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π Welcome to the Elite Data Engineering & Agentic AI Hub! π π Community Creator: Mandar Patil π¨βπ» 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
Ko'proq ko'rsatishπ Telegram kanali Prepnplaced.com analitikasi
Prepnplaced.com (@dataanalyticsbuddy) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 29 309 obunachidan iborat bo'lib, TaΚΌlim toifasida 6 656-o'rinni va Hindiston mintaqasida 14 750-o'rinni egallagan.
π Auditoriya koβrsatkichlari va dinamika
Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ sanasidan buyon loyiha tez oβsib, 29 309 obunachiga ega boβldi.
12 Iyun, 2026 dagi oxirgi maβlumotlarga koβra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -944 ga, soβnggi 24 soatda esa -23 ga oβzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
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- Reaksiyalar va oβzaro taβsir: Auditoriya faol: har bir postga oβrtacha 1 ta reaksiya keladi.
- Tematik yoβnalishlar: Kontent analyst, sql, analytic, dashboard, roadmap kabi asosiy mavzularga jamlangan.
π Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taβriflaydi:
βπ Welcome to the Elite Data Engineering & Agentic AI Hub! π
π Community Creator: Mandar Patil
π¨βπ» 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 ...β
Yuqori yangilanish chastotasi (oxirgi maβlumot 13 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boβlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni TaΚΌlim toifasidagi muhim taβsir nuqtasiga aylantirishini koβrsatadi.
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| 16 | πππ ππππ«π§π’π§π ππ¨πππ¦ππ©π₯π₯π₯
|ββ Basics
| βββ What is SQL?
| βββ Database vs DBMS vs RDBMS
| βββ Databases & Tables
| βββ Rows vs Columns
| βββ Data Types (INT, VARCHAR, DATE, FLOAT, BOOLEAN)
| βββ Constraints (NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY, CHECK, DEFAULT)
| βββ Keys (Primary, Foreign, Candidate, Composite, Super Key)
| βββ CRUD Operations (Create, Read, Update, Delete)
|
|ββ DDL (Data Definition Language)
| βββ CREATE DATABASE
| βββ CREATE TABLE
| βββ ALTER TABLE
| βββ DROP TABLE
| βββ TRUNCATE TABLE
| βββ RENAME TABLE
|
|ββ DML (Data Manipulation Language)
| βββ INSERT INTO
| βββ UPDATE
| βββ DELETE
| βββ Bulk Inserts
|
|ββ DQL (Data Query Language)
| βββ SELECT
| βββ Column Selection
| βββ Aliases (AS)
| βββ Expressions & Calculations
|
|ββ Data Retrieval
| βββ SELECT, FROM, WHERE
| βββ DISTINCT
| βββ ORDER BY (ASC, DESC)
| βββ LIMIT / TOP / OFFSET-FETCH
| βββ BETWEEN
| βββ IN / NOT IN
| βββ LIKE (%, _)
| βββ IS NULL / IS NOT NULL
|
|ββ Filtering & Conditions
| βββ AND, OR, NOT
| βββ Operator Precedence
| βββ Nested Conditions
| βββ Short-circuit Evaluation
|
|ββ Joins
| βββ INNER JOIN
| βββ LEFT JOIN
| βββ RIGHT JOIN
| βββ FULL OUTER JOIN
| βββ CROSS JOIN
| βββ SELF JOIN
| βββ Join Conditions (ON vs WHERE)
| βββ Handling NULLs in Joins
|
|ββ Grouping & Aggregation
| βββ GROUP BY
| βββ Aggregate Functions: COUNT(), SUM(), AVG(), MIN(), MAX()
| βββ HAVING
| βββ Conditional Aggregation (CASE WHEN)
| βββ Grouping Rules & Errors
|
|ββ CASE Statements & Conditional Logic
| βββ CASE WHEN
| βββ Nested CASE
| βββ Conditional Columns
| βββ Conditional Aggregations
|
|ββ NULL Handling
| βββ NULL Behavior in SQL
| βββ IS NULL, IS NOT NULL
| βββ COALESCE()
| βββ NULLIF()
