Data Analytics Bootcamp 5.0: Job Placement Support + AI Automation & Data Engineering Basics

2.1: Peter Pandey’s Journey and Need to Learn Excel

2.2: Installing Excel: Windows / Mac

2.3: Getting Familiar With Excel

2.4: Getting More Familiar With Excel

2.5: Introduction to Formulas

2.6: Formula Behavior

2.7: Introduction to Tables

2.8: Introduction to Charts

2.9: Other Essential Features

2.10: Peter Gets His First Assignment From His Senior

2.11: Practice Exercise: Personal Expense Tracker

2.12: Quiz

2.13: Chapter Summary

3.1: Intro to Movies Dataset

3.2: Cleaning Bad Data

3.3: Combining Data From Two Tables (VLOOKUP)

3.4: Combining Data From Two Tables (INDEXMATCH)

3.5: Combining Data From Two Tables (XLOOKUP)

3.6: Peter’s Request To Tony

3.7: Practice Exercise

3.8: Quiz

3.9: Chapter Summary

4.1: Basic Maths to Business Metrics (Bollywood movies revenue avg)

4.2: Commonly Used Business Metrics (P&L)

4.3: Commonly Used Statistic Concepts (Mean, Median, Mode)

4.4: Commonly Used Statistic Concepts (Variance, Standard Deviation)

4.5: Commonly Used Statistic Concepts (Correlation)

4.6: Peter Gets to Solve a Real Time Problem

4.7: Practice Exercise

4.8: Quiz

4.9: Chapter Summary

5.1: What is Power Query and Why it is the game changer?

5.2: Cleaning Data In Power Query

5.3: Merging Data in Power Query

5.4: Adding New Columns in Power Query

5.5: Loading Power Query Data to Excel

5.6: Peter Get’s His Hand Dirty

5.7: Practice Exercise

5.8: Quiz

5.9: Chapter Summary

6.1: Pivot Table Introduction: Apple Product Sales

6.2: Pivot Table For Movie Analytics

6.3: Creating a Report Using Pivot Table

6.4: Pivot Table Options & Formatting

6.5: Using Power Pivot & DAX For Powerful Business Reports

6.6: Adding Targets Using Data Modelling in Power Pivot

6.7: Breaking Down Complex Problems: Thinking Process of a Highly Paid Data Analyst

6.8: More Business Metrics and Conditional Formatting

6.9: VBA Basics & How Much VBA You Should Learn

6.10: Peter Practices On His Own

6.11: Practice Exercise

6.12: Quiz

6.13: I Need a Favour

6.14: Chapter Summary

7.1: Atliq’s Business Model

7.2: Usage of Excel & Business Requirement in Atliq

8.1: ETL (Extract, Transform and Load Data) in Excel I

8.2: ETL (Extract, Transform and Load Data) in Excel II

8.3: Business Report: Solution Design Thought Process

8.4: Creating Connections Among Tables Using Data Modelling

8.5: Adding a Date Table Using Power Query

8.6: Functional Knowledge: Sales

8.7: Sales Analytics: Creating Customer Performance Report

8.8: User Empathetic Report Design

8.9: Sales Analytics: Creating Market Performance vs Targets Reported

8.10: Practice Exercise

8.11: Quiz

8.12: Chapter Summary

9.1: Understanding P & L

9.2: Functional Knowledge: Finance

9.3: Adding the Finance Data to Data Model

9.4: Finance Analytics: P & L by Year Report

9.5: Fine Tuning P & L By Year Report

9.6: Adding Months & Quarters in Data Model

9.7: Finance Analytics: P & L by Months Report

9.8: Fine Tuning P & L By Month Report

9.9: Practice Exercise

9.10: Quiz

9.11: Chapter Summary

10.1: Wanda’s Challenge With Prioritizing Projects

10.2: Peter & Tony Creates a Project Priority Matrix

10.3: Mentor Talk: Being A Problem Solver

10.4: Practice Exercise

10.5: Bruce Haryali Needs Help With Excel

10.6: Peter & Tony Creates a Scenario Planning Tool

10.7: Mentor Talk: Growth Zone

10.8: Practice Exercise

10.9: Quiz

11.1: Final Quiz

12.1: Transition to Online Credibility & Power BI

2.1: Introduction to Online Credibility

3.1: Get Your LinkedIn Profile Right

3.2: LinkedIn Banners

4.1: Why Domain Knowledge?

4.2: Understanding KPIs

5.1: Add Your Excel Project to GitHub

5.2: Share Your Excel Project with Potential Recruiters

5.3: One Step Closer to Your Dream Portfolio

6.1: Knowledge Check

6.2: Checkpoint

1.1: Welcome to Power BI Experience

1.2: How Learning Power BI Can Help You in Your Career?

