1️⃣ What is Data Analytics?

👉 Data Analytics = Turning raw data into useful insights for decision-making.

• Data = raw facts (e.g., daily cab fares, student marks).

• Analytics = finding patterns & answers from data.

• Purpose = make better business decisions.

Example:
Chai stall daily cups sold →

• Find busiest day?

• Do weekends sell more?

• Should I buy extra milk on Fridays?

2️⃣ Types of Data Analytics

1. Descriptive → What happened? (e.g., Total sales this month)

2. Diagnostic → Why did it happen? (e.g., Sales dropped on holidays)

3. Predictive → What might happen? (e.g., Diwali sales forecast)

4. Prescriptive → What should we do? (e.g., Add more staff on weekends)

3️⃣ Role of a Data Analyst

A Data Analyst is a translator between data & business.

Main tasks:

1. Collect data (Excel, SQL, Surveys).

2. Clean data (remove errors, duplicates, missing).

3. Analyze data (formulas, queries, stats).

4. Visualize (charts, dashboards).

5. Share insights in plain language.

Example (Swiggy):

• Data: Orders from cities.

• Insight: “Hyderabad = top biryani city.”

• Action: Add more delivery partners in Hyderabad weekends.

4️⃣ Data Cleaning Basics

👉 Rule: Clean first, analyze later (“Garbage In → Garbage Out”).

Steps:

1. Spelling errors (Prodct A → Product A).

2. Missing values (replace with 0 or average).

3. Outliers (Sales = -50, Sales = 100000 → check business meaning).

4. Consistency (dates, formats, spaces).

✅ Don’t delete strange values blindly → understand the business (e.g., negative = refunds).

5️⃣ Descriptive Analytics (Basics)

Answers “What happened?”

Techniques:

• Total (SUM)

• Average (AVG)

• Max/Min

• Count

• Percentages / Growth

Example:

• Total sales = 1450

• Avg sales/day = 290

• Highest sale = 1000

• Lowest sale = 0

6️⃣ SQL Basics (Setup)

Why SQL? Works on millions of rows, much faster than Excel.

Setup (beginner):

1. Install DB Browser for SQLite.

2. Create database learn_sql.sqlite.

3. Create table:

CREATE TABLE sales (
  id INTEGER PRIMARY KEY,
  sales_date TEXT,
  product TEXT,
  amount INTEGER
);

Insert sample data:

INSERT INTO sales (sales_date, product, amount) VALUES
('2025-08-01','Product A',100),
('2025-08-02','Product A',0),
('2025-08-03','Product B',200),
('2025-08-04','Product A',1000),
('2025-08-05','Product C',150);

7️⃣ SQL vs Excel (Formula Mapping)