7 Steps to Mastering Data Preparation for Machine Learning with Python
Author: Matthew Mayo, KDNuggets
Source: https://www.kdnuggets.com/2019/06/7-steps-mastering-data-preparation-python.html
How: Pandas library, Python, EDA (Exploratory Data Analysis)
When to use this: when preparing data for machine learning
Why it's helpful: Step-by-step reference with supporting links, as well as an introduction for those in IT or data sciences but not as involved in the data preparation process
Suggested application: Refresher for those involved in data wrangling, this article was updated from the 2017 version to incorporate updated library references, related articles and insights from real world practice
Business impact or insights to be gained: Developments in Machine Learning and resources available to support data wrangling work hand in hand to improve the outcomes by better preparing the inputs
Data Viz Project Reference Library
Author: Ferdio
Source: https://datavizproject.com/
How: Quick visual reference to various visualization styles. Click on the image for a description. optimal uses and examples of each style in use
When to use this: When trying to decide what style to use for a new visualization or to train others about data visualization options and how to choose what style to use
Why it's helpful: Quick Reference Guide for Visualizations
Suggested application: Intelligently break out of default styles by using clear guides of when to select a particular alternative
Business impact or insights to be gained: Get more people in your organization leveraging data visualization with this easy to use reference source
10 Powerful Python Tricks for Data Science you Need to Try Today
Author: Lakshay Arora
Source: analyticsvidhya.com/blog/2019/08/10-powerful-python-tricks-data-science/
How: Code and links are included in the article to augment your use of Python
When to use this: when you are working with lists, Google Maps, categorical variables, time series data and more
Why it's helpful: Tips to solve real world applications
Suggested application: working with categorical variables, to analyze time spent on data science tasks or running a Python code
Business impact or insights to be gained: save time and headaches by using pre-existing code to accelerate addressing common challenges
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