1.3: Read Before You Begin

2.1: Install Power BI

2.2: Power BI: Tool Introduction

2.3: Power Query: Intro & Column Transformations

2.4: Power Query: Merging and Appending

2.5: Power Query: Best Practices

2.6: Introduction to DAX

2.7: Introduction to DAX - II

2.8: Introduction to Data Modeling

2.9: Introduction to Creating Visuals

2.10: Tony asks Peter to Fix Data Holes

2.11: Practice Exercise

2.12: Chapter Summary

2.13: Quiz

3.1: Problem Statement

3.2: The Email that Started this Project

3.3: Project Kick-off Meeting

3.4: Learn how a ‘Project Charter’ is Used in Companies

3.5: Project Kick-off: Session Debrief

3.6: Senior Data Analyst Sets Up the Next Steps with Junior Data Analyst NEW

3.7: Simplified: Profit and Loss Statement

3.8: Chapter Summary

3.9: Quiz

4.1: Simplified: Data Warehouse, OLTP vs OLAP, Data Catalog

4.2: Install MySQL and Import Data

4.3: Simplified: Data Exploration Using SQL, Star Schema, Fact vs Dimension Tables

4.4: Load and connect data with MySQL

4.5: Create a Date Dimension Table

4.6: Validate Data against Benchmark Numbers

4.7: Tony’s Valuable Advise to Peter NEW

4.8: Practice Exercise

4.9: Chapter Summary

4.10: Quiz

5.1: Review Finance View Mockups

5.2: How to Enhance your Mockups

5.3: Transform Data in Power Query Editor

5.4: Create Calculated Columns in Power Query Editor

5.5: Power Query Best Practices

5.6: Peter Gets His Hands Dirty NEW

5.7: Practice Exercise NEW

5.8: Chapter Summary

5.9: Quiz

6.1: Peter's Fear for DAX

6.2: DAX Behaviour: Filter Context

6.3: DAX: Using Calculate to Change Filter Context

6.4: All and ALLEXCEPT

6.5: Direct Filters and FILTER Function

6.6: DAX Calculated Column

6.7: Practice Exercise

6.8: Chapter Summary

6.9: Quiz

7.1: Power Query or DAX for Generating Calculated Columns?

7.2: Data Modelling: Star and Snowflake Schema

7.3: Data Modeling: Connect Dimensions with Fact Tables

7.4: Simplified: Why Do We Need Dimension Tables?

7.5: Mentor Talk: Congratulations! You are Levelling Up!

7.6: Create Calculated Columns Using DAX

7.7: Easy Way to Verify Your Numbers in Power BI

7.8: Assignment: Optimize Report and Reduce File Size by 25 %

7.9: I Need a Favour

7.10: Chapter Summary

7.11: Quiz

8.1: Finance View: Prepare a List of Metrics

8.2: Simplified: Calculate Function & Filter Context

8.3: Finance View: Creating Metrics

8.4: Finance View: Create P&L Table Structure

8.5: Finance View: Create Last Year (LY) Column

8.6: Finance View: Build an Ultimate DAX Measure for P & L Table Structure - I

8.7: Finance View: Build an Ultimate DAX Measure for P & L Table Structure - II

8.8: Finance View: Create ‘Quarters’ & ‘YTD/YTG’ Slicers

8.9: Mentor Talk: Figuring Out Solutions

8.10: Finance View: Create a Line chart to Show Performance Over Time

8.11: Finance View: Build Top Product, Market & Region Visuals

8.12: Intermediate Review: I Met Product Owner Nick and He Gave this Feedback!

8.13: Finance View: Add Net Profit

8.14: Chapter Summary

8.15: Quiz

9.1: Review Sales View Mockup

9.2: Sales View: Build Top Customers & Performance Matrix Visuals

9.3: Sales View: Build Product Performance & Unit Economics Visuals

9.4: Build Marketing View

9.5: Simplified: Supply Chain Basics

9.6: Supply Chain View: Review Mock Up

9.7: Supply Chain View: Build Key Measures

9.8: Supply Chain View: Build Visuals

9.9: Chapter Summary

9.10: Quiz

10.1: Simplified: Dashboard vs Report

10.2: 15 Design Rules for an Effective Dashboard

10.3: Finalize Page Layout Design

10.4: Create Home Page

10.5: Design Finance Dashboard

10.6: Add Key Elements to Finance Dashboard

10.7: Copy the Design to Sales, Marketing & Supply Chain Dashboard

10.8: Chapter Summary

10.9: Quiz

11.1: Create a User Acceptance Test report

11.2: Create a Power BI account

11.3: Create a Workspace in Power BI

11.4: Create a Live Excel Report from Power BI

11.5: Chapter Summary

11.6: Quiz

12.1: Stakeholder Analysis and its Significance

12.2: Peter Recalls the Stakeholder Mapping Meeting

12.3: Stakeholder Review Meeting: How did it Go?

12.4: This is the secret to be ‘Job Ready’!

