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📊 Data Modelling — we design a full food-delivery schema on screen
📅 30 Aug · 🕛 12 PM IST · 💻 Zoom · 💸 Free
🔑 Keys, ERD, 1NF→3NF, live Q&A
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🏏 FREE LIVE: PySpark on Cricket Data — End-to-End in Databricks

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Saturday, 29 August · 12:00 PM IST · 2 hours · Zoom · Free
Most PySpark practice stops at .show(). This session runs all the way to a Delta table.

What we build in 2 hours:

✅ Load raw ball-by-ball match data into Databricks
✅ groupBy, window functions, joins and UDFs
✅ Charts: top scorers, run rate, win margins
✅ Tuning: cache(), partitions, shuffle settings
✅ Save the processed output to Delta
✅ Live Q&A at the end

Who it’s for: anyone preparing for Data Engineering roles who needs a finished project to point at. Basic Python or SQL is enough, no prior Spark work needed.

You get: live hands-on session, live Q&A, and session notes + resources by email. Recording optional at ₹99.
Host: Durgesh Yadav — Founder @ PrepNPlaced, Sr. Data Engineer @ 7-Eleven, ex-Target

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|── 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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🚀 DE Cohort 4 — Orientation Session

Hey Everyone! 👋

Please join us tomorrow at 7:00 AM IST for the Data Engineering Cohort 4 Orientation Session.

🔗 Session Link:
https://us06web.zoom.us/j/82084947108?pwd=ctzqc2t9EzNXfyVvlR7JzIwv7LM8zc.1

✨ What We’ll Cover:
• Understand the complete DE Cohort 4 journey
• Explore the curriculum, learning roadmap & projects
• Understand how the cohort is structured
• Know what you can expect throughout the program
• Learn how we prepare you for Data Engineering roles in product-based companies
• Get clarity on the learning process, mentorship and career support

🌱 This orientation is a great opportunity to understand how the cohort can help you build the skills, projects and interview readiness required to move toward product-based companies.

📚 Explore the Cohort:
https://www.prepnplaced.com/courses

🔥 DE Cohort 4 starts with the right foundation. See you all at 7 AM!

Let's build, learn and grow together. 🚀

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Hi All, I have Created a Free Platform where you People can get best Job as a Fresher Click on the Link - https://www.prepnplaced.com/apprenticeships

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Durgesh Yadav

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A 244-page Data Engineering book. 1 month of live Data Engineering webinars. A full Data Analytics course. ₹0. No card. No catch. Links below.

Why I built this:

I'm a Data Engineer at 7-Eleven, ex-Target. For the last 4 years, outside work, I've mentored people 1:1 into data roles and taught at GeeksforGeeks, Scaler, Bosscoder and Analytics Vidhya.

The same story kept repeating. Someone in ops, support, sales or a mechanical job, with a career gap and zero tech experience, who could clearly do this work. What stopped them wasn't ability. It was courses they couldn't afford, and a job hunt scattered across a dozen tools: one site to score a resume, another to find jobs, another to hunt referrals, a spreadsheet to track it all, and nobody telling them what to do next.

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Launch week goodies, all free:

📘 Data Engineering: From Zero to Production & Interview Ready (244 pages)
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🧰 Field Kit: cheat sheets + interview questions, open source
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🎯 Live Data Engineering webinars, 1 month access
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This Saturday, 3 Oct, 12:30 PM IST: Tesla Telemetry Pipeline. S3 → Airflow → Snowflake, running on your own laptop by the end.
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📊 Data Analytics course: SQL, Python, Power BI. Free forever.
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The platform: https://www.prepnplaced.com

One ask. Comment "DE" or "DA" and I'll reply with the exact page to start on for your goal. Repost so the person who needs this actually sees it.

I still write pipelines at 7-Eleven by day. This is what I do with everything else. Start today.

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