Best Practices in Data Science

A Reference Guide for Data Scientists Engaging in Machine Learning Projects

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Best Practices in Data Science

This reference guide full of useful “Dos and Don’ts” will help position your data science team for success.

-Understand the business problem
-Define in advance the methodology for model evaluation and selection
-Under-promise, over-deliver

-Perform turning with cross-validation
-Pursue “rabbit holes”

Read the guide for a complete list of helpful recommendations.

About Skytree

Skytree – The Machine Learning Company® is disrupting the Advanced Analytics market with our enterprise-grade machine-learning platform that gives organizations the power to discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets and customers. Skytree’s mission is to bring the power of state-of-the-art machine learning to everyone: including data scientists, developers and non-experts alike.

The Skytree machine-learning platform is built for speed and scalability, allowing users to build the most accurate machine-learning models, faster. Skytree machine-learning software delivers an end-to-end model building experience, from data preparation to model creation and deployment. Skytree automates the machine learning model-building process, saving you time. Machine-learning models using Skytree can be built using all of your data, both structured and unstructured, with no down sampling required.

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