12.5: Practice Exercise: Quick Fixes

12.6: Quick Fix: Chg% formula

12.7: Practice Exercise: Implementing Dynamic Benchmark

12.8: Practice Exercise: Adding Dynamic Slicer to Filter Visual

12.9: Practice Exercise: Create a Toggle Button to Switch between Two Visuals

12.10: Practice Exercise: Create a Tool Tip to Show Trend

12.11: Learn: Adding Market Share Data

12.12: Practice Exercise: Create an Executive Dashboard

12.13: Learn: Performance Optimization

12.14: Learn: Fix Data Quality Issues

12.15: Chapter Summary

12.16: Quiz

13.1: Power BI Service Overview, Report Sharing, and Apps

13.2: How to Set Up Automatic Data Refresh: My SQL

13.3: How to Set Up Automatic Data Refresh: Excel

13.4: Simplified: Collaboration, Bookmarks, and Insights in Power BI Service

13.5: Driving the Extra Mile: Documentation and Maintenance

13.6: Chapter Summary

13.7: Quiz

14.1: Solution: Quick Fixes

14.2: Solution: Implementing Dynamic Targets (Add Targets)

14.3: Solution: Implementing Dynamic Targets (Create a dynamic switch between Targets and LY)

14.4: Solution: Implementing Dynamic Targets (P & L visuals to compare Target or LY based on selection)

14.5: Solution: Adding Dynamic Slicer to Filter Visual

14.6: Solution: Create a Toggle Button to Switch between Two Visuals

14.7: Solution: Create a Tool Tip to Show Trend

14.8: Solution: Create an Executive Dashboard (KPI Visuals)

14.9: Solution: Create an Executive Dashboard (Key Insights by Subzone )

14.10: Solution: Create an Executive Dashboard (Market Share Visual & Conditional Formatting)

14.11: Solution: Create an Executive Dashboard (Final Enhancements)

15.1: Feature Request from Mr. Haryali

15.2: Your Turn: Mr Haryali's Challenge + Extended Dataset

15.3: Interview Scenarios

15.4: Interview Scenario 1

15.5: Interview Scenario 2

15.6: Interview Scenario 3

16.1: Intro

16.2: Feature Updates Task -1

16.3: Solution: Task - 1

16.4: Feature Updates Task- 2

16.5: Solution: Task - 2

16.6: Feature Updates Task - 3

16.7: Solution: Task - 3

16.8: Feature Updates Task - 4

16.9: Solution: Task - 4

16.10: Feature Updates Task - 5

16.11: Solution: Task - 5

16.12: Feature Updates Task - 6

16.13: Solution: Task - 6

16.14: Feature Updates Task - 7 NEW

16.15: Solution: Task - 7 NEW

16.16: Feature Updates Task - 8 NEW

17.1: Exam Intro

17.2: Question Bank - 1

18.1: Final Quiz

19.1: Transition to Online Credibility, Project Portfolio, and Resume

2.1: What is ATS and Why it is Important?

2.2: Follow These Guidelines to Build an Amazing Resume

2.3: Improve the ATS Score of Your Resume

2.4: Customize Resume as Per Job Posting

2.5: Career Transition: Sample Experience Sections NEW

2.6: Meet Naveen, He is applying for a Job Like You!

2.7: Resume Guidance 2026 - By the Talent Management Team NEW

2.8: Knowledge Check

2.9: Checkpoint

3.1: Resume Builder Intro NEW

4.1: Intro to Job Winning Resumes

4.2: Resume - 1 Career-break

4.3: Resume - 2 Fresher

4.4: Resume - 3 Fresher

4.5: Resume - 4 Fresher

4.6: Resume - 5 Career-break

4.7: Resume - 6 Experienced

4.8: Resume - 7 Career-Break

4.9: Resume - 8 Fresher

4.10: Resume - 9 NEW

4.11: Resume - 10 NEW

4.12: Resume - 11 NEW

4.13: Resume - 12 NEW

5.1: Get a Shareable Public Link For Your Power BI Project

5.2: Share Your Power BI Project with Potential Recruiters

5.3: Differentiate Your Work with New Design

5.4: How to differentiate your work - Expert Webinar

5.5: One More Power BI Project for Your Portfolio

5.6: Check Your Work With this Guided Project

5.7: One Step Closer to Your Dream Portfolio

6.1: Getting Started: Creating a Portfolio Website

6.2: Add Basic Info: Intro Video

6.3: Add Basic Info: Profile Picture

6.4: Add Basic Info: About Yourself

6.5: Add Resume and Skills

6.6: Add Projects & Certifications

6.7: Generate Portfolio & Share it With Potential Recruiters

7.1: Update Headline, Experience, etc. Sections on LinkedIn

7.2: How to Engage Meaningfully on LinkedIn?

7.3: List of influencers to follow

7.4: Knowledge Check

7.5: Checkpoint

8.1: What is Smart Job Assistance Portal and Why it is Important?

9.1: Practical Resources to Build Domain Knowledge

10.1: Transition to SQL

10.2: Checkpoint

2.1: Install MySQL: Windows

2.2: Install MySQL: Linux, Mac

2.3: Import Movies Dataset in MySQL

2.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)

2.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)

2.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)

2.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)

2.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)

2.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)

2.10: HAVING Clause

2.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)

2.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)

2.13: The Data God’s Blessing

2.14: Quiz

2.15: Chapter Summary

3.1: Why do We Need Multiple Tables?

