SQL Detailed Roadmap
|
| | |-- Fundamentals
| |-- Introduction to Databases
| | |-- What SQL does
| | |-- Relational model
| | |-- Tables, rows, columns
| |-- Keys and Constraints
| | |-- Primary keys
| | |-- Foreign keys
| | |-- Unique and check constraints
| |-- Normalization
| | |-- 1NF, 2NF, 3NF
| | |-- ER diagrams

| | |-- Core SQL
| |-- SQL Basics
| | |-- SELECT, WHERE, ORDER BY
| | |-- GROUP BY and HAVING
| | |-- JOINS: INNER, LEFT, RIGHT, FULL
| |-- Intermediate SQL
| | |-- Subqueries
| | |-- CTEs
| | |-- CASE statements
| | |-- Aggregations
| |-- Advanced SQL
| | |-- Window functions
| | |-- Analytical functions
| | |-- Ranking, moving averages, lag and lead
| | |-- UNION, INTERSECT, EXCEPT

| | |-- Data Management
| |-- Data Types
| | |-- Numeric, text, date, JSON
| |-- Indexes
| | |-- B tree and hash indexes
| | |-- When to create indexes
| |-- Transactions
| | |-- ACID properties
| |-- Views
| | |-- Standard views
| | |-- Materialized views

| | |-- Database Design
| |-- Schema Design
| | |-- Star schema
| | |-- Snowflake schema
| |-- Fact and Dimension Tables
| |-- Constraints for clean data

| | |-- Performance Tuning
| |-- Query Optimization
| | |-- Execution plans
| | |-- Index usage
| | |-- Reducing scans
| |-- Partitioning
| | |-- Horizontal partitioning
| | |-- Sharding basics

| | |-- SQL for Analytics
| |-- KPI calculations
| |-- Cohort analysis
| |-- Funnel analysis
| |-- Churn and retention tables
| |-- Time based aggregations
| |-- Window functions for metrics

| | |-- SQL for Data Engineering
| |-- ETL Workflows
| | |-- Staging tables
| | |-- Transformations
| | |-- Incremental loads
| |-- Data Warehousing
| | |-- Snowflake
| | |-- Redshift
| | |-- BigQuery
| |-- dbt Basics
| | |-- Models
| | |-- Tests
| | |-- Lineage

| | |-- Tools and Platforms
| |-- PostgreSQL
| |-- MySQL
| |-- SQL Server
| |-- Oracle
| |-- SQLite
| |-- Cloud SQL
| |-- BigQuery UI
| |-- Snowflake Worksheets

| | |-- Projects
| |-- Build a sales reporting system
| |-- Create a star schema from raw CSV files
| |-- Design a customer segmentation query
| |-- Build a churn dashboard dataset
| |-- Optimize slow queries in a sample DB
| |-- Create an analytics pipeline with dbt

| | |-- Soft Skills and Career Prep
| |-- SQL interview patterns
| |-- Joins practice
| |-- Window function drills
| |-- Query writing speed
| |-- Git and GitHub
| |-- Data storytelling

| | |-- Bonus Topics
| |-- NoSQL intro
| |-- Working with JSON fields
| |-- Spatial SQL
| |-- Time series tables
| |-- CDC concepts
| |-- Real time analytics

| | |-- Community and Growth
| |-- LeetCode SQL
| |-- Kaggle datasets with SQL
| |-- GitHub projects
| |-- LinkedIn posts
| |-- Open source contributions

Free Resources to learn SQL

• W3Schools SQL
https://www.w3schools.com/sql/

• SQL Programming
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

• SQL Notes
https://whatsapp.com/channel/0029Vb6hJmM9hXFCWNtQX944

• Mode Analytics SQL tutorials
https://mode.com/sql-tutorial/

• Data Analytics Resources
https://t.me/sqlspecialist

• HackerRank SQL practice
https://www.hackerrank.com/domains/sql

• LeetCode SQL problems
https://leetcode.com/problemset/database/

• Data Engineering Resources
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

• Khan Academy SQL basics
https://www.khanacademy.org/computing/computer-programming/sql

• PostgreSQL official docs
https://www.postgresql.org/docs/

• MySQL official docs
https://dev.mysql.com/doc/

• NoSQL Resources
https://whatsapp.com/channel/0029VaxA2hTHgZWe5FpFjm3p

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✅ Power BI Interview Questions 🎯📊

1️⃣ What is Power BI?
A Microsoft tool for data visualization, reporting, and business intelligence.

2️⃣ What are the building blocks of Power BI?
• Datasets
• Reports
• Dashboards
• Tiles
• Visualizations

3️⃣ Difference between Power BI Desktop and Power BI Service?
• Desktop: Used to create and design reports
• Service: Cloud-based platform to share and collaborate

4️⃣ What is Power Query?
A data transformation tool for cleaning and shaping data before loading into the model.

5️⃣ What is DAX?
Data Analysis Expressions – a formula language used for calculations in Power BI.

6️⃣ What are measures and calculated columns?
• Measure: Calculated on aggregation (e.g. SUM of sales)
• Calculated Column: Row-level computation (e.g. profit = revenue - cost)

7️⃣ What is a slicer?
A visual filter that allows users to dynamically filter data on a report.

