LIVE Data Engineering Bootcamp for Analysts: Become the End-to-End Data Professional

2.1: Who can Enroll in this Course? Any Prerequisites?

2.2: What Type of Computer do I Need for this Course?

2.3: Course Outline

3.1: How Learning SQL can Help You in Your Career

3.2: Who is Peter Pandey?

3.3: Peter Pandey’s Early Life and SQL

4.1: Setting Video Resolution and Video Speed Globally

4.2: Join Discord

5.1: Install MySQL: Windows

5.2: Install MySQL: Linux, Mac

5.3: Import Movies Dataset in MySQL

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

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

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

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

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

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

5.10: HAVING Clause

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

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

5.13: The Data God’s Blessing

5.14: Quiz

5.15: Chapter Summary

6.1: Why do We Need Multiple Tables?

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

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

6.4: Cross Join

6.5: Analytics on Tables

6.6: Join More Than Two Tables

6.7: Exercise - Join More Than Two Tables

6.8: Quiz

6.9: Chapter Summary

7.1: Subqueries

7.2: ANY, ALL Operators

7.3: Co-Related Subquery

7.4: Exercise: Subqueries

7.5: Common Table Expression (CTE)

7.6: CTE Benefits & Other Applications

7.7: Exercise: CTE

7.8: Peter Pandey Flexes his SQL Skills

7.9: Quiz

7.10: Chapter Summary

8.1: Database Normalization and Data Integrity

8.2: Entity Relationship Diagram (ERD)

8.3: Mentor Talk: Art of Googling

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

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

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

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

8.8: Luck Favors the LinkedIn Post

8.9: Primary key

8.10: Foreign Key

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

8.12: Import Data From a CSV File Into a Database

8.13: Insert Statement

8.14: Update and Delete

8.15: I Need a Favour

8.16: Expect the Unexpected: The Intermission Scene

8.17: Quiz

8.18: Chapter Summary

9.1: The Rise of Databases at AtliQ

9.2: Relational vs No-SQL Database

9.3: AtliQ Hardware’s Business Model

9.4: Profit & Loss Statement

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

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

9.7: Simplified: What is Kanban?

9.8: Quiz

9.9: Chapter Summary

10.1: Backlog Grooming Meeting: Gross Sales Report

10.2: User-Defined SQL Functions

10.3: Exercise: User-Defined SQL Functions

10.4: Gross Sales Report: Monthly Product Transactions

10.5: Gross Sales Report: Total Sales Amount

10.6: Exercise: Yearly Sales Report

10.7: Stored Procedures: Monthly Gross Sales Report

10.8: Stored Procedure: Market Badge

10.9: Benefits of Stored Procedures

10.10: Quiz

11.1: Problem Statement and Pre-Invoice Discount Report

11.2: Performance Improvement # 1

11.3: Performance Improvement # 2

11.4: Database Views: Introduction

11.5: Database Views: Post Invoice Discount, Net Sales

11.6: Exercise: Database Views

11.7: Top Markets and Customers

11.8: Exercise: Top Products

11.9: The Two Most Important Skills for the Data Analyst

11.10: Window Functions: OVER Clause

11.11: Window Functions: Using it in a Task

11.12: Exercise: Window Functions: OVER Clause

11.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK

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

11.15: 5 Ways SQL is Used in the Industry

11.16: Quiz

12.1: Window Functions: OVER Clause

12.2: Window Functions: ROW_NUMBER, RANK, DENSE_RANK

12.3: Quiz

13.1: Supply Chain Basics : Simplified

13.2: Problem Statement

13.3: Create a Helper Table

13.4: Database Triggers

13.5: Database Events

13.6: Temporary Tables & Forecast Accuracy Report

13.7: Exercise: CTE, Temporary Tables

13.8: Subquery vs CTE vs Views vs Temporary Table

13.9: User Accounts and Privileges

13.10: Database Indexes: Overview

13.11: Database Indexes: Composite Index

13.12: Database Indexes: Index Types

13.13: Peter Pandey's Order: I Have Completed the Course - Now What?