3.2: SQL Joins (INNER, LEFT, RIGHT, FULL)

3.3: Exercise - SQL Joins (INNER, LEFT, RIGHT, FULL)

3.4: Cross Join

3.5: Analytics on Tables

3.6: Join More Than Two Tables

3.7: Exercise - Join More Than Two Tables

3.8: Quiz

3.9: Chapter Summary

4.1: Subqueries

4.2: ANY, ALL Operators

4.3: Co-Related Subquery

4.4: Exercise: Subqueries

4.5: Common Table Expression (CTE)

4.6: CTE Benefits & Other Applications

4.7: Exercise: CTE

4.8: Peter Pandey Flexes his SQL Skills

4.9: Quiz

4.10: Chapter Summary

5.1: Database Normalization and Data Integrity

5.2: Entity Relationship Diagram (ERD)

5.3: Mentor Talk: Art of Googling

5.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)

5.5: Data Types: String (VARCHAR, CHAR, ENUM)

5.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)

5.7: Data Types: JSON, Spatial (JSON, GEOMETRY)

5.8: Luck Favors the LinkedIn Post

5.9: Primary key

5.10: Foreign Key

5.11: Create a Database From an Entity Relationship Diagram - ERD

5.12: Import Data From a CSV File Into a Database

5.13: Insert Statement

5.14: Update and Delete

5.15: I Need a Favour

5.16: Expect the Unexpected: The Intermission Scene

5.17: Quiz

5.18: Chapter Summary

6.1: The Rise of Databases at AtliQ

6.2: Relational vs No-SQL Database

6.3: AtliQ Hardware’s Business Model

6.4: Profit & Loss Statement

6.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog

6.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import

6.7: Simplified: What is Kanban?

6.8: Quiz

6.9: Chapter Summary

7.1: Backlog Grooming Meeting: Gross Sales Report

7.2: User-Defined SQL Functions

7.3: Exercise: User-Defined SQL Functions

7.4: Gross Sales Report: Monthly Product Transactions

7.5: Gross Sales Report: Total Sales Amount

7.6: Exercise: Yearly Sales Report

7.7: Stored Procedures: Monthly Gross Sales Report

7.8: Stored Procedure: Market Badge

7.9: Benefits of Stored Procedures

7.10: Quiz

8.1: Problem Statement and Pre-Invoice Discount Report

8.2: Performance Improvement # 1

8.3: Performance Improvement # 2

8.4: Database Views: Introduction

8.5: Database Views: Post Invoice Discount, Net Sales

8.6: Exercise: Database Views

8.7: Top Markets and Customers

8.8: Exercise: Top Products

8.9: The Two Most Important Skills for the Data Analyst

8.10: Window Functions: OVER Clause

8.11: Window Functions: Using it in a Task

8.12: Exercise: Window Functions: OVER Clause

8.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK

8.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK

8.15: 5 Ways SQL is Used in the Industry

8.16: Quiz

9.1: Supply Chain Basics : Simplified

9.2: Problem Statement

9.3: Create a Helper Table

9.4: Database Triggers

9.5: Database Events

9.6: Temporary Tables & Forecast Accuracy Report

9.7: Exercise: CTE, Temporary Tables

9.8: Subquery vs CTE vs Views vs Temporary Table

9.9: User Accounts and Privileges

9.10: Database Indexes: Overview

9.11: Database Indexes: Composite Index

9.12: Database Indexes: Index Types

9.13: Quiz

10.1: Final Quiz

11.1: Transition to Python & Interviews

2.1: Project 1: Hospitality Domain Data Analysis

2.2: Project 2: Expense Tracking System

3.1: Python Installation - Windows

3.2: Python Installation - Linux

3.3: Python Installation - Mac

3.4: How to Download Code and Get Help If I Have a Question?