8️⃣ How do you handle data refresh in Power BI?
• Schedule refresh via Power BI Service
• Use gateways for on-prem data sources

9️⃣ What is the difference between direct query and import mode?
• Import: Data is loaded into Power BI
• Direct Query: Queries run directly on the source in real time

🔟 What is the Power BI Gateway?
A bridge between on-premise data sources and Power BI cloud service.

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Steps to become a data analyst

Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - https://t.me/learndataanalysis

Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:

SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst

Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst

Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst

Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst

Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).

Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.

Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.

Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.

Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.

Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss

Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.

Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.

Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.

Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.

ENJOY LEARNING 👍👍

Excel Shortcut Keys You Should Know!

1. Save file → Ctrl + S
2. Undo last action → Ctrl + Z
3. Redo action → Ctrl + Y
4. Cut selection → Ctrl + X
5. Paste → Ctrl + V
6. Select entire row → Shift + Space
7. Select entire column → Ctrl + Space
8. Insert new worksheet → Shift + F11
9. Rename sheet → Alt + H, O, R
10. AutoSum → Alt + =
11. Edit active cell → F2
12. Lock cell reference → F4
13. Apply filter → Ctrl + Shift + L
14. Insert current date → Ctrl + ;
15. Insert current time → Ctrl + Shift + :

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🚀 Real SQL Interview Question Reported in a Swiggy Business Analyst Interview

Question:

Given an orders table with the following columns:

driver_id
order_time
delivered_time

Write an SQL query to calculate the average waiting/delivery time (in minutes) for each delivery partner.

✅ SQL Solution (MySQL)

SELECT
driver_id,
AVG(TIMESTAMPDIFF(MINUTE, order_time, delivered_time)) AS avg_delivery_time
FROM orders
GROUP BY driver_id;

💡 Approach:

• Calculate the time difference between order_time and delivered_time.
• Convert the difference into minutes using TIMESTAMPDIFF().
• Group records by driver_id.
• Use AVG() to find the average delivery time for each delivery partner.

📚 Concepts Tested:

• Date & Time Functions
• GROUP BY
• Aggregate Functions (AVG)
• Business Metrics

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Excel Basics for Data Analytics

Excel sits at the start of most analysis work.

What you use Excel for
• Cleaning raw data
• Exploring patterns
• Quick summaries for teams

Core concepts you must know
• Data setup
– Freeze header row. View → Freeze Top Row.
– Convert range to table. Ctrl + T.
– Use proper headers. No merged cells. One value per cell.

• Data cleaning
– Remove duplicates. Data → Remove Duplicates.
– Trim extra spaces. =TRIM(A2)
– Convert text to numbers. =VALUE(A2)
– Fix date format. Format Cells → Date.
– Handle blanks. Filter blanks, fill or delete.
– Find and replace. Ctrl + H.

• Essential formulas
– Math and counts
▪ SUM. =SUM(A2:A100)
▪ AVERAGE. =AVERAGE(A2:A100)
▪ MIN. =MIN(A2:A100)
▪ MAX. =MAX(A2:A100)
▪ COUNT. Counts numbers.
▪ COUNTA. Counts non blanks.
▪ COUNTBLANK. Counts blanks.
– Conditional formulas
▪ IF. =IF(A2>5000,"High","Low")
▪ IFS. Multiple conditions.
▪ AND. =AND(A2>5000,B2="West")
▪ OR. =OR(A2>5000,A2<1000)
– Lookup formulas
▪ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
▪ VLOOKUP. Old but common.
▪ INDEX + MATCH. Powerful alternative.
– Text formulas
▪ LEFT. =LEFT(A2,4)
▪ RIGHT. =RIGHT(A2,2)
▪ MID. =MID(A2,2,3)
▪ LEN. =LEN(A2)
▪ CONCAT or TEXTJOIN.
▪ LOWER, UPPER, PROPER.
– Date formulas
▪ TODAY. Current date.
▪ NOW. Date and time.
▪ YEAR, MONTH, DAY.
▪ DATEDIF. Date difference.
▪ EOMONTH. Month end.

• Sorting and filtering
– Sort by multiple columns.
– Filter by value, color, condition.
– Top 10 filter for quick insights.

• Conditional formatting
– Highlight duplicates.
– Color scales for trends.
– Rules for thresholds. Example. Sales > 10000 in green.

• Pivot tables
– Insert → PivotTable.
– Rows. Category or Product.
– Values. Sum, Count, Average.
– Filters. Date, Region.
– Refresh after data update.

• Charts you must know
– Column. Comparison.
– Bar. Ranking.
– Line. Trends over time.
– Pie. Share or percentage.
– Combo. Actual vs target.

• Data validation
– Dropdown list. Data → Data Validation → List.
– Prevent wrong entries.

• Useful shortcuts
– Ctrl + Arrow. Jump data.
– Ctrl + Shift + Arrow. Select range.
– Ctrl + 1. Format cells.
– Ctrl + L. Apply filter.
– Alt + =. Auto sum.
– Ctrl + Z / Y. Undo redo.

• Common analyst mistakes to avoid
– Merged cells.
– Hard coded totals.
– Mixed data types in one column.
– No backup before cleaning.

• Daily practice task
– Download any sales CSV.
– Clean it.
– Build one pivot table.
– Create one chart.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354

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