ARTICLE

This article covers

• Recognizing the signs of tightly coupled code

• Strategies for reducing coupling

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Loose coupling is what allows you to affect change in different areas of your code without worry you’ll break something elsewhere. It’s also what allows you to work on one feature as your coworker tackles another. It’s the foundation for other desirable characteristics like extensibility. Without loose coupling, maintaining your code can quickly grow out of hand. …

TWITCH

In case you missed it, here is Carl Gold’s live Twitch coding stream.

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ARTICLE

This article describes how and when to deploy Fluentd.

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Deployment considerations

When considering Fluentd deployment for production we need to consider volume metrics i.e. the amount of log data needing to be captured, filtered, routed and stored. To start with let’s assume we’re working in an environment that doesn’t demand hyper scaling. Despite this, at the end of the day we’re dealing with an element of processing demand, but most importantly log entries are being consumed and stored which creates a lot of I/O…

TWITCH

In case you missed it, here is Richard Nuckolls’ live Twitch coding stream.

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ARTICLE

Want a job in Data Science?

This article discusses one popular way to get the skills that you’re going to need: attending a bootcamp.

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Going through a bootcamp

A bootcamp is an 8–15 week intensive course put on by companies like Metis and Galvanize. During the bootcamp you spend 8+ hours every day learning data science skills, listening to industry speakers, and working on projects. At the end of the course, you’ll usually present a capstone project to a room full…

ARTICLE

In this article, we’ll layer some of the critical and more advanced functionality needed for most data platforms today. Without this added layer of sophistication, your data platform would work but it wouldn’t scale easily, nor would it meet the growing data velocity challenges. It would also be limited in terms of the types of data consumers (people and systems who consume the data from the platform) it supports, as they’re also growing in both numbers and variety.

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Let’s…

VIDEO

In case you missed it, here is Nuwan Dias’ live Twitch coding stream recap.

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ARTICLE

Our first case study explores a medical decision-making task: breast cancer diagnosis. We will see how to use scikit-learn’s homogeneous parallel ensemble modules in practice. Specifically, we will train and evaluate the performance of three homogeneous parallel algorithms, each characterized by increasing randomness: bagging with decision trees, random forests and ExtraTrees.

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Breast-cancer diagnosis is the task of classifying patient tumor data into one of two classes: malignant vs. benign. This task (and similar medical tasks) are at the…