Data Analyst Telegram Channel Link

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 🔥SQL - https://lnkd.in/dZjUh9cqPython - https://lnkd.in/dYnG5jN3Excel - https://lnkd.in/d79Fks_yPower BI - https://lnkd.in/dVzsZqQ9 Tableau - https://lnkd.in/dXdC7mcXProjects - https://lnkd.in/dyviaAbTExcel Portfolio Projects: https://lnkd.in/dJPciPvUSQL Portfolio Projects: https://lnkd.in/dzk4eQKkPower BI Portfolio Project: https://lnkd.in/dC_S7P2rData Analyst Roadmap - https://lnkd.in/e7_b8NBuFor Free resources below channels are best and don't forget to join & share invitation with others as well!!👉 WhatsApp Channel: https://whatsapp.com/channel/0029Vb4zI2II7BeC1CLrSG46👉 Telegram Channel: https://t.me/dataaanalystDon't forget to share with others who are looking for learning more about Data Analyst 🙌 ☺️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/0029Vb4zI2II7BeC1CLrSG46👉Telegram: https://t.me/dataaanalystTill then keep learning & keep exploring 🙌☺️

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/0029Vb4zI2II7BeC1CLrSG46👉Telegram Channel: https://t.me/dataaanalystTill 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/0029Vb4zI2II7BeC1CLrSG46👉Telegram: https://t.me/dataaanalystTill then keep learning & keep exploring 🙌☺️

𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 🔥1️⃣ Core Skills Every Data Analyst Must Learn📈 Excel/ SpreadsheetsSkills:– 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 FunctionsSQL - https://www.w3schools.com/sql/3️⃣ Python for Data AnalysisSkills:– pandas– numpy– matplotlib– seaborn– Data cleaning & EDAPython - https://www.w3schools.com/python/4️⃣ Data Visualization ToolsPower BI & TableauSkills:– Data modeling– DAX basics– Filters & slicers– Dashboard designPower BI - https://www.datacamp.com/tutorial/tutorial-power-bi-for-beginnersTableau - https://www.datacamp.com/tutorial/tableau-tutorial-for-beginnersFor Free resources below channels are best & don't forget to join & share invitation with others as well👉WhatsApp: https://whatsapp.com/channel/0029Vb4zI2II7BeC1CLrSG46👉Telegram: https://t.me/dataaanalystDon't forget to share with others who are looking for learning more about Data Analyst 🙌 ☺️Till then keep learning & keep exploring 🙌☺️

SQL Zero to Advanced Roadmap with Practice Questions 🔥Share with others to help ✨✅ Join our Communities:Telegram Channel: https://t.me/dataaanalystWhatsApp Channel: https://whatsapp.com/channel/0029Vb4zI2II7BeC1CLrSG46Do react ❤️ if you want more resources like this

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/0029Vb4zI2II7BeC1CLrSG46👉Telegram Channel: https://t.me/dataaanalystTill then keep learning and keep exploring 🙌 😊

SQL Notes by APNA College 🔥Share with others to help ✨✅ Join our Communities:Telegram Channel: https://t.me/dataaanalystWhatsApp Channel: https://whatsapp.com/channel/0029Vb4zI2II7BeC1CLrSG46Do react ❤️ if you want more resources like this

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