13.14: Quiz

14.1: Final Quiz

15.1: Transition to Python & Interviews

16.1: Step Into the Practice Room (SQL)

2.1: Join the discord channel

3.1: SQL Practice Questions in Practice Arena

2.1: Course Introduction

2.2: What Benefits You Get By Learning Python?

2.3: Why Python is so Popular?

2.4: Who can Enroll In This Course? Any Prerequisites?

2.5: How is This Different From Free Codebasics YouTube Playlist?

2.6: What Kind Of Computer Do I Need For This Course?

2.7: Course Content Overview

3.1: Python for Data Engineering

3.2: Important Note [ Mandatory ]

4.1: Join Discord for doubt clearing support & networking

4.2: Setting Video Resolution and Video Speed Globally

4.3: Important Note [ Mandatory ]

5.1: Project 1: Hospitality Domain Data Analysis

5.2: Project 2: Expense Tracking System

6.1: Peter Pandey’s Journey and Need to Learn Python

6.2: Peter Pandey’s Journey and Need to Learn Python

6.3: Python Installation - Windows

6.4: Python Installation - Linux

6.5: Python Installation - Mac

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

7.1: Variables

7.2: Numbers

7.3: Strings

7.4: Strings - Exercise

7.5: Quiz: Variables, Numbers and Strings

7.6: Peter shares his Coding Fear with Tony

7.7: Exercise: Variables, Numbers and Strings

7.8: Chapter Summary

8.1: Lists

8.2: Install Pycharm

8.3: If Condition

8.4: For Loop

8.5: Quiz: Lists, If Condition and For Loop

8.6: Peter Found Something Interesting to Practice

8.7: Exercise: Lists, If Condition and For Loop

8.8: Chapter Summary

9.1: Functions

9.2: Dictionary and Tuples

9.3: Modules and Pip

9.4: File Handling

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

9.6: Peter’s Request to Tony

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

9.8: Two Deadly Viruses Infecting Learners

9.9: Chapter Summary

10.1: Classes and Objects

10.2: Operator Overloading

10.3: Inheritance

10.4: Exception Handling

10.5: __main__ Function

10.6: Quiz: Classes, Exception Handling

10.7: Using Python in a Real Time Problem

10.8: Exercise: Classes and Exception Handling

10.9: Chapter Summary

11.1: Introduction and Benefits

11.2: Basic Operations

11.3: Matrix Operations

11.4: Slicing, Stacking

11.5: Quiz: NumPy

11.6: Peter Solves a Real Time Problem Using NumPy

11.7: Exercise: NumPy

11.8: Chapter Summary

12.1: Pandas Introduction and Installation

12.2: Dataframe Basics

12.3: Read, Write Excel and CSV Files

12.4: Handle NA values - Part 1

12.5: Handle NA values - Part 2

12.6: Group by

12.7: Concat and Merge

12.8: Data Visualization Using Matplotlib and Seaborn

12.9: Quiz: EDA using Python

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

13.2: Data Understanding: CSV Files

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

13.4: Data Exploration

13.5: Data Cleaning

13.6: Data Transformation

13.7: Insights Generation

14.1: Introduction

14.2: Set and Frozenset

14.3: Lists, Dict and Set Comprehensions

14.4: Quiz: Comprehensions and Sets

14.5: Peter gets Adhoc tasks on Advanced Topics

14.6: Exercise: Comprehensions and Sets

14.7: PEP8 Naming Convention

14.8: Code Debugging Using PyCharm

14.9: Chapter Summary

15.1: Working with JSON

15.2: Generators and Iterators

15.3: Decorators

15.4: Quiz: JSON, Generators and Decorators

15.5: Exercise: JSON, Generators and Decorators

15.6: Chapter Summary

16.1: What is API?

16.2: Calling APIs With requests Package

16.3: Building APIs With FastAPI

16.4: Quiz: APIs

16.5: Peter fetches data using APIs

16.6: Exercise: APIs

16.7: Chapter Summary

17.1: Working with JSON

17.2: What is API?

