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Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
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Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data
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๐ ๐ง๐๐ฆ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฎ๐ฌ๐ฎ๐ฒ โ ๐๐ป๐ฟ๐ผ๐น๐น ๐ก๐ผ๐!
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๐ฅ Excellent opportunity to gain valuable certifications from one of Indiaโs top IT companies completely FREE.
| 2 | ๐ฅ Top SQL Interview Questions with Answers
๐ฏ 1๏ธโฃ Find 2nd Highest Salary
๐ Table: employees
id | name | salary
1 | Rahul | 50000
2 | Priya | 70000
3 | Amit | 60000
4 | Neha | 70000
โ Problem Statement: Find the second highest distinct salary from the employees table.
โ
Solution
SELECT MAX(salary) FROM employees WHERE salary < ( SELECT MAX(salary) FROM employees );
๐ฏ 2๏ธโฃ Find Nth Highest Salary
๐ Table: employees
id | name | salary
1 | A | 100
2 | B | 200
3 | C | 300
4 | D | 200
โ Problem Statement: Write a query to find the 3rd highest salary.
โ
Solution
SELECT salary FROM ( SELECT salary, DENSE_RANK() OVER(ORDER BY salary DESC) r FROM employees ) t WHERE r = 3;
๐ฏ 3๏ธโฃ Find Duplicate Records
๐ Table: employees
id | name
1 | Rahul
2 | Amit
3 | Rahul
4 | Neha
โ Problem Statement: Find all duplicate names in the employees table.
โ
Solution
SELECT name, COUNT(*) FROM employees GROUP BY name HAVING COUNT(*) > 1;
๐ฏ 4๏ธโฃ Customers with No Orders
๐ Table: customers
customer_id | name
1 | Rahul
2 | Priya
3 | Amit
๐ Table: orders
order_id | customer_id
101 | 1
102 | 2
โ Problem Statement: Find customers who have not placed any orders.
โ
Solution
SELECT c.name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id WHERE o.customer_id IS NULL;
๐ฏ 5๏ธโฃ Top 3 Salaries per Department
๐ Table: employees
name | department | salary
A | IT | 100
B | IT | 200
C | IT | 150
D | HR | 120
E | HR | 180
โ Problem Statement: Find the top 3 highest salaries in each department.
โ
Solution
SELECT * FROM ( SELECT name, department, salary, ROW_NUMBER() OVER( PARTITION BY department ORDER BY salary DESC ) r FROM employees ) t WHERE r <= 3;
๐ฏ 6๏ธโฃ Running Total of Sales
๐ Table: sales
date | sales
2024-01-01 | 100
2024-01-02 | 200
2024-01-03 | 300
โ Problem Statement: Calculate the running total of sales by date.
โ
Solution
SELECT date, sales, SUM(sales) OVER(ORDER BY date) AS running_total FROM sales;
๐ฏ 7๏ธโฃ Employees Above Average Salary
๐ Table: employees
name | salary
A | 100
B | 200
C | 300
โ Problem Statement: Find employees earning more than the average salary.
โ
Solution
SELECT name, salary FROM employees WHERE salary > ( SELECT AVG(salary) FROM employees );
๐ฏ 8๏ธโฃ Department with Highest Total Salary
๐ Table: employees
name | department | salary
A | IT | 100
B | IT | 200
C | HR | 500
โ Problem Statement: Find the department with the highest total salary.
โ
Solution
SELECT department, SUM(salary) AS total_salary FROM employees GROUP BY department ORDER BY total_salary DESC LIMIT 1;
๐ฏ 9๏ธโฃ Customers Who Placed Orders
๐ Tables: Same as Q4
โ Problem Statement: Find customers who have placed at least one order.
โ
Solution
SELECT name FROM customers c WHERE EXISTS ( SELECT 1 FROM orders o WHERE c.customer_id = o.customer_id );
๐ฏ ๐ Remove Duplicate Records
๐ Table: employees
id | name
1 | Rahul
2 | Rahul
3 | Amit
โ Problem Statement: Delete duplicate records but keep one unique record.
