Data Analytics

What is Data Analytics?

Data Analytics is the process of examining raw data to uncover patterns, correlations, trends, and insights that can support better decision-making. It involves collecting, cleaning, processing, and interpreting data using statistical, programming, and visualization techniques.

Why is Data Analytics Used?

To make data-driven decisions.
To identify patterns and predict future trends.
To improve efficiency and reduce costs.
To understand customer behavior and enhance experiences.
To detect risks or fraud in business operations.
To support strategic planning with evidence-based insights.

Role and Responsibilities of a Data Analyst

Data Collection - Gather data from multiple sources (databases, APIs, spreadsheets, etc.).
Data Cleaning & Preparation – Handle missing values, remove duplicates, standardize formats.
Exploratory Data Analysis (EDA) – Find patterns, trends, and relationships.
Data Visualization – Present insights via dashboards, charts, and graphs.
Reporting & Communication – Share findings with stakeholders in business-friendly language.
Statistical & Predictive Analysis – Use models to forecast and simulate scenarios.
Collaboration – Work with business, data engineers, and data scientists to improve systems.

Tools Required for Data Analytics

Here’s a categorized list with official download links and why they’re used:

1. Python

Uses: Widely used for data analysis, machine learning, and automation with powerful libraries like Pandas, NumPy, Matplotlib, and Scikit-learn.

2. Excel (with Power Query & Power Pivot)

Uses: Essential for data manipulation, cleaning, and reporting. Power Query enables data extraction and transformation, while Power Pivot helps with data modeling and analysis.