| βββ NULL in Aggregations
|
|ββ Subqueries & Nested Queries
| βββ Subquery in SELECT
| βββ Subquery in WHERE
| βββ Subquery in FROM
| βββ Correlated Subqueries
| βββ Scalar vs Multi-row Subqueries
| βββ Performance Considerations
|
|ββ Set Operations
| βββ UNION
| βββ UNION ALL
| βββ INTERSECT
| βββ EXCEPT / MINUS
|
|ββ Advanced SQL
| βββ EXISTS / NOT EXISTS
| βββ Derived Tables
| βββ Inline Views
| βββ Pivoting & Unpivoting
| βββ Dynamic SQL (Basics)
|
|ββ Window Functions (Analytical SQL)
| βββ OVER() Clause
| βββ PARTITION BY
| βββ ORDER BY in Window
| βββ Ranking: ROW_NUMBER(), RANK(), DENSE_RANK()
| βββ Value Functions: LEAD(), LAG()
| βββ Aggregates as Window Functions
| βββ Running Totals & Moving Averages
|
|ββ Common Table Expressions (CTEs)
| βββ WITH Clause
| βββ Multiple CTEs
| βββ Recursive CTEs
| βββ CTE vs Subquery
|
|ββ Views
| βββ Creating Views
| βββ Updating Views
| βββ Materialized Views
| βββ Use Cases
|
|ββ Indexes & Performance
| βββ What is Index
| βββ Clustered vs Non-Clustered Index
| βββ Composite Index
| βββ Indexing Strategies
| βββ Query Optimization
| βββ Execution Plan
| βββ EXPLAIN / ANALYZE
|
|ββ Transactions & ACID
| βββ Transaction Basics
| βββ COMMIT, ROLLBACK, SAVEPOINT
| βββ ACID Properties
| βββ Concurrency Issues
|
|ββ Locks & Isolation Levels
| βββ Lock Types
| βββ Isolation Levels
| βββ Dirty Read, Non-repeatable Read, Phantom Read
| βββ Deadlocks
|
|ββ Database Design Concepts
| βββ ER Diagrams
| βββ Normalization (1NF, 2NF, 3NF, BCNF)
| βββ Denormalization
| βββ Relationships (1-1, 1-M, M-M)
| βββ Schema Design Best Practices
|
|ββ Data Warehousing Concepts
| βββ OLTP vs OLAP
| βββ Fact & Dimension Tables
| βββ Star Schema
| βββ Snowflake Schema
| βββ ETL Basics
|
|ββ SQL for Data Analysis
| βββ Business Metrics (Revenue, Retention, AOV)
| βββ Cohort Analysis
| βββ Funnel Analysis
| βββ Time Series Analysis
| βββ Data Cleaning in SQL
|
|ββ SQL in Real Projects
| βββ E-commerce Analysis
| βββ Customer Behavior Analysis
| βββ Sales Dashboard Queries
| βββ KPI Reporting
|
|ββ Tools & Platforms
| βββ MySQL
| βββ PostgreSQL
| βββ SQL Server
| βββ Oracle
| βββ SQLite
| βββ BigQuery
| βββ Snowflake
| βββ Amazon Redshift
|
|ββ END
πWhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
πTelegram Channel: https://t.me/dataanalyticsbuddy
Till then keep learning and keep exploring π π | 2 214 |
| 17 | ππππ ππ§ππ₯π²π¬π ππππ«π§π’π§π ππ¨πππ¦ππ© 2026 π₯
|ββ Foundations (Business + Analytics Thinking)
| βββ What is Data Analysis?
| βββ Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
| βββ Business Metrics (Revenue, Profit, Growth, Retention, CAC, LTV)
| βββ KPI vs Metrics
| βββ Data-driven Decision Making
| βββ Problem Solving Framework
| βββ Asking Business Questions
|
|ββ Excel (Core Tool β Still Widely Used)
| βββ Basics (Cells, Sheets, Formatting)
| βββ Formulas (SUM, IF, COUNT, AVERAGE)
| βββ Lookup Functions (VLOOKUP, XLOOKUP, INDEX-MATCH)
| βββ Pivot Tables & Pivot Charts
| βββ Data Cleaning (Text functions, Remove duplicates)
| βββ Conditional Formatting
| βββ Basic Dashboards
| βββ Excel Automation (Basic Macros)
|
|ββ Python for Data Analysis
| βββ Python Basics (Variables, Data Types)
| βββ Control Flow (if, for, while)
| βββ Functions
| βββ Error Handling (try-except)
| βββ Data Structures (List, Tuple, Set, Dictionary)