โ
Solution
DELETE FROM employees WHERE id NOT IN ( SELECT MIN(id) FROM employees GROUP BY name );
๐ Pro Tip:
๐ In interviews:
First explain logic
Then write query
Then optimize
Double Tap โฅ๏ธ For More | 570 |
| 3 |  ๐ง๐ผ๐ฝ ๐ฏ ๐๐ฅ๐๐ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฒ! ๐๐ป
These FREE certification courses can help you build strong programming skills and stand out from the crowd ๐
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๐ Perfect for students, freshers, aspiring developers, data analysts, and tech enthusiasts.
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๐ Start learning today and level up your career with Python! | 642 |
| 4 |  Confused between ML, NLP, Generative, and other AI models? ๐ค
Hereโs a quick breakdown of the 6 most important types of AI models you must understand in 2026๐
1. Machine Learning Models ๐ค
They learn from labeled and unlabeled data to classify, predict, and detect patterns. Think decision trees, SVMs, and XGBoost.
2. Deep Learning Models ๐ง
Neural networks built for unstructured data like images, audio, and text. Includes CNNs, RNNs, Transformers, and GANs.
3. NLP Models ๐ฌ
Focused on understanding and generating human language - used in chatbots, summarizers, and assistants like GPT and BERT.
4. Generative Models โจ
These models create, from text to images to music. Powered by models like GPT-4, DALLยทE, and StyleGAN.
5. Hybrid Models ๐
Combine the best of rule-based and neural AI. Perfect for use cases needing both reasoning and context awareness (e.g., RAG pipelines).
6. Computer Vision Models ๐
Built for images and videos. Used in object detection, facial recognition, and medical scans - powered by models like YOLO and ResNet.
Each AI model has its strengths and knowing which one fits your use case is half the battle. Save this guide as your cheat sheet! ๐โ
| 933 |
| 5 |  ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ถ๐๐ต ๐๐ฒ๐ป๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ช๐ฒ๐ฏ๐ถ๐ป๐ฎ๐ฟ ๐
AI is replacing analysts who don't adapt.
Learn Data Analytics + GenAI with IBM & Microsoft certifications. Land your dream role with dedicated placement support.
๐1200+ Hiring Partners. 128% avg hike. 35 LPA Highest CTC in Placements.
๐ซ๐๐ผ๐ผ๐ธ ๐๐ผ๐๐ฟ ๐๐ฅ๐๐ ๐๐ฒ๐ฏ๐ถ๐ป๐ฎ๐ฟ :-
https://pdlink.in/4uwBw3q
Hurry Up โโ๏ธ! Limited seats are available. | 765 |
| 6 | โ
SQL Roadmap: Step-by-Step Guide to Master SQL ๐ง ๐ป
Whether you're aiming to be a backend dev, data analyst, or full-time SQL pro โ this roadmap has got you covered ๐
๐ 1. SQL Basics
โฆย SELECT, FROM, WHERE
โฆย ORDER BY, LIMIT, DISTINCTย
ย ย Learn data retrieval & filtering.
๐ 2. Joins Mastery
โฆย INNER JOIN, LEFT/RIGHT/FULL OUTER JOIN
โฆย SELF JOIN, CROSS JOINย
ย ย Master table relationships.
๐ 3. Aggregate Functions
โฆย COUNT(), SUM(), AVG(), MIN(), MAX()ย
ย ย Key for reporting & analytics.
๐ 4. Grouping Data
โฆย GROUP BY to group
โฆย HAVING to filter groupsย
ย ย Example: Sales by region, top categories.
๐ 5. Subqueries & Nested Queries
โฆย Use subqueries in WHERE, FROM, SELECT
โฆย Use EXISTS, IN, ANY, ALLย
ย ย Build complex logic without extra joins.