4.1: Variables

4.2: Numbers

4.3: Strings

4.4: Strings - Exercise

4.5: Quiz: Variables, Numbers and Strings

4.6: Peter shares his Coding Fear with Tony

4.7: Exercise: Variables, Numbers and Strings

4.8: Chapter Summary

5.1: Lists

5.2: Install Pycharm

5.3: If Condition

5.4: For Loop

5.5: Quiz: Lists, If Condition and For Loop

5.6: Peter Found Something Interesting to Practice

5.7: Exercise: Lists, If Condition and For Loop

5.8: Chapter Summary

6.1: Functions

6.2: Dictionary and Tuples

6.3: Modules and Pip

6.4: File Handling

6.5: Quiz: Functions, Dictionaries, Tuples and File Handling

6.6: Peter’s Request to Tony

6.7: Exercise: Functions, Dictionaries, Tuples and File Handling

6.8: Two Deadly Viruses Infecting Learners

6.9: Chapter Summary

7.1: Classes and Objects

7.2: Operator Overloading

7.3: Inheritance

7.4: Exception Handling

7.5: __main__ Function

7.6: Quiz: Classes, Exception Handling

7.7: Using Python in a Real Time Problem

7.8: Exercise: Classes and Exception Handling

7.9: Chapter Summary

8.1: Introduction and Benefits

8.2: Basic Operations

8.3: Matrix Operations

8.4: Slicing, Stacking

8.5: Quiz: NumPy

8.6: Peter Solves a Real Time Problem Using NumPy

8.7: Exercise: NumPy

8.8: Chapter Summary

9.1: Pandas Introduction and Installation

9.2: Dataframe Basics

9.3: Read, Write Excel and CSV Files

9.4: Handle NA values - Part 1

9.5: Handle NA values - Part 2

9.6: Group by

9.7: Concat and Merge

9.8: Data Visualization Using Matplotlib and Seaborn

9.9: Quiz: EDA using Python

10.1: Problem Statement, OLTP vs OLAP, ETL, Data Warehouse

10.2: Data Understanding: CSV Files

10.3: Data Understanding: Fact vs Dim table, Star vs Snowflake Schema

10.4: Data Exploration

10.5: Data Cleaning

10.6: Data Transformation

10.7: Insights Generation

11.1: Introduction

11.2: Set and Frozenset

11.3: Lists, Dict and Set Comprehensions

11.4: Quiz: Comprehensions and Sets

11.5: Peter gets Adhoc tasks on Advanced Topics

11.6: Exercise: Comprehensions and Sets

11.7: PEP8 Naming Convention

11.8: Code Debugging Using PyCharm

11.9: Chapter Summary

12.1: Working with JSON

12.2: Generators and Iterators

12.3: Decorators

12.4: Quiz: JSON, Generators and Decorators

12.5: Exercise: JSON, Generators and Decorators

12.6: Chapter Summary

13.1: What is API?

13.2: Calling APIs With requests Package

13.3: Building APIs With FastAPI

13.4: Quiz: APIs

13.5: Peter fetches data using APIs

13.6: Exercise: APIs

13.7: Chapter Summary

14.1: Logging

14.2: Automated Testing with Pytest

14.3: MySQL Setup: Windows

14.4: MySQL Setup: Linux, Mac

14.5: Working with MySQL in Python

14.6: Data Validation with Pydantic

14.7: Quiz: Logging, Pytest, Pydantic and Databases

14.8: Exercise: Pytest, Pydantic

14.9: Chapter Summary

15.1: Problem Statement & Tech Architecture

15.2: Database CRUD Operations

15.3: Automated Tests Setup for CRUD

15.4: Expense Management: Backend (FastAPI)

15.5: Expense Management: Logging

15.6: Streamlit Introduction

15.7: Expense Management: Frontend (Streamlit)

15.8: Analytics: Backend (FastAPI)

15.9: Analytics: Frontend (Streamlit)

15.10: README and Requirements.txt

15.11: Exercise

16.1: Disclaimer

17.1: Final_Quiz

18.1: Technical Architecture of the Project

18.2: Installation of Necessary Libraries

18.3: Extract text from a pdf document

18.4: Thresholding in OpenCV

18.5: Regular Expressions or Regex

18.6: Regex Exercise

18.7: Python class for prescription

18.8: Code Refactoring

18.9: Unit Tests using pytest

18.10: I Need a Favour

19.1: Write patient details class yourself

19.2: Extract name from a document

19.3: Extract remaining fields from a document

19.4: Write patient details class

19.5: Integrate doc parser classes into an extractor

20.1: FastAPI Basics

20.2: Write FastAPI server for our project

20.3: Integration With Frontend And Deployment

21.1: Transition to Online Credibility

2.1: Share Your SQL Project with Potential Recruiters

2.2: One More SQL Project For Your Portfolio

2.3: One Step Closer to Your Dream Portfolio

3.1: 3 Ways Of Job Application

3.2: List of companies hiring for data analyst roles

3.3: Naveen is Applying for Jobs

3.4: List the Best portals, and platforms to apply for the jobs

3.5: Knowledge Check

3.6: Checkpoint

4.1: Taking You One Step Closer To Your Job

5.1: Job Application Playbook Intro

6.1: Early Bird Offer

6.2: Finding Your Hiring Manager

6.3: Exploring Different Job Titles

6.4: Use Alumni Networks For Networking And Referrals

6.5: Connecting With Employees Through Glassdoor

6.6: Job Search Mindset 2026 - By the Talent Management Team NEW

7.1: I'm a fresher. How do I get a job when companies only want experienced candidates?

7.2: What ATS score do I need to pass resume screening?

7.3: Should I join a startup or a large company for my first job?

7.4: I have a career gap. Will companies reject me automatically?

7.5: How do I identify transferable skills from my previous experience for the role I'm targeting?

7.6: I've been applying for jobs for months and feel completely lost. What should I do differently?

7.7: Should I accept a low-paying job to gain experience or keep searching for a better opportunity?

7.8: Do recruiters actually look at LinkedIn profiles before shortlisting candidates?

7.9: How do I gain domain knowledge?

