Subject

Data

Preparing Yourself for a Job in Data Science, Part 2: building a portfolio

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis If you have already learned the skills you need for a data science job, why not put them to use in a way that a potential employer… Continue Reading →

The Practical Guide to Data Leadership

From Become a Leader in Data Science by Jike Chong and Yue Cathy Chang

Preparing Yourself for a Job in Data Science, Part 1: bootcamp

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis

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.

The Layers of a Cloud Data Platform

From Designing Cloud Data Platforms by Danil Zburivsky and Lynda Partner

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.

Case Study: Breast Cancer Diagnosis

From Ensemble Methods for Machine Learning by Gautam Kunapuli

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.

Getting started with graphs

From R in Action, Third Edition by Robert Kabacoff

This article dicusses graphs and graphic using the ggplot2 package

Techniques for handling modern big data applications

From Algorithms and Data Structures for Massive Datasets by Dzejla Medjedovic, Emin Tahirovic, and Ines Dedovic

All About Bloom Filters

From Algorithms and Data Structures for Massive Datasets by Dzejla Medjedovic, Emin Tahirovic, and Ines Dedovic

This article covers:

·  Learning what Bloom filters are, why and when they are useful

·  Understanding how Bloom filters work

·  Configuring a Bloom filter in a practical setting

·  Exploring the interplay between Bloom filter parameters

Shallow Transfer Learning in NLP

From Transfer Learning for Natural Language Processing by Paul Azunre

This article delves into using shallow transfer learning to improve your NLP models.

Key exchange standards

From Real-World Cryptography by David Wong

In this article, the author teaches readers about the Diffie-Hellman key exchange standard, which was the very first key exchange algorithm ever invented, and the Elliptic Curve Diffie-Hellman key exchange standard, which is the Diffie-Hellman built with elliptic curves.

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