read how Gartner defines the Data Science Platform category and the evolution of the data science platform market.

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Do You Need a Data Science Platform?

Why data-driven companies are increasingly adopting data science platforms.

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What Engineering Leaders Need to Know About Data Science

Common pitfalls data science leaders face, and how to avoid them.

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Data Science in the Cloud

How to get the most out of moving data science to AWS.

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Should You Build a Data Science Platform?

Effort involved, features you’ll need and the long-term implications of this decision.

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GPU Computing for Data Science

Ways to incorporate GPU computing to complete computationally intensive tasks.

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Building a High-Throughput Data Science Machine

What it takes to run a sophisticated data science organization.

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Interactive Notebooks

Pyfolio for Stock Analysis

A Python library for analysis of financial portfolios developed by Quantopian Inc.

Face Recognition with Machine Learning

Learn about face recognition with the Labeled Faces in the Wild dataset.

Comparing Python Visualization Libraries

Rob Story’s notebook comparing 8 different visualization libraries.

Survival Rates on the Titanic

Code, entries, and visualizations from the Titanic Kaggle competition.

The Prudential Kaggle Insurance Challenge

Python and R starter scripts to work on this popular Kaggle competition.

Political Speeches by Computers

Artificial political speeches based on real speeches made in Congress.

Clustering with Graphistry

Two examples of using Graphistry for understanding networks.

Basics with Scikit-Learn

Instructive notebooks for the popular machine learning package scikit-learn.

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