1) Data Analytics vs Data Analysis vs Data Science

• Data Analysis: Finding answers from data (like totals, trends, comparisons).
• Data Analytics: Bigger view—analysis + using insights to help business decisions.
• Data Science: Advanced level—uses coding + machine learning to predict and build models.

2) Data Analysis Process (Steps)

A common step-by-step flow:

1. Understand the problem (what question to solve)
2. Collect data
3. Clean data (remove mistakes, blanks, duplicates)
4. Analyze (calculations, SQL queries, charts)
5. Visualize (dashboard/report)
6. Share insights (what it means + what action to take)

3) What is Data Cleaning?

Data cleaning means making data correct and usable.
Common problems:

• Missing values (blank cells)
• Duplicates (same record repeated)
• Wrong format (dates stored as text)
• Outliers (very unusual values)
• Spelling issues (Noida / noida / NOIDA)

4) Types of Data

• Structured: Proper tables (Excel/SQL). Example: Student marks sheet.
• Semi-structured: Has some structure but not perfect tables. Example: JSON, XML.
• Unstructured: No fixed format. Example: images, videos, emails, PDFs.

5) What is SQL and Why Important?

SQL is used to talk to databases and get data.
Data analysts use SQL to:

• fetch data quickly
• filter, sort, and group data
• join multiple tables (customer + orders)

6) SQL Query to Find Duplicate Records

Duplicates depend on which columns should be unique (example: email).

SELECT email, COUNT(*) AS total
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

This shows emails that appear more than once.

7) WHERE vs HAVING in SQL

• WHERE filters rows before grouping.
• HAVING filters groups after GROUP BY.

Example:

SELECT city, COUNT(*) AS total
FROM users
WHERE city IS NOT NULL
GROUP BY city
HAVING COUNT(*) > 100;

8) What are JOINs? (INNER JOIN vs LEFT JOIN)

JOIN means combining data from two tables.

Example tables:

• customers(customer_id, name)
• orders(order_id, customer_id, amount)

INNER JOIN: only matching records from both tables.

SELECT c.name, o.amount
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id;

LEFT JOIN: all customers + matching orders (even if no order, customer still shows).

SELECT c.name, o.amount
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id;

9) What is GROUP BY?

GROUP BY groups similar records and helps you calculate totals/average/count.

Example: total students per course

SELECT course, COUNT(*) AS total_students
FROM students
GROUP BY course;

10) Primary Key vs Foreign Key

• Primary Key (PK): Unique ID in a table (cannot repeat). Example: student_id.
• Foreign Key (FK): A column that links to another table’s primary key. Example: orders.customer_id links to customers.customer_id.

Simple meaning: PK = unique identity, FK = connection between tables.

11) Excel: VLOOKUP vs XLOOKUP vs INDEX-MATCH

• VLOOKUP: Old and common. Looks from left to right only. Can break if columns change.
• XLOOKUP: Newer and easier. Can look left/right, safer.
• INDEX-MATCH: Powerful combo, works like flexible lookup and used widely.

Simple tip: If available, XLOOKUP is usually easiest.

12) What is a Pivot Table?

A Pivot Table is used to summarize big data quickly without formulas.
You can get:

• total sales by month
• count of students by course
• average marks by subject

It’s like making a report in 2 minutes.

13) What is Data Visualization? What makes a dashboard good?

Data visualization means showing data in charts/graphs so people understand quickly.

A good dashboard:

• answers the main business questions
• is simple (not too many charts)
• has correct labels and clear numbers
• shows key KPIs (sales, profit, conversion, etc.)

14) Power BI/Tableau: Calculated Column vs Measure

• Calculated Column: Created for every row (stored in the table). Good for row-level logic.
• Measure: Calculated only when needed (dynamic). Good for totals like sum, average.

Example idea:

• Column: Profit = Sales - Cost (per row)
• Measure: Total Profit = SUM(Profit) (changes with filters)

15) Correlation vs Causation

• Correlation: Two things move together (example: ice cream sales and cold drinks sales).
• Causation: One thing causes the other.

Important: Correlation does NOT always mean causation.
Example: Ice cream sales increase and drowning incidents increase in summer—not because ice cream causes drowning, but because summer increases both.