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𝐒𝐐𝐋 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩🔥🔥🔥 |── Basics | ├── What is SQL? | ├── Database vs DBMS vs RDBMS | ├── Databases & Tables | ├── Rows vs Columns | ├── Data Types (INT, VARCHAR, DATE, FLOAT, BOOLEAN) | ├── Constraints (NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, CHECK, DEFAULT) | ├── Keys (Primary, Foreign, Candidate, Composite, Super Key) | └── CRUD Operations (Create, Read, Update, Delete) | |── DDL (Data Definition Language) | ├── CREATE DATABASE | ├── CREATE TABLE | ├── ALTER TABLE | ├── DROP TABLE | ├── TRUNCATE TABLE | └── RENAME TABLE | |── DML (Data Manipulation Language) | ├── INSERT INTO | ├── UPDATE | ├── DELETE | └── Bulk Inserts | |── DQL (Data Query Language) | ├── SELECT | ├── Column Selection | ├── Aliases (AS) | └── Expressions & Calculations | |── Data Retrieval | ├── SELECT, FROM, WHERE | ├── DISTINCT | ├── ORDER BY (ASC, DESC) | ├── LIMIT / TOP / OFFSET-FETCH | ├── BETWEEN | ├── IN / NOT IN | ├── LIKE (%, _) | └── IS NULL / IS NOT NULL | |── Filtering & Conditions | ├── AND, OR, NOT | ├── Operator Precedence | ├── Nested Conditions | └── Short-circuit Evaluation | |── Joins | ├── INNER JOIN | ├── LEFT JOIN | ├── RIGHT JOIN | ├── FULL OUTER JOIN | ├── CROSS JOIN | ├── SELF JOIN | ├── Join Conditions (ON vs WHERE) | └── Handling NULLs in Joins | |── Grouping & Aggregation | ├── GROUP BY | ├── Aggregate Functions: COUNT(), SUM(), AVG(), MIN(), MAX() | ├── HAVING | ├── Conditional Aggregation (CASE WHEN) | └── Grouping Rules & Errors | |── CASE Statements & Conditional Logic | ├── CASE WHEN | ├── Nested CASE | ├── Conditional Columns | └── Conditional Aggregations | |── NULL Handling | ├── NULL Behavior in SQL | ├── IS NULL, IS NOT NULL | ├── COALESCE() | ├── NULLIF() | └── NULL in Aggregations | |── Subqueries & Nested Queries | ├── Subquery in SELECT | ├── Subquery in WHERE | ├── Subquery in FROM | ├── Correlated Subqueries | ├── Scalar vs Multi-row Subqueries | └── Performance Considerations | |── Set Operations | ├── UNION | ├── UNION ALL | ├── INTERSECT | └── EXCEPT / MINUS | |── Advanced SQL | ├── EXISTS / NOT EXISTS | ├── Derived Tables | ├── Inline Views | ├── Pivoting & Unpivoting | └── Dynamic SQL (Basics) | |── Window Functions (Analytical SQL) | ├── OVER() Clause | ├── PARTITION BY | ├── ORDER BY in Window | ├── Ranking: ROW_NUMBER(), RANK(), DENSE_RANK() | ├── Value Functions: LEAD(), LAG() | ├── Aggregates as Window Functions | └── Running Totals & Moving Averages | |── Common Table Expressions (CTEs) | ├── WITH Clause | ├── Multiple CTEs | ├── Recursive CTEs | └── CTE vs Subquery | |── Views | ├── Creating Views | ├── Updating Views | ├── Materialized Views | └── Use Cases | |── Indexes & Performance | ├── What is Index | ├── Clustered vs Non-Clustered Index | ├── Composite Index | ├── Indexing Strategies | ├── Query Optimization | ├── Execution Plan | └── EXPLAIN / ANALYZE | |── Transactions & ACID | ├── Transaction Basics | ├── COMMIT, ROLLBACK, SAVEPOINT | ├── ACID Properties | └── Concurrency Issues | |── Locks & Isolation Levels | ├── Lock Types | ├── Isolation Levels | ├── Dirty Read, Non-repeatable Read, Phantom Read | └── Deadlocks | |── Database Design Concepts | ├── ER Diagrams | ├── Normalization (1NF, 2NF, 3NF, BCNF) | ├── Denormalization | ├── Relationships (1-1, 1-M, M-M) | └── Schema Design Best Practices | |── Data Warehousing Concepts | ├── OLTP vs OLAP | ├── Fact & Dimension Tables | ├── Star Schema | ├── Snowflake Schema | └── ETL