What is Data Analytics and its tools and types ?
A
what is Data Analyst ?
Data Analyst is a professional who collects , cleans and interperts raw data sets to identify trends patterns and insights that helps organization to take business decisions.
Data Analyst =
Skills + Projects + Consistency
What is Data Analytics? (Definition)
Data Analytics is the process of collecting, cleaning, and analyzing raw data to find hidden patterns and make smart business decisions.
Simple Example:
An e-commerce company has 1 million orders. A Data Analyst will look at this data and answer questions like:
Which product is selling the most?
Which city generates the highest revenue?
What time of day do most customers shop?
Types of Data Analytics -
Data Analytics is generally divided into four main categories :
1.Descriptive Analytics: What happened? (e.g., Sales dropped by 10% last month).
2.Diagnostic Analytics: Why did it happen? (e.g., Sales dropped because a competitor launched a discount).
3.Predictive Analytics: What will happen next? (e.g., Based on past data, sales will rise again in the festival season).'
4.Prescriptive Analytics: What should we do about it? (e.g., We should increase our marketing budget by 5% to beat the competitor).
Importance of Data Analyst -
1. Decision making - It gives clear facts and patterns from data which help people make smarter choices.
2. Problem solving- What's going wrong and who making is easier to fix problems
3. Identify opportunity - It shows trends and chances of growth .
4. Improved efficiency - It helps reduce waste ,save times and makes work smother by finding better ways to do things .
Tools You Need to Learn-
Microsoft Excel: The foundation of all data work. You should know Pivot Tables, VLOOKUP, and basic formulas.
SQL (Structured Query Language): The most important language to communicate with databases and extract the data you need.
Power BI or Tableau: Tools used to create beautiful, interactive dashboards and charts so that non-technical people can understand the data.
Python: Used for advanced data cleaning, manipulation (using Pandas), and machine learning.
What Actually Matters:
Portfolio Projects (Most Important)
SQL + Python Skills
Power BI Dashboard experience
Problem-solving ability
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