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🚀 Welcome to the Elite Data Engineering & Agentic AI Hub! 🚀 👑 Community Creator: Mandar Patil 👨💻 Admin & Mentor: Durgesh Yadav The era of basic data tasks is over. With Agentic AI evolving the industry, up to 60% of traditional Data Analyst roles
Показати більше📈 Аналітичний огляд Telegram-каналу Prepnplaced.com
Канал Prepnplaced.com (@dataanalyticsbuddy) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 29 309 підписників, посідаючи 6 656 місце в категорії Освіта та 14 750 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 29 309 підписників.
За останніми даними від 12 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -944, а за останні 24 години на -23, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.93%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.55% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 445 переглядів. Протягом першої доби публікація в середньому набирає 454 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 1.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як analyst, sql, analytic, dashboard, roadmap.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“🚀 Welcome to the Elite Data Engineering & Agentic AI Hub! 🚀
👑 Community Creator: Mandar Patil
👨💻 Admin & Mentor: Durgesh Yadav
The era of basic data tasks is over. With Agentic AI evolving the industry, up to 60% of traditional Data Analyst ...”
Завдяки високій частоті оновлень (останні дані отримано 13 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
29 309
Підписники
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-1547 днів
-94430 день
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| 16 | 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
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Till then keep learning and keep exploring 🙌 😊 | 2 214 |
| 17 | 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
|
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Till then keep learning & keep exploring 🙌☺️ | 2 049 |
| 18 | Till then keep learning & keep exploring 🙌☺️ | 0 |
| 19 | 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
|
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| 20 | Till then keep learning & keep exploring 🙌☺️ | 0 |