Basics | |── SQL for Data Analysis | ├── Business Metrics (Revenue, Retention, AOV) | ├── Cohort Analysis | ├── Funnel Analysis | ├── Time Series Analysis | └── Data Cleaning in SQL | |── SQL in Real Projects | ├── E-commerce Analysis | ├── Customer Behavior Analysis | ├── Sales Dashboard Queries | └── KPI Reporting | |── Tools & Platforms | ├── MySQL | ├── PostgreSQL | ├── SQL Server | ├── Oracle | ├── SQLite | ├── BigQuery | ├── Snowflake | └── Amazon Redshift | |── END 👉WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉Telegram Channel: https://t.me/dataanalyticsbuddy Till then keep learning and keep exploring 🙌 😊

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 2026 🔥 |── Foundations (Business + Analytics Thinking) | ├── What is Data Analysis? | ├── Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive) | ├── Business Metrics (Revenue, Profit, Growth, Retention, CAC, LTV) | ├── KPI vs Metrics | ├── Data-driven Decision Making | ├── Problem Solving Framework | └── Asking Business Questions | |── Excel (Core Tool – Still Widely Used) | ├── Basics (Cells, Sheets, Formatting) | ├── Formulas (SUM, IF, COUNT, AVERAGE) | ├── Lookup Functions (VLOOKUP, XLOOKUP, INDEX-MATCH) | ├── Pivot Tables & Pivot Charts | ├── Data Cleaning (Text functions, Remove duplicates) | ├── Conditional Formatting | ├── Basic Dashboards | └── Excel Automation (Basic Macros) | |── Python for Data Analysis | ├── Python Basics (Variables, Data Types) | ├── Control Flow (if, for, while) | ├── Functions | ├── Error Handling (try-except) | ├── Data Structures (List, Tuple, Set, Dictionary) | ├── List & Dict Comprehensions | ├── NumPy (Arrays, Vectorization) | ├── Pandas (DataFrames, Cleaning, Transformation) | ├── GroupBy & Aggregations | ├── Merge, Join, Pivot | ├── Time Series Basics | ├── Data Visualization (Matplotlib, Seaborn) | └── Automation Scripts | |── SQL (Core Skill – Must Have) | ├── SELECT, WHERE, ORDER BY | ├── Joins (INNER, LEFT, RIGHT, FULL) | ├── GROUP BY & Aggregations | ├── CASE WHEN | ├── Subqueries | ├── CTEs | ├── Window Functions | ├── Data Cleaning in SQL | └── Query Optimization | |── Data Visualization & BI Tools | ├── Power BI | │ ├── Data Loading | │ ├── Data Modeling | │ ├── Relationships | │ ├── DAX (Measures, CALCULATE, Time Intelligence) | │ ├── Dashboard Design | │ └── Publishing & Sharing | │ | ├── Tableau (Optional) | │ ├── Worksheets & Dashboards | │ ├── Calculated Fields | │ ├── Filters & Parameters | │ └── Storytelling | │ | └── Dashboard Best Practices | ├── UX/UI Design | ├── KPI Visualization | └── Storytelling with Data | |── Statistics for Data Analysts | ├── Descriptive Statistics (Mean, Median, Mode) | ├── Variance & Standard Deviation | ├── Distribution Basics | ├── Correlation | ├── A/B Testing Basics | ├── Hypothesis Testing | └── Confidence Intervals | |── Data Cleaning & Preparation | ├── Handling Missing Values | ├── Removing Duplicates | ├── Data Type Conversion | ├── Outlier Detection | ├── Data Validation | └── Data Standardization | |── Data Analysis Techniques | ├── Trend Analysis | ├── Cohort Analysis | ├── Funnel Analysis | ├── Retention Analysis | ├── Segmentation (RFM Analysis) | └── Root Cause Analysis | |── Data Engineering Basics (High Demand 🔥) | ├── OLTP vs OLAP | ├── Data Warehousing Concepts | ├── Fact & Dimension Tables | ├── Star Schema | ├── Snowflake Schema | ├── ETL vs ELT | ├── Data Pipelines | ├── dbt (Data Transformation) ⭐️ | └── Apache Airflow (Basics) | |── Cloud & Modern Data Stack (2026 Must 🚀) | ├── Cloud Platforms | │ ├── AWS (S3, Redshift Basics) | │ ├── Google BigQuery ⭐️ | │ └── Azure Synapse | │ | ├── Data Platforms | │ ├── Snowflake ⭐️ | │ ├── BigQuery | │ ├── Amazon Redshift | │ └── Databricks (Basics) | │ | └── Data Storage Concepts | ├── Data Lakes | ├── Data Warehouses | └── Lakehouse Architecture | |── AI & Automation for Analysts (Game Changer 🔥) | ├── ChatGPT for SQL & Python | ├── Copilot for Coding | ├── Prompt Engineering Basics | ├── Automated Reporting | ├── Smart Dashboards | └── AI-assisted Data Analysis | |── Real-World Data Analyst Workflow | ├── Data Collection (SQL, APIs, Files) | ├── Data Cleaning | ├── Data Analysis | ├── Visualization | ├── Insight Generation | └── Stakeholder Communication | |── Projects (MOST IMPORTANT) | ├── Beginner | │ ├── Sales Analysis | │ └── Customer Segmentation | │ | ├── Intermediate | │ ├── E-commerce Dashboard | │ ├── Retention Analysis | │ └── KPI Dashboard | │ | ├── Advanced | │ ├── End-to-End Data Pipeline | │ ├── Real-Time Dashboard | │ └── Business Case Study | 👉 WhatsApp: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉 Telegram: https://t.me/dataanalyticsbuddy Till then keep learning & keep exploring 🙌☺️

Till then keep learning & keep exploring 🙌☺️

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 2026 🔥🔥 |── Foundations (Business + Analytics Thinking) | ├── What is Data Analysis? | ├── Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive) | ├── Business Metrics (Revenue, Profit, Growth, Retention, CAC, LTV) | ├── KPI vs Metrics | ├── Data-driven Decision Making | ├── Problem Solving Framework | └── Asking Business Questions | |── Excel (Core Tool – Still Widely Used) | ├── Basics (Cells, Sheets, Formatting) | ├── Formulas (SUM, IF, COUNT, AVERAGE) | ├── Lookup Functions (VLOOKUP, XLOOKUP, INDEX-MATCH) | ├── Pivot Tables & Pivot Charts | ├── Data Cleaning (Text functions, Remove duplicates) | ├── Conditional Formatting | ├── Basic Dashboards | └── Excel Automation (Basic Macros) | |── Python for Data Analysis | ├── Python Basics (Variables, Data Types) | ├── Control Flow (if, for, while) | ├── Functions | ├── Error Handling (try-except) | ├── Data Structures (List, Tuple, Set, Dictionary) | ├── List & Dict Comprehensions | ├── NumPy (Arrays, Vectorization) | ├── Pandas (DataFrames, Cleaning, Transformation) | ├── GroupBy & Aggregations | ├── Merge, Join, Pivot | ├── Time Series Basics | ├── Data Visualization (Matplotlib, Seaborn) | └── Automation Scripts | |── SQL (Core Skill – Must Have) | ├── SELECT, WHERE, ORDER BY | ├── Joins (INNER, LEFT, RIGHT, FULL) | ├── GROUP BY & Aggregations | ├── CASE WHEN | ├── Subqueries | ├── CTEs | ├── Window Functions | ├── Data Cleaning in SQL | └── Query Optimization | |── Data Visualization & BI Tools | ├── Power BI | │ ├── Data Loading | │ ├── Data Modeling | │ ├── Relationships | │ ├── DAX (Measures, CALCULATE, Time Intelligence) | │ ├── Dashboard Design | │ └── Publishing & Sharing | │ | ├── Tableau (Optional) | │ ├── Worksheets & Dashboards | │ ├── Calculated Fields | │ ├── Filters & Parameters | │ └── Storytelling | │ | └── Dashboard Best Practices | ├── UX/UI Design | ├── KPI Visualization | └── Storytelling with Data | |── Statistics for Data Analysts | ├── Descriptive Statistics (Mean, Median, Mode) | ├── Variance & Standard Deviation | ├── Distribution Basics | ├── Correlation | ├── A/B Testing Basics | ├── Hypothesis Testing | └── Confidence Intervals | |── Data Cleaning & Preparation | ├── Handling Missing Values | ├── Removing Duplicates | ├── Data Type