๐ 6. Data Modification
โฆย INSERT INTO, UPDATE, DELETE
โฆย MERGE (advanced)ย
ย ย Safely change dataset content.
๐ 7. Database Design Concepts
โฆย Normalization (1NF to 3NF)
โฆย Primary, Foreign, Unique Keysย
ย ย Design scalable, clean DBs.
๐ 8. Indexing & Query Optimization
โฆย Speed queries with indexes
โฆย Use EXPLAIN, ANALYZE to tuneย
ย ย Vital for big data/enterprise work.
๐ 9. Stored Procedures & Functions
โฆย Reusable logic, control flow (IF, CASE, LOOP)ย
ย ย Backend logic inside the DB.
๐ 10. Transactions & Locks
โฆย ACID properties
โฆย BEGIN, COMMIT, ROLLBACK
โฆย Lock types (SHARED, EXCLUSIVE)ย
ย ย Prevent data corruption in concurrency.
๐ 11. Views & Triggers
โฆย CREATE VIEW for abstraction
โฆย TRIGGERS auto-run SQL on eventsย
ย ย Automate & maintain logic.
๐ 12. Backup & Restore
โฆย Backup/restore with tools (mysqldump, pg_dump)ย
ย ย Keep your data safe.
๐ 13. NoSQL Basics (Optional)
โฆย Learn MongoDB, Redis basics
โฆย Understand where SQL ends & NoSQL begins.
๐ 14. Real Projects & Practice
โฆย Build projects: Employee DB, Sales Dashboard, Blogging System
โฆย Practice on LeetCode, StrataScratch, HackerRank
๐ 15. Apply for SQL Dev Roles
โฆย Tailor resume with projects & optimization skills
โฆย Prepare for interviews with SQL challenges
โฆย Know common business use cases
๐ก Pro Tip: Combine SQL with Python or Excel to boost your data career options.
๐ฌ Double Tap โฅ๏ธ For More! | 907 |
| 7 |  ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
โจ Learn In-Demand Tech Skills
โจ Boost Your Resume & LinkedIn Profile
โจ Improve Career Opportunities
โจ Self-Paced Online Learning
โจ Great for Freshers & Students
๐ ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:
https://pdlink.in/49p31Uh
๐ฅ Start learning today and prepare for high-paying tech careers with Microsoft free certification programs | 751 |
| 8 | โ
Step-by-Step Approach to Learn Data Analytics ๐๐ง
โ Excel Fundamentals:
โ Master formulas, pivot tables, data validation, charts, and graphs.
โ SQL Basics:
โ Learn to query databases, use SELECT, FROM, WHERE, JOIN, GROUP BY, and aggregate functions.
โ Data Visualization:
โ Get proficient with tools like Tableau or Power BI to create insightful dashboards.
โ Statistical Concepts:
โ Understand descriptive statistics (mean, median, mode), distributions, and hypothesis testing.
โ Data Cleaning & Preprocessing:
โ Learn how to handle missing data, outliers, and data inconsistencies.
โ Exploratory Data Analysis (EDA):
โ Explore datasets, identify patterns, and formulate hypotheses.
โ Python for Data Analysis (Optional but Recommended):
โ Learn Pandas and NumPy for data manipulation and analysis.
โ Real-World Projects:
โ Analyze datasets from Kaggle, UCI Machine Learning Repository, or your own collection.
โ Business Acumen:
โ Understand key business metrics and how data insights impact business decisions.
โ Build a Portfolio:
โ Showcase your projects on GitHub, Tableau Public, or a personal website. Highlight the impact of your analysis.
๐ Tap โค๏ธ for more! | 793 |
| 9 |  ๐๐ & ๐ ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ ๐ฏ๐ ๐๐๐, ๐๐๐ง ๐ ๐ฎ๐ป๐ฑ๐ถ๐
Freshers get 15 LPA Average Salary with AI & ML Skills!