7.10: Is it still worth learning data skills in the age of AI?

8.1: Transition to Interview Preparation

2.1: Interview Playbook Intro

3.1: Naveen gets an Interview call

3.2: Think like a Hiring Manager

3.3: Introducing Yourself

3.4: Answering Questions on projects

3.5: SQL Questions

3.6: Excel Questions

3.7: Power BI Questions

3.8: Presenting Insights

3.9: Scenario Based Questions

3.10: Behavioral Questions

3.11: Asking Questions to Hiring Managers

3.12: Hiring Managers Discussion

3.13: Knowledge Check

3.14: Checkpoint

4.1: Mock Interview Intro

5.1: Interview Question Bank: Technical

5.2: MCQ Mock Test

6.1: Interview Coaching Live Session: July 2023

7.1: Fill This Form When You Land a Job

8.1: Transition (Trust the process and get into the virtual internship)

2.1: Welcome Note

2.2: Your Onboarding Letter

2.3: Welcome Note From Your Manager

2.4: Let's dive right into Week 1!

2.5: Getting Help From Your Seniors / Fellow Team Members

2.6: Your First Task

2.7: Incoming Task Email 1

2.8: Have you completed this task?

2.9: Quality Check 1

2.10: Quality Check 2

2.11: Quality Check 3

2.12: Quality Check 4

2.13: Congratulations you have completed the first task of your internship.

2.14: Incoming Task Email 2

2.15: Have you completed this task?

2.16: Quality Check

2.17: Congratulations you have completed 2 tasks in a row!

2.18: You Need Scrum Training

2.19: Incoming Task Email 3

2.20: Have you completed the assigned task?

2.21: Scrum Knowledge Check

2.22: Congratulations you have completed week 1 Successfully.

3.1: Let's dive right into Week 2!

3.2: Create This Variance Report

3.3: Incoming Task Email 1

3.4: Have you completed the assigned task?

3.5: Quality Check 1

3.6: Quality Check 2

3.7: Quality Check 3

3.8: Congratulations on finishing this task!

3.9: Incoming Task Email 2

3.10: Have you completed the assigned task?

3.11: Quality Check 1

3.12: Quality Check 2

3.13: Quality Check 3

3.14: Quality Check 4

3.15: Quality Check 5

3.16: Quality Check 6

3.17: Quality Check 7

3.18: Quality Check 8

3.19: Quality Check 9

3.20: Congratulations on successfully completing the task!

3.21: Incoming Task Email 3

3.22: Have you completed the assigned task?

3.23: Quality Check 1

3.24: Quality Check 2

3.25: Quality Check 3

3.26: Quality Check 4

3.27: Quality Check 5

3.28: You did an amazing job completing the task.

3.29: Critical Presentation Deck

3.30: Incoming Task Email 4

3.31: Have you completed the assigned task?

3.32: Presentation Submission

3.33: Congratulations

4.1: Let's dive right into the Final 2 Weeks !

4.2: Can You Handle This Insurance Project?

4.3: Incoming Task Email: Feature List

4.4: Please don't share datasets

4.5: Have you completed the feature list?

4.6: Download Benchmark Feature List

4.7: Quality Check

4.8: Don’t Forget to Send A Mock Up to Client

4.9: Have you completed the mock up?

4.10: Incoming Task

4.11: Response from Client

4.12: Quality Check

4.13: Incoming Task Email: Dashboard Creation

4.14: Have you completed the dashboard?

4.15: Submission Checklist

4.16: Please don't share Datasets

4.17: Dashboard Submission

4.18: End Note

4.19: Get Your Letter Of Completion

5.1: Fill This Form When You Land a Job

6.1: Final Note: End of the Bootcamp and All the best

2.1: Installing Tableau Public

2.2: Introduction to Tableau Interface

2.3: Data modeling

2.4: Data transformation (cleaning, filtering, joining)

2.5: Creating Measures

2.6: Quiz

2.7: Creating Basic Visuals

2.8: Creating dashboard & Stories

2.9: Tableau vs Power BI: Key Difference

2.10: Quiz

3.1: All Takeaways for Tableau Basics

4.1: Parameters

4.2: Introduction to LOD expressions

4.3: EXCLUDE: Simple Explanation with a Usecase

4.4: FIXED: Simple Explanation with a Usecase

4.5: INCLUDE: Simple Explanation with a Usecase

4.6: Quick Summary: LOD Expressions vs DAX

4.7: Quiz

5.1: All Takeaways for Tableau Advanced

6.1: About this Guided Project

6.2: Problem Statement

6.3: Data Discovery

6.4: Data Analysis Using SQL

6.5: Data Cleaning & ETL in Tableau

6.6: Build a Tableau Dashboard

6.7: Feedback From Stakeholders & Profit Analysis

7.1: Why you need to do this project

7.2: Provide Insights to the Product Strategy Team in the Banking Domain

8.1: Final Quiz

2.1: Why Fabric: AtliQ Hardware Problem Statement

2.2: View Fabric Using Your Power BI Account

2.3: Quiz

2.4: Breaking Down Fabric

2.5: How Much Should You Know as a Data Analyst?

