Embarking on an Insight-Finding Mission
Preparing yourself and your data like we have done thus far in this series is essential to analyzing your data well. However, the most exciting part of Exploratory Data Analysis (EDA) is actually getting in . . .
with Python and Dedupe
Entity resolution (ER) is the task of disambiguating records that correspond to real world entities across and within datasets. The applications of entity resolution are tremendous, particularly for public sector and federal datasets related to health, transportation, finance, law enforcement, and antiterrorism.
Unfortunately, the problems . . .
Preparing Your Data to be Explored
This is the second post in our Data Exploration with Python series. Before reading this post, make sure to check out Data Exploration with Python, Part 1!
Mise en place (noun): In a professional kitchen, the disciplined organization and preparation of equipment and food before service begins.
When performing exploratory data analysis (EDA), . . .
Part 1: Skip-gram Feedforward
Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out the other posts in the series so far:
- NLP Research Lab Part 1: Distributed Representations
- NLP Research Lab Part 2: Skip-Gram Architecture Overview
Let's continue our treatment of the . . .
New Years Resolutions for the Intermediate Data Scientist
2016 marked a zenith in the data science renaissance. In the wake of a series of articles and editorials declaiming the shortage of data analysts, the internet responded in force, exploding with blog posts, tutorials, and listicles aimed at launching the beginner into the world of data science. And yet, in spite of all the claims that this . . .
Preparing Yourself to Become a Great Explorer
Exploratory data analysis (EDA) is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with. It is exploratory analysis that gives us a sense of what additional work should be performed to quantify and extract insights from our data. It also . . .
Machine learning models benefit from an increased number of features — “more data beats better algorithms”. In the financial and social domains, macroeconomic indicators are routinely added to models particularly those that contain a discrete time or date. For example, loan or credit analyses that predict the likelihood of . . .