- Eligibility: Open to everyone
- Duration: 6 Months
- Program Mode: Online
- Taught By: IIT Mandi Professors
90% Resumes without AI + ML skills are being rejected.
ย ๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐ :-ย
https://pdlink.in/4nmI024
Get Placement Assistance With 5000+ Companies | 849 |
| 10 | FREE sites to improve your coding knowledge ๐จ๐ปโ๐ป๐ -
๐ HTML - w3schools.com
๐
CSS - web.dev/learn/css
๐ฅ JavaScript - javascript.info
๐ Git and Github - git-scm.com
๐ API - rapidapi.com/learn
๐ Python - t.me/pythonproz
โ๏ธ React - react-tutorial.app
๐ก Laravel - laracasts.com
๐ VueJS - learnvue.co
๐ SQL - t.me/sqlspecialist
๐ Tailwind CSS - tailwindcss.com
๐ Go - gobyexample.com
๐ณ Docker - docker-curriculum.com
๐ฆ Flutter - flutter.dev/learn
๐ฆ Rust - rust-lang.org/learn
๐ง AI/ML - t.me/machinelearning_deeplearning
โ๏ธ DevOps - t.me/AWS_GCP_Azure
๐งฉ TypeScript - typescriptlang.org/learn
React โค๏ธ for more like this | 968 |
| 11 |  ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ถ๐๐ต ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ | ๐ญ๐ฌ๐ฌ% ๐๐ผ๐ฏ ๐๐๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ๐
Build Python, Machine Learning, and AI Skills
๐ซ60+ Hiring Drives Every Month | Receive 1-on-1 mentorship
12.65 Lakhs Highest Salary | 500+ Partner Companies
๐๐ผ๐ผ๐ธ ๐ฎ ๐๐ฅ๐๐ ๐ฆ๐ฒ๐๐๐ถ๐ผ๐ป :- ๐:-
ย Online :- https://pdlink.in/4fdWxJB
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Hurry Up ๐โโ๏ธ! Limited seats are available. | 911 |
| 12 | ๐ Data Analytics Career Paths & What to Learn ๐ง ๐
๐งฎ 1. Data Analyst
โถ๏ธ Tools: Excel, SQL, Power BI, Tableau
โถ๏ธ Skills: Data cleaning, data visualization, business metrics
โถ๏ธ Languages: Python (Pandas, Matplotlib)
โถ๏ธ Projects: Sales dashboards, customer insights, KPI reports
๐ 2. Business Analyst
โถ๏ธ Tools: Excel, SQL, PowerPoint, Tableau
โถ๏ธ Skills: Requirements gathering, stakeholder communication, data storytelling
โถ๏ธ Domain: Finance, Retail, Healthcare
โถ๏ธ Projects: Market analysis, revenue breakdowns, business forecasts
๐ง 3. Data Scientist
โถ๏ธ Tools: Python, R, Jupyter, Scikit-learn
โถ๏ธ Skills: Statistics, ML models, feature engineering
โถ๏ธ Projects: Churn prediction, sentiment analysis, classification models
๐งฐ 4. Data Engineer
โถ๏ธ Tools: SQL, Python, Spark, Airflow
โถ๏ธ Skills: Data pipelines, ETL, data warehousing
โถ๏ธ Platforms: AWS, GCP, Azure
โถ๏ธ Projects: Real-time data ingestion, data lake setup
๐ฆ 5. Product Analyst
โถ๏ธ Tools: Mixpanel, SQL, Excel, Tableau
โถ๏ธ Skills: User behavior analysis, A/B testing, retention metrics
โถ๏ธ Projects: Feature adoption, funnel analysis, product usage trends
๐ 6. Marketing Analyst
โถ๏ธ Tools: Google Analytics, Excel, SQL, Looker
โถ๏ธ Skills: Campaign tracking, ROI analysis, segmentation
โถ๏ธ Projects: Ad performance, customer journey, CLTV analysis
๐งช 7. Analytics QA (Data Quality Tester)
โถ๏ธ Tools: SQL, Python (Pytest), Excel
โถ๏ธ Skills: Data validation, report testing, anomaly detection
โถ๏ธ Projects: Dataset audits, test case automation for dashboards
๐ก Tip: Pick a role โ Learn tools โ Practice with real datasets โ Build a portfolio โ Share insights
๐ฌ Tap โค๏ธ for more! | 1 046 |
| 13 | 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
โข W3Schools SQL