3.1: Data Storage Options

3.2: Data Transformation Options

3.3: Quiz

4.1: Problem Statement: AtliQ Hardware Pilot Fabric Project

4.2: The Project Plan

5.1: Setting Up External Data in Fabric

5.2: Migrating Old BI 360 to Fabric

5.3: Update and Publish BI 360 to Fabric

5.4: Orchestrating Refreshes Using Data Pipeline

6.1: Domain Knowledge: Reliability in Supply Chain

6.2: Unguided Project Dataset

7.1: Final Quiz

2.1: What is Machine Learning

2.2: Classification vs Regression

2.3: Supervised vs Unsupervised Learning

2.4: ML Algorithms Overview

2.5: Tooling for ML

3.1: 10 Stages of AI Project Lifecycle

3.2: Requirements and Scope of Work (SOW)

3.3: Data Collection

3.4: Data Preparation & Exploratory Data Analysis

3.5: Feature Engineering

3.6: Model Selection & Training

3.7: Model Evaluation Metrics (Accuracy, Prediction, Recall & F1 Score)

3.8: Model Evaluation Metrics: When to use which Metric?

3.9: Model Fine Tuning

3.10: Model Deployment

3.11: Deployment & Monitoring Using ML Ops

4.1: The Role of Data in AI

4.2: Data Infrastructure in a Company

4.3: Data Collection: Overview

4.4: Data Storage and Transformation: Overview

4.5: Data Distribution: Privacy, Ethics & Governance

5.1: Data Collection

5.2: Data Cleaning

5.3: Data Transformation

5.4: Data Visualization & Insights

5.5: 4 General Use Cases

6.1: Why Humans are Better in Communication Compared To AI?

6.2: Using AI for Emails & Presentations

7.1: Job Titles & Resume Customization

7.2: Asking For Referral

7.3: Mock Interviews: Resume & Job-Specific Interview Questions

7.4: Mock Interviews: Technical Interview

7.5: Mock Interviews: Case Study & Behavioral

8.1: PDF File of Useful AI Prompts

2.1: Welcome Note

2.2: Your Onboarding Letter

2.3: Welcome Note From Your Manager

2.4: Let's dive right into Week 1!

2.5: Getting Help From Your Seniors / Fellow Team Members

2.6: Your First Task

2.7: Incoming Task Email 1

2.8: Have you completed this task?

2.9: Quality Check 1

2.10: Quality Check 2

2.11: Quality Check 3

2.12: Quality Check 4

2.13: Congratulations you have completed the first task of your internship.

2.14: Python Script Task

2.15: Incoming Task Email 2

2.16: Have you completed this task?

2.17: Quality Check 1

2.18: Quality Check 2

2.19: Quality Check 3

2.20: Quality Check 4

2.21: Quality Check 5

2.22: Congratulations you have completed 2 tasks in a row!

2.23: You need Kanban Training

2.24: Incoming Task Email 3

2.25: Have you completed the assigned task?

2.26: Kanban Knowledge Check

2.27: Congratulations you have completed week 1 Successfully.

3.1: Let's dive right into Week 2!

3.2: Rhonda MVP Project - Your First Power BI Task

3.3: Incoming Task Email 1

3.4: Have you completed the assigned task?

3.5: Quality Check - 1

3.6: Quality Check - 2

3.7: Designing and Enhancing a Dashboard

3.8: Create a Mock-up for the Client

3.9: Incoming Task Email: Dashboard Creation

3.10: Have you completed the dashboard?

3.11: Congratulations

4.1: Let's dive right into the Final 2 Weeks !

4.2: Can you help our Marketing Team?

4.3: Incoming Task Email: Netflix Analysis

4.4: Have you completed the analysis?

4.5: Quality Check - 1

4.6: Quality Check - 2

4.7: Write a mail to the Social Media Analytics Team

4.8: Response from the Social Media Analytics Team

4.9: Appreciation & New Project Task

4.10: Incoming Task Email: Tech Instagram Influencer Analysis

4.11: Have you completed the analysis?

4.12: Presentation submission

4.13: End Note

4.14: Get Your Letter Of Completion

1.1: Why Unguided Projects?

2.1: Provide Insights to Management in Consumer Goods Domain NEW

3.1: Provide Insights to an Automotive company on Electric vehicles launch in India

4.1: Dashboard Migration: Tableau to Power BI

2.1: Why Domain Knowledge?

2.2: Understanding KPIs

2.3: Practical Resources to Build Domain Knowledge

3.1: How to Enhance your Mockups

4.1: Why Guesstimate?