| βββ List & Dict Comprehensions
| βββ NumPy (Arrays, Vectorization)
| βββ Pandas (DataFrames, Cleaning, Transformation)
| βββ GroupBy & Aggregations
| βββ Merge, Join, Pivot
| βββ Time Series Basics
| βββ Data Visualization (Matplotlib, Seaborn)
| βββ Automation Scripts
|
|ββ SQL (Core Skill β Must Have)
| βββ SELECT, WHERE, ORDER BY
| βββ Joins (INNER, LEFT, RIGHT, FULL)
| βββ GROUP BY & Aggregations
| βββ CASE WHEN
| βββ Subqueries
| βββ CTEs
| βββ Window Functions
| βββ Data Cleaning in SQL
| βββ Query Optimization
|
|ββ Data Visualization & BI Tools
| βββ Power BI
| β βββ Data Loading
| β βββ Data Modeling
| β βββ Relationships
| β βββ DAX (Measures, CALCULATE, Time Intelligence)
| β βββ Dashboard Design
| β βββ Publishing & Sharing
| β
| βββ Tableau (Optional)
| β βββ Worksheets & Dashboards
| β βββ Calculated Fields
| β βββ Filters & Parameters
| β βββ Storytelling
| β
| βββ Dashboard Best Practices
| βββ UX/UI Design
| βββ KPI Visualization
| βββ Storytelling with Data
|
|ββ Statistics for Data Analysts
| βββ Descriptive Statistics (Mean, Median, Mode)
| βββ Variance & Standard Deviation
| βββ Distribution Basics
| βββ Correlation
| βββ A/B Testing Basics
| βββ Hypothesis Testing
| βββ Confidence Intervals
|
|ββ Data Cleaning & Preparation
| βββ Handling Missing Values
| βββ Removing Duplicates
| βββ Data Type Conversion
| βββ Outlier Detection
| βββ Data Validation
| βββ Data Standardization
|
|ββ Data Analysis Techniques
| βββ Trend Analysis
| βββ Cohort Analysis
| βββ Funnel Analysis
| βββ Retention Analysis
| βββ Segmentation (RFM Analysis)
| βββ Root Cause Analysis
|
|ββ Data Engineering Basics (High Demand π₯)
| βββ OLTP vs OLAP
| βββ Data Warehousing Concepts
| βββ Fact & Dimension Tables
| βββ Star Schema
| βββ Snowflake Schema
| βββ ETL vs ELT
| βββ Data Pipelines
| βββ dbt (Data Transformation) βοΈ
| βββ Apache Airflow (Basics)
|
|ββ Cloud & Modern Data Stack (2026 Must π)
| βββ Cloud Platforms
| β βββ AWS (S3, Redshift Basics)
| β βββ Google BigQuery βοΈ
| β βββ Azure Synapse
| β
| βββ Data Platforms
| β βββ Snowflake βοΈ
| β βββ BigQuery
| β βββ Amazon Redshift
| β βββ Databricks (Basics)
| β
| βββ Data Storage Concepts
| βββ Data Lakes
| βββ Data Warehouses
| βββ Lakehouse Architecture
|
|ββ AI & Automation for Analysts (Game Changer π₯)
| βββ ChatGPT for SQL & Python
| βββ Copilot for Coding
| βββ Prompt Engineering Basics
| βββ Automated Reporting
| βββ Smart Dashboards
| βββ AI-assisted Data Analysis
|
|ββ Real-World Data Analyst Workflow
| βββ Data Collection (SQL, APIs, Files)
| βββ Data Cleaning
| βββ Data Analysis
| βββ Visualization
| βββ Insight Generation
| βββ Stakeholder Communication
|
|ββ Projects (MOST IMPORTANT)
| βββ Beginner
| β βββ Sales Analysis
| β βββ Customer Segmentation
| β
| βββ Intermediate
| β βββ E-commerce Dashboard
| β βββ Retention Analysis
| β βββ KPI Dashboard
| β
| βββ Advanced
| β βββ End-to-End Data Pipeline
| β βββ Real-Time Dashboard
| β βββ Business Case Study
|
π WhatsApp: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
π Telegram: https://t.me/dataanalyticsbuddy
Till then keep learning & keep exploring πβΊοΈ | 2 049 |
| 18 | Till then keep learning & keep exploring πβΊοΈ | 0 |
| 19 | ππππ ππ§ππ₯π²π¬π ππππ«π§π’π§π ππ¨πππ¦ππ© 2026 π₯π₯
|ββ Foundations (Business + Analytics Thinking)
| βββ What is Data Analysis?