https://www.w3schools.com/sql/
โข SQL Programming
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
โข SQL Notes
https://whatsapp.com/channel/0029Vb6hJmM9hXFCWNtQX944
โข Mode Analytics SQL tutorials
https://mode.com/sql-tutorial/
โข Data Analytics Resources
https://t.me/sqlspecialist
โข HackerRank SQL practice
https://www.hackerrank.com/domains/sql
โข LeetCode SQL problems
https://leetcode.com/problemset/database/
โข Data Engineering Resources
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
โข Khan Academy SQL basics
https://www.khanacademy.org/computing/computer-programming/sql
โข PostgreSQL official docs
https://www.postgresql.org/docs/
โข MySQL official docs
https://dev.mysql.com/doc/
โข NoSQL Resources
https://whatsapp.com/channel/0029VaxA2hTHgZWe5FpFjm3p
Double Tap โค๏ธ For More | 1 034 |
| 14 |  ๐๐/๐ ๐ ๐ฟ๐ผ๐น๐ฒ๐ ๐ฎ๐ฟ๐ฒ ๐ณ๐ฎ๐๐๐ฒ๐๐-๐ด๐ฟ๐ผ๐๐ถ๐ป๐ด ๐ฐ๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ณ๐ถ๐ฒ๐น๐ฑ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฒ๐
The demand is real, salaries are high, and the talent gap is wide open
Enrol for AI/ML Certification Program by CCE, IIT Mandi!
Eligibility: Open to everyone
Duration: 6 Months
Program Mode: Online
Taught By: IIT Mandi Professors
Deadline :- 23rd May
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| 15 | ๐ Complete SQL Syllabus Roadmap (Beginner to Expert) ๐๏ธ
๐ฐ Beginner Level:
1. Intro to Databases: What are databases, Relational vs. Non-Relational
2. SQL Basics: SELECT, FROM, WHERE
3. Data Types: INT, VARCHAR, DATE, BOOLEAN, etc.
4. Operators: Comparison, Logical (AND, OR, NOT)
5. Sorting & Filtering: ORDER BY, LIMIT, DISTINCT
6. Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
7. GROUP BY and HAVING: Grouping Data and Filtering Groups
8. Basic Projects: Creating and querying a simple database (e.g., a student database)
โ๏ธ Intermediate Level:
1. Joins: INNER, LEFT, RIGHT, FULL OUTER JOIN
2. Subqueries: Using queries within queries
3. Indexes: Improving Query Performance
4. Data Modification: INSERT, UPDATE, DELETE
5. Transactions: ACID Properties, COMMIT, ROLLBACK
6. Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT
7. Views: Creating Virtual Tables
8. Stored Procedures & Functions: Reusable SQL Code
9. Date and Time Functions: Working with Date and Time Data
10. Intermediate Projects: Designing and querying a more complex database (e.g., an e-commerce database)
๐ Expert Level:
1. Window Functions: RANK, ROW_NUMBER, LAG, LEAD
2. Common Table Expressions (CTEs): Recursive and Non-Recursive
3. Performance Tuning: Query Optimization Techniques
4. Database Design & Normalization: Understanding Database Schemas (Star, Snowflake)
5. Advanced Indexing: Clustered, Non-Clustered, Filtered Indexes
6. Database Administration: Backup and Recovery, Security, User Management
7. Working with Large Datasets: Partitioning, Data Warehousing Concepts
8. NoSQL Databases: Introduction to MongoDB, Cassandra, etc. (optional)
9. SQL Injection Prevention: Secure Coding Practices
10. Expert Projects: Designing, optimizing, and managing a large-scale database (e.g., a social media database)
๐ก Bonus: Learn about Database Security, Cloud Databases (AWS RDS, Azure SQL Database, Google Cloud SQL), and Data Modeling Tools.