4.2: Solving a Guesstimate: Top Down Approach

4.3: Solving a Guesstimate: Bottom Up Approach

5.1: Scenario - 1

5.2: Quality Checks

5.3: Scenario - 2

5.4: Quality Checks

5.5: Scenario - 3

5.6: Quality Checks

5.7: Scenario - 4

5.8: Quality Checks

5.9: Scenario - 5

5.10: Quality Checks

5.11: Scenario - 6

5.12: Quality Checks

5.13: Scenario - 7

5.14: Quality Checks

5.15: Scenario - 8

5.16: Quality Checks

5.17: Scenario - 9 NEW

5.18: Quality Checks

5.19: Scenario - 10 NEW

5.20: Quality Checks

5.21: Scenario - 11 NEW

5.22: Quality Checks

5.23: Scenario - 12 NEW

5.24: Quality Checks

5.25: Scenario - 13 NEW

5.26: Quality Checks

2.1: LinkedIn Guidance 2026 – By the Talent Management Team NEW

2.2: Resume Guidance 2026 - By the Talent Management Team NEW

2.3: Job Search Mindset 2026 - By the Talent Management Team NEW

2.4: Beginners Guide to Job Seeking - Sep 23

2.5: 6 Free Internet Tools to Get an Interview Call - Oct 23

2.6: Smart Job Assistance Portal & Expert Resume Insights

2.7: The Secret Behind Resumes and Portfolios That Landed Jobs: Decode with your Talent Manager

2.8: Strategic Job Search with Google, LinkedIn and Naukri - 22nd November

3.1: How to use Discord

4.1: Freelancing in Data Analytics & Building Your Credibility on LinkedIn - By Zain Altaf NEW

4.2: The Practical Power BI Workflow and UAT Process Every Analyst Should Know - By Trilochan Tripathy

4.3: Transitioning from Non-Tech to Data Analytics - Journey and Tips by Shail Sahu

4.4: PL-300 Certification: What You Need to Know & How to Prepare - Anmol Malviya

4.5: How to Approach Scenario-Based Questions and Guesstimates in the Interviews - Shashank Singh

4.6: Tips and Tricks to Approach Data Analyst Interviews - Gaurav Agrawal

4.7: How I would prepare for Data Analyst Interviews If I had to start over - Munna Das

4.8: Key Lessons and Interview Tips from My Journey as a Data Analyst - Bharath Kumar G

4.9: My Life as a Data Analyst at Ford Motors- Raghavan P

4.10: Data Analytics Freelancing Essentials - Santhanalakshmi Ponnurasan

4.11: How to differentiate your work - Ashish Babaria

2.1: How to Get Maximum Out of this Course

2.2: Join Discord for doubt clearing support & networking

2.3: Setting Video Resolution and Video Speed Globally

3.1: OLAP vs OLTP

3.2: Role of Data Engineer, Data Analyst, AI Engineer

3.3: Data Lake, Data Warehouse, Data Lakehouse

3.4: ETL vs ELT

3.5: Quiz

4.1: Medallion Architecture

4.2: Row-based File Formats - CSV, JSON

4.3: Columnar File Formats - Parquet

4.4: Quiz

5.1: Problem Statement

5.2: Data Engineering Architecture

6.1: AWS Account Setup

6.2: OLTP Data Understanding

6.3: AWS S3 Overview and Bucket Setup

6.4: Data Ingestion in S3 Data Lake

7.1: Log Files ETL: Local Data Transformation

7.2: AWS Lambda Overview

7.3: Log Files ETL: AWS Lambda Manual Trigger

7.4: Log Files ETL: AWS Lambda Automated Trigger

7.5: CSV Files ETL: AWS Glue

7.6: CSV Files ETL: Automation using S3 Events

7.7: Glue vs Lambda

7.8: Setting up Budget Alerts

8.1: AWS Athena Overview

8.2: Athena Setup

8.3: Ad-Hoc SQL Queries Using Athena

8.4: Amazon Redshift Overview

8.5: Load S3 Data into Redshift

8.6: Build Power BI Dashboard with Redshift Data

8.7: Incremental Data Load in the Data Warehouse

8.8: I need a favour

8.9: Exercise

8.10: Important Note

9.1: How to use this chapter?

9.2: AWS vs Azure: An Analyst’s Overview

9.3: Snowflake: An Analyst’s Overview

9.4: Databricks: An Analyst’s Overview

9.5: Apache Airflow: An Analyst’s Overview

9.6: DBT: An Analyst’s Overview

9.7: Quiz

9.8: Exercise

10.1: Practice 1: Data Masking for PII Protection

10.2: Practice 2: Versioning Data and Pipelines

10.3: Practice 3: Maintaining Proper Documentation

10.4: Practice 4: Effective Data Modelling

10.5: Practice 5: Creating Clear Pipeline Diagrams

10.6: Quiz

10.7: Exercise

11.1: How to use this chapter

11.2: Interview Questions

12.1: Final Quiz

13.1: End Note

2.1: Join Discord

2.2: Join Discord - Guide

2.3: How to get maximum benefit from this course?

2.4: Setting Video Resolution and Video Speed Globally

3.1: Project 1: Automating CSV Append from Email to Postgres DB

3.2: Project 2: API Automation - Exchange Rate Data Collection

3.3: Quiz

4.1: Creating a Natural Language - to - SQL Query Agent

4.2: Quiz