✅ 🔤 A–Z of Data Science

A – Analytics
Extracting insights from data using statistical and computational methods.

B – Big Data
Large and complex datasets that require special tools to process and analyze.

C – Correlation
Measure of how strongly two variables move together.

D – Data Cleaning
Fixing or removing incorrect, incomplete, or duplicate data.

E – Exploratory Data Analysis (EDA)
Initial investigation of data patterns using visualizations and statistics.

F – Feature Engineering
Creating new input features to improve model performance.

G – Graphs
Visual representations like bar charts, histograms, and scatter plots to understand data.

H – Hypothesis Testing
Statistical method to determine if a hypothesis about data is supported.

I – Imputation
Filling in missing data with estimated values.

J – Join
Combining data from different tables based on a common key.

K – KPI (Key Performance Indicator)
Measurable value that shows how well a model or business is performing.

L – Linear Regression
Model to predict a target variable based on linear relationships.

M – Machine Learning
Using algorithms to learn from data and make predictions.

N – NumPy
Popular Python library for numerical and array operations.

O – Outliers
Extreme values that can distort data analysis and model results.

P – Pandas
Python library for data manipulation and analysis using DataFrames.

Q – Query
Request for information from a database using SQL or similar languages.

R – Regression
Technique for modeling and analyzing the relationship between variables.

S – SQL (Structured Query Language)
Language used to manage and retrieve data from relational databases.

T – Time Series
Data collected over time intervals, used for forecasting.

U – Unstructured Data
Data without a predefined format like text, images, or videos.

V – Visualization
Converting data into charts and graphs to find patterns and insights.

W – Web Scraping
Extracting data from websites using tools or scripts.

X – XML (eXtensible Markup Language)
Format used to store and transport structured data.

Y – YAML
Data format used in configuration files, often in data pipelines.

Z – Zero-Variance Feature
A feature with the same value across all observations, offering no useful signal.

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