Data Scientist Digest 1

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

Subscribe to get more tips and references. (See “subscribe here” on this page.)

Have an article you’d like featured? Send us a note at Contact Us.