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| 16 | ๐น DATA ANALYST โ INTERVIEW REVISION SHEET
1๏ธโฃ Role Clarity
> โA data analyst collects, cleans, analyzes data, and converts it into insights that help businesses make decisions.โ
2๏ธโฃ SQL (Most Important)
Must-know clauses:
โข SELECT, WHERE, ORDER BY, LIMIT
โข GROUP BY, HAVING
โข JOINS (INNER, LEFT)
โข Subqueries, CTEs
โข Window functions (ROW_NUMBER, RANK)
Golden rules:
โข WHERE โ before aggregation
โข HAVING โ after aggregation
โข LEFT JOIN โ keeps all left table rows
โข NULLs break calculations โ use COALESCE
Classic questions:
โข Top N per group
โข Find duplicates
โข Running totals
3๏ธโฃ Excel Essentials
Formulas:
โข IF, XLOOKUP
โข COUNTIFS, SUMIFS
โข TRIM, LEFT, RIGHT
Core features:
โข Pivot tables
โข Conditional formatting
โข Data validation (dropdowns)
Avoid:
โข Merged cells
โข Hard-coded values
4๏ธโฃ Power BI / Tableau
Concepts:
โข Data model (star schema)
โข Relationships (one-to-many)
โข Measures > calculated columns
Must-know DAX:
โข Total Sales = SUM(Sales[Amount])
โข YTD Sales = TOTALYTD(SUM(Sales[Amount]), Sales[Date])
Design rules:
โข KPIs on top
โข One story per dashboard
โข Minimal visuals
5๏ธโฃ Statistics (Only What Matters)
โข Mean vs Median
โข Standard deviation
โข Correlation โ causation
โข Outliers distort averages
โข Use median for Salaries, House prices
6๏ธโฃ Data Cleaning (Interview Gold)
Steps you should say:
1. Remove duplicates
2. Handle missing values
3. Fix data types
4. Standardize text
7๏ธโฃ Business Metrics
โข Revenue
โข Growth rate
โข Conversion rate
โข Churn
โข Retention
โข Average order value
Always connect metrics to business impact.
8๏ธโฃ Case Question Framework (Very Important)
Always answer like this:
1. What happened
2. Why it happened
3. What should be done
Example:
> โSales dropped due to lower traffic in one region, so Iโd recommend increasing marketing spend there.โ
9๏ธโฃ Project Explanation Template
> โThe goal was . I used to clean data, to analyze, and to visualize. The key insight was . The business impact was .โ
Memorize this.
๐ HR Power Answers
Why data analyst?