Conversion | ├── Outlier Detection | ├── Data Validation | └── Data Standardization | |── Data Analysis Techniques | ├── Trend Analysis | ├── Cohort Analysis | ├── Funnel Analysis | ├── Retention Analysis | ├── Segmentation (RFM Analysis) | └── Root Cause Analysis | |── Data Engineering Basics (High Demand 🔥) | ├── OLTP vs OLAP | ├── Data Warehousing Concepts | ├── Fact & Dimension Tables | ├── Star Schema | ├── Snowflake Schema | ├── ETL vs ELT | ├── Data Pipelines | ├── dbt (Data Transformation) ⭐️ | └── Apache Airflow (Basics) | |── Cloud & Modern Data Stack (2026 Must 🚀) | ├── Cloud Platforms | │ ├── AWS (S3, Redshift Basics) | │ ├── Google BigQuery ⭐️ | │ └── Azure Synapse | │ | ├── Data Platforms | │ ├── Snowflake ⭐️ | │ ├── BigQuery | │ ├── Amazon Redshift | │ └── Databricks (Basics) | │ | └── Data Storage Concepts | ├── Data Lakes | ├── Data Warehouses | └── Lakehouse Architecture | |── AI & Automation for Analysts (Game Changer 🔥) | ├── ChatGPT for SQL & Python | ├── Copilot for Coding | ├── Prompt Engineering Basics | ├── Automated Reporting | ├── Smart Dashboards | └── AI-assisted Data Analysis | |── Real-World Data Analyst Workflow | ├── Data Collection (SQL, APIs, Files) | ├── Data Cleaning | ├── Data Analysis | ├── Visualization | ├── Insight Generation | └── Stakeholder Communication | |── Projects (MOST IMPORTANT) | ├── Beginner | │ ├── Sales Analysis | │ └── Customer Segmentation | │ | ├── Intermediate | │ ├── E-commerce Dashboard | │ ├── Retention Analysis | │ └── KPI Dashboard | │ | ├── Advanced | │ ├── End-to-End Data Pipeline | │ ├── Real-Time Dashboard | │ └── Business Case Study | 👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉 Telegram Channel: https://t.me/dataanalyticsbuddy

Till then keep learning & keep exploring 🙌☺️

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 2026 🔥🔥🔥 |── Foundations (Business + Analytics Thinking) | ├── What is Data Analysis? | ├── Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive) | ├── Business Metrics (Revenue, Profit, Growth, Retention, CAC, LTV) | ├── KPI vs Metrics | ├── Data-driven Decision Making | ├── Problem Solving Framework | └── Asking Business Questions | |── Excel (Core Tool – Still Widely Used) | ├── Basics (Cells, Sheets, Formatting) | ├── Formulas (SUM, IF, COUNT, AVERAGE) | ├── Lookup Functions (VLOOKUP, XLOOKUP, INDEX-MATCH) | ├── Pivot Tables & Pivot Charts | ├── Data Cleaning (Text functions, Remove duplicates) | ├── Conditional Formatting | ├── Basic Dashboards | └── Excel Automation (Basic Macros) | |── Python for Data Analysis | ├── Python Basics (Variables, Data Types) | ├── Control Flow (if, for, while) | ├── Functions | ├── Error Handling (try-except) | ├── Data Structures (List, Tuple, Set, Dictionary) | ├── List & Dict Comprehensions | ├── NumPy (Arrays, Vectorization) | ├── Pandas (DataFrames, Cleaning, Transformation) | ├── GroupBy & Aggregations | ├── Merge, Join, Pivot | ├── Time Series Basics | ├── Data Visualization (Matplotlib, Seaborn) | └── Automation Scripts | |── SQL (Core Skill – Must Have) | ├── SELECT, WHERE, ORDER BY | ├── Joins (INNER, LEFT, RIGHT, FULL) | ├── GROUP BY & Aggregations | ├── CASE WHEN | ├── Subqueries | ├── CTEs | ├── Window Functions | ├── Data Cleaning in SQL | └── Query Optimization | |── Data Visualization & BI Tools | ├── Power BI | │ ├── Data Loading | │ ├── Data Modeling | │ ├── Relationships | │ ├── DAX (Measures, CALCULATE, Time Intelligence) | │ ├── Dashboard Design | │ └── Publishing & Sharing | │ | ├── Tableau (Optional) | │ ├── Worksheets & Dashboards | │ ├── Calculated Fields | │ ├── Filters & Parameters | │ └── Storytelling | │ | └── Dashboard Best Practices | ├── UX/UI Design | ├── KPI Visualization | └── Storytelling with Data | |── Statistics for Data Analysts | ├── Descriptive Statistics (Mean, Median, Mode) | ├── Variance & Standard Deviation | ├── Distribution Basics | ├── Correlation | ├── A/B Testing Basics | ├── Hypothesis Testing | └── Confidence Intervals | |── Data Cleaning & Preparation | ├── Handling Missing Values | ├── Removing Duplicates | ├── Data Type Conversion | ├── Outlier Detection | ├── Data Validation | └── Data Standardization | |── Data Analysis Techniques | ├── Trend Analysis | ├── Cohort Analysis | ├── Funnel Analysis | ├── Retention Analysis | ├── Segmentation (RFM Analysis) | └── Root Cause Analysis | |── Data Engineering Basics (High Demand 🔥) | ├── OLTP vs OLAP | ├── Data Warehousing Concepts | ├── Fact & Dimension Tables | ├── Star Schema | ├── Snowflake Schema | ├── ETL vs ELT | ├── Data Pipelines | ├── dbt (Data Transformation) ⭐️ | └── Apache Airflow (Basics) | |── Cloud & Modern Data Stack (2026 Must 🚀) | ├── Cloud Platforms | │ ├── AWS (S3, Redshift Basics) | │ ├── Google BigQuery ⭐️ | │ └── Azure Synapse | │ | ├── Data Platforms | │ ├── Snowflake ⭐️ | │ ├── BigQuery | │ ├── Amazon Redshift | │ └── Databricks (Basics) | │ | └── Data Storage Concepts | ├── Data Lakes | ├── Data Warehouses | └── Lakehouse Architecture | |── AI & Automation for Analysts (Game Changer 🔥) | ├── ChatGPT for SQL & Python | ├── Copilot for Coding | ├── Prompt Engineering Basics | ├── Automated Reporting | ├── Smart Dashboards | └── AI-assisted Data Analysis | |── Real-World Data Analyst Workflow | ├── Data Collection (SQL, APIs, Files) | ├── Data Cleaning | ├── Data Analysis | ├── Visualization | ├── Insight Generation | └── Stakeholder Communication | |── Projects (MOST IMPORTANT) | ├── Beginner | │ ├── Sales Analysis | │ └── Customer Segmentation | │ | ├── Intermediate | │ ├── E-commerce Dashboard | │ ├── Retention Analysis | │ └── KPI Dashboard | │ | ├── Advanced | │ ├── End-to-End Data Pipeline | │ ├── Real-Time Dashboard | │ └── Business Case Study | 👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉 Telegram Channel: https://t.me/dataanalyticsbuddy

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 🔥 1️⃣ Core Skills Every Data Analyst Must Learn 📈 Excel/ Spreadsheets Skills: – Formulas (IF, VLOOKUP/XLOOKUP) – Pivot Tables – Charts – Power Query (basic) Excel - https://www.w3schools.com/excel/ 2️⃣ SQL (Most Important Skill) Skills: – SELECT, WHERE, ORDER BY – JOINs – GROUP BY, HAVING – Subqueries & CTEs – Window Functions SQL - https://www.w3schools.com/sql/ 3️⃣ Python for Data Analysis Skills: – pandas – numpy – matplotlib – seaborn – Data cleaning & EDA Python - https://www.w3schools.com/python/ 4️⃣ Data Visualization Tools Power BI & Tableau Skills: – Data modeling – DAX basics – Filters & slicers – Dashboard design Power BI - https://www.datacamp.com/tutorial/tutorial-power-bi-for-beginners Tableau - https://www.datacamp.com/tutorial/tableau-tutorial-for-beginners For Free resources below channels are best & don't forget to join & share invitation with others as well 👉 WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46 👉 Telegram Channel: https://t.me/dataanalyticsbuddy Don't forget to share with others who are looking for learning more about Data Analyst 🙌 ☺️ Till then keep learning & keep exploring 🙌☺️

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