Outliers

Data Cleaning: The Foundation of Reliable Analytics Data cleaning is the quiet hero behind reliable analytics. When data is messy, even strong models can mislead. Small errors in a dataset may skew results, create false signals, or hide real trends. Cleaning data is not a single task; it is a practical, ongoing process that makes data usable, comparable, and trustworthy across projects. Common problems include missing values, duplicate records, inconsistent units, and wrong data types. These issues slow work and can lead to wrong conclusions if they are not addressed. ...

Exploratory Data Analysis: Techniques for Beginners Exploratory Data Analysis (EDA) is the first look at your data after you collect it. It helps you understand what the numbers say, find mistakes, and plan the next steps. This guide covers simple techniques that work for most datasets and all kinds of tools. What is Exploratory Data Analysis? EDA is a mindset as much as a set of tricks. You learn the shape of the data, check data types, and spot patterns. You look for missing values, unusual values, and surprising relationships. The goal is to describe the data clearly and prepare it for any modeling or reporting. ...