| βββ Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
| βββ Business Metrics (Revenue, Profit, Growth, Retention, CAC, LTV)
| βββ KPI vs Metrics
| βββ Data-driven Decision Making
| βββ Problem Solving Framework
| βββ Asking Business Questions
|
|ββ Excel (Core Tool β Still Widely Used)
| βββ Basics (Cells, Sheets, Formatting)
| βββ Formulas (SUM, IF, COUNT, AVERAGE)
| βββ Lookup Functions (VLOOKUP, XLOOKUP, INDEX-MATCH)
| βββ Pivot Tables & Pivot Charts
| βββ Data Cleaning (Text functions, Remove duplicates)
| βββ Conditional Formatting
| βββ Basic Dashboards
| βββ Excel Automation (Basic Macros)
|
|ββ Python for Data Analysis
| βββ Python Basics (Variables, Data Types)
| βββ Control Flow (if, for, while)
| βββ Functions
| βββ Error Handling (try-except)
| βββ Data Structures (List, Tuple, Set, Dictionary)
| βββ List & Dict Comprehensions
| βββ NumPy (Arrays, Vectorization)
| βββ Pandas (DataFrames, Cleaning, Transformation)
| βββ GroupBy & Aggregations
| βββ Merge, Join, Pivot
| βββ Time Series Basics
| βββ Data Visualization (Matplotlib, Seaborn)
| βββ Automation Scripts
|
|ββ SQL (Core Skill β Must Have)
| βββ SELECT, WHERE, ORDER BY
| βββ Joins (INNER, LEFT, RIGHT, FULL)
| βββ GROUP BY & Aggregations
| βββ CASE WHEN
| βββ Subqueries
| βββ CTEs
| βββ Window Functions
| βββ Data Cleaning in SQL
| βββ Query Optimization
|
|ββ Data Visualization & BI Tools
| βββ Power BI
| β βββ Data Loading
| β βββ Data Modeling
| β βββ Relationships
| β βββ DAX (Measures, CALCULATE, Time Intelligence)
| β βββ Dashboard Design
| β βββ Publishing & Sharing
| β
| βββ Tableau (Optional)
| β βββ Worksheets & Dashboards
| β βββ Calculated Fields
| β βββ Filters & Parameters
| β βββ Storytelling
| β
| βββ Dashboard Best Practices
| βββ UX/UI Design
| βββ KPI Visualization
| βββ Storytelling with Data
|
|ββ Statistics for Data Analysts
| βββ Descriptive Statistics (Mean, Median, Mode)
| βββ Variance & Standard Deviation
| βββ Distribution Basics
| βββ Correlation
| βββ A/B Testing Basics
| βββ Hypothesis Testing
| βββ Confidence Intervals
|
|ββ Data Cleaning & Preparation
| βββ Handling Missing Values
| βββ Removing Duplicates
| βββ Data Type Conversion
| βββ Outlier Detection
| βββ Data Validation
| βββ Data Standardization
|
|ββ Data Analysis Techniques
| βββ Trend Analysis
| βββ Cohort Analysis
| βββ Funnel Analysis
| βββ Retention Analysis
| βββ Segmentation (RFM Analysis)
| βββ Root Cause Analysis
|
|ββ Data Engineering Basics (High Demand π₯)
| βββ OLTP vs OLAP
| βββ Data Warehousing Concepts
| βββ Fact & Dimension Tables
| βββ Star Schema
| βββ Snowflake Schema
| βββ ETL vs ELT
| βββ Data Pipelines
| βββ dbt (Data Transformation) βοΈ
| βββ Apache Airflow (Basics)
|
|ββ Cloud & Modern Data Stack (2026 Must π)
| βββ Cloud Platforms
| β βββ AWS (S3, Redshift Basics)
| β βββ Google BigQuery βοΈ
| β βββ Azure Synapse
| β
| βββ Data Platforms
| β βββ Snowflake βοΈ
| β βββ BigQuery
| β βββ Amazon Redshift
| β βββ Databricks (Basics)
| β
| βββ Data Storage Concepts
| βββ Data Lakes
| βββ Data Warehouses
| βββ Lakehouse Architecture
|
|ββ AI & Automation for Analysts (Game Changer π₯)
| βββ ChatGPT for SQL & Python
| βββ Copilot for Coding
| βββ Prompt Engineering Basics
| βββ Automated Reporting
| βββ Smart Dashboards
| βββ AI-assisted Data Analysis
|
|ββ Real-World Data Analyst Workflow
| βββ Data Collection (SQL, APIs, Files)
| βββ Data Cleaning
| βββ Data Analysis
| βββ Visualization
| βββ Insight Generation
| βββ Stakeholder Communication
|
|ββ Projects (MOST IMPORTANT)
| βββ Beginner
| β βββ Sales Analysis
| β βββ Customer Segmentation
| β
| βββ Intermediate
| β βββ E-commerce Dashboard
| β βββ Retention Analysis
| β βββ KPI Dashboard
| β
| βββ Advanced
| β βββ End-to-End Data Pipeline
| β βββ Real-Time Dashboard
| β βββ Business Case Study
|
π WhatsApp Channel: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46
π Telegram Channel: https://t.me/dataanalyticsbuddy | 0 |
| 20 | Till then keep learning & keep exploring πβΊοΈ | 0 |
