Golden State Warriors Analytics Exercise

[unable to retrieve full-text content]This post outlines a data analysis exercise undertaken by students in a recent University of San Francisco MBA class, in which they were forced to make difficult data science trade-offs between gathering data, preparing the data and performing the actual analysis.
Original Post: Golden State Warriors Analytics Exercise

Top 15 Python Libraries for Data Science in 2017

[unable to retrieve full-text content]Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.
Original Post: Top 15 Python Libraries for Data Science in 2017

Your Checklist to Get Data Science Implemented in Production

[unable to retrieve full-text content]For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you’re working on your design-to-production pipeline.
Original Post: Your Checklist to Get Data Science Implemented in Production

How HR Managers Use Data Science to Manage Talent for Their Companies

[unable to retrieve full-text content]Data sciences can also be used by HR manager to create several estimates like the investment on talent pool, cost per hire, cost on training, and cost per employee. It provides better techniques for optimization, forecasting, and reporting.
Original Post: How HR Managers Use Data Science to Manage Talent for Their Companies

7 Steps to Mastering Data Preparation with Python

[unable to retrieve full-text content]Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.
Original Post: 7 Steps to Mastering Data Preparation with Python

Data Science for Newbies: An Introductory Tutorial Series for Software Engineers

[unable to retrieve full-text content]This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.
Original Post: Data Science for Newbies: An Introductory Tutorial Series for Software Engineers

KDnuggets™ News 17:n21, May 31: Python Machine Learning Workflows from Scratch; Machine Learning Crash Course

[unable to retrieve full-text content]Machine Learning Workflows in Python from Scratch Part 1: Data Preparation; Machine Learning Crash Course: Part 1; An Introduction to the MXNet Python API; How A Data Scientist Can Improve Productivity; Data science platforms are on the rise and IBM is leading the way
Original Post: KDnuggets™ News 17:n21, May 31: Python Machine Learning Workflows from Scratch; Machine Learning Crash Course