> โI enjoy finding patterns in data and turning them into actionable insights.โ
Strength:
โI combine technical skills with business understanding.โ
Weakness:
โI used to over-analyze, but now I focus on impact.โ
๐ง Last-Day Interview Tips
โข Think out loud
โข Ask clarifying questions
โข Donโt jump to tools immediately
โข Focus on impact, not syntax
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| 18 | โ
8-Week Beginner Roadmap to Learn Data Analysis ๐
๐๏ธ Week 1: Excel & Data Basicsย
Goal: Master data organization and analysis basicsย
Topics: Excel formulas, functions, PivotTables, data cleaningย
Tools: Microsoft Excel, Google Sheetsย
Mini Project: Analyze sales or survey data with PivotTables
๐๏ธ Week 2: SQL Fundamentalsย
Goal: Learn to query databases efficientlyย
Topics: SELECT, WHERE, JOIN, GROUP BY, subqueriesย
Tools: MySQL, PostgreSQL, SQLiteย
Mini Project: Query sample customer or sales database
๐๏ธ Week 3: Data Visualization Basicsย
Goal: Create meaningful charts and graphsย
Topics: Bar charts, line charts, scatter plots, dashboardsย
Tools: Tableau, Power BI, Excel chartsย
Mini Project: Build dashboard to analyze sales trends
๐๏ธ Week 4: Data Cleaning & Preparationย
Goal: Handle messy data for analysisย
Topics: Handling missing values, duplicates, data typesย
Tools: Excel, Python (Pandas) basicsย
Mini Project: Clean and prepare real-world dataset for analysis
๐๏ธ Week 5: Statistics for Data Analysisย
Goal: Understand key statistical conceptsย
Topics: Descriptive stats, distributions, correlation, hypothesis testingย
Tools: Excel, Python (SciPy, NumPy)ย
Mini Project: Analyze survey data & draw insights
๐๏ธ Week 6: Advanced SQL & Database Conceptsย
Goal: Optimize queries & explore database design basicsย
Topics: Window functions, indexes, normalizationย
Tools: SQL Server, MySQLย
Mini Project: Complex query for sales and customer analysis
๐๏ธ Week 7: Automating Analysis with Pythonย
Goal: Use Python for repetitive data tasksย
Topics: Pandas automation, data aggregation, visualization scriptingย
Tools: Jupyter Notebook, Pandas, Matplotlibย
Mini Project: Automate monthly sales report generation
๐๏ธ Week 8: Capstone Project + Reportingย
Goal: End-to-end analysis and presentationย
Project Ideas: Customer segmentation, sales forecasting, churn analysisย
Tools: Tableau/Power BI for visualization + Python/SQL for backendย
Bonus: Present findings in a polished report or dashboard
๐ก Tips:
โฆย Practice querying and analysis on public datasets (Kaggle, data.gov)
โฆย Join data challenges and community projects
๐ฌ Tap โค๏ธ for the detailed explanation of each topic! | 1 025 |
| 19 |  ๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ข๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ( ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐)๐
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| 20 | โ
SQL Mistakes Beginners Should Avoid ๐ง ๐ป
1๏ธโฃ Using SELECT *
โข Pulls unused columns
โข Slows queries
โข Breaks when schema changes
โข Use only required columns
2๏ธโฃ Ignoring NULL Values
โข NULL breaks calculations
โข COUNT(column) skips NULL
โข Use COALESCE or IS NULL checks
3๏ธโฃ Wrong JOIN Type
โข INNER instead of LEFT
โข Data silently disappears
โข Always ask: Do you need unmatched rows?
4๏ธโฃ Missing JOIN Conditions
โข Creates cartesian product
โข Rows explode
โข Always join on keys
5๏ธโฃ Filtering After JOIN Instead of Before
โข Processes more rows than needed
โข Slower performance
โข Filter early using WHERE or subqueries
6๏ธโฃ Using WHERE Instead of HAVING
โข WHERE filters rows
โข HAVING filters groups
โข Aggregates fail without HAVING
7๏ธโฃ Not Using Indexes
โข Full table scans
โข Slow dashboards
โข Index columns used in JOIN, WHERE, ORDER BY
8๏ธโฃ Relying on ORDER BY in Subqueries
โข Order not guaranteed
โข Results change
โข Use ORDER BY only in final query
9๏ธโฃ Mixing Data Types
โข Implicit conversions
โข Index not used
โข Match column data types
๐ No Query Validation
โข Results look right but are wrong
โข Always cross-check counts and totals
๐ง Practice Task
โข Rewrite one query
โข Remove SELECT *
โข Add proper JOIN
โข Handle NULLs
โข Compare result count
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