17.3: Calling APIs With requests Package

17.4: Building APIs With FastAPI

17.5: Quiz: APIs

17.6: Peter fetches data using APIs

17.7: Exercise: APIs

17.8: Chapter Summary

18.1: Logging

18.2: Automated Testing with Pytest

18.3: MySQL Setup: Windows

18.4: MySQL Setup: Linux, Mac

18.5: Working with MySQL in Python

18.6: Data Validation with Pydantic

18.7: Quiz: Logging, Pytest, Pydantic and Databases

18.8: Exercise: Pytest, Pydantic

18.9: Chapter Summary

19.1: Problem Statement & Tech Architecture

19.2: Database CRUD Operations

19.3: Automated Tests Setup for CRUD

19.4: Expense Management: Backend (FastAPI)

19.5: Expense Management: Logging

19.6: Streamlit Introduction

19.7: Expense Management: Frontend (Streamlit)

19.8: Analytics: Backend (FastAPI)

19.9: Analytics: Frontend (Streamlit)

19.10: README and Requirements.txt

19.11: Exercise

20.1: Disclaimer

21.1: 7 Career Options After Learning Python

21.2: Next Steps: Python Backend Engineer, Software Engineer

21.3: Next Steps: Data Analyst, Data Scientist

21.4: Stay Connected And Thank You

22.1: Final Quiz

22.2: Final_Quiz

23.1: Python Interview Question Bank

24.1: Technical Architecture of the Project

24.2: Installation of Necessary Libraries

24.3: Extract text from a pdf document

24.4: Thresholding in OpenCV

24.5: Regular Expressions or Regex

24.6: Regex Exercise

24.7: Python class for prescription

24.8: Code Refactoring

24.9: Unit Tests using pytest

24.10: I Need a Favour

25.1: Write patient details class yourself

25.2: Extract name from a document

25.3: Extract remaining fields from a document

25.4: Write patient details class

25.5: Integrate doc parser classes into an extractor

26.1: FastAPI Basics

26.2: Write FastAPI server for our project

26.3: Integration With Frontend And Deployment

27.1: Transition to Online Credibility

28.1: Step Into the Practice Room (Python)

2.1: Scenario Question

2.2: Have you solved the problem?

2.3: Quality Check -1

2.4: Quality Check - 2

3.1: Scenario Question

3.2: Have you solved the problem?

3.3: Quality Check - 1

3.4: Quality Check - 2

4.1: Scenario Question

4.2: Have you solved the problem?

4.3: Quality Check -1

4.4: Quality Check - 2

5.1: Scenario Question

5.2: Have you solved the problem?

5.3: Quality Check -1

5.4: Quality Check - 2

6.1: Scenario Question

6.2: Have you solved the problem?

6.3: Quality Check - 1

6.4: Quality Check - 2

7.1: Scenario Question

7.2: Have you solved the problem?

7.3: Quality Check -1

7.4: Quality Check -2

8.1: Scenario Question

8.2: Have you solved the problem?

8.3: Quality Check -1

8.4: Quality Check - 2

9.1: Scenario Question

9.2: Have you solved the problem?

9.3: Quality Check -1

9.4: Quality Check - 2

10.1: Coming Soon!

2.1: Introduction to online credibility / open-source contribution

3.1: Set Up Your LinkedIn Profile

3.2: LinkedIn Banners

4.1: Why Domain Knowledge?

4.2: Understanding KPIs

5.1: Add your Python Project to GitHub

5.2: Share Your Python Skill to the World

5.3: Exercise: Add your SQL project to GitHub and Share your SQL skill to the world

6.1: Knowledge Check

6.2: Transitioning to Data Engineering Foundations

6.3: Checkpoint

2.1: Peter decides To Get Into Data Engineering

3.1: How to Get Maximum Out of this Course

3.2: Join Discord for doubt clearing support & networking

3.3: Setting Video Resolution and Video Speed Globally

4.1: OLAP vs OLTP

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

4.3: Data Lake, Data Warehouse, Data Lakehouse

4.4: ETL vs ELT

4.5: Quiz

5.1: OLAP vs OLTP

5.2: Data Lake, Data Warehouse, Data Lakehouse

6.1: ETL vs ELT

6.2: Medallion Architecture

6.3: Row-based File Formats - CSV, JSON

6.4: Columnar File Formats – Parquet

6.5: Quiz

7.1: Medallion Architecture

7.2: Row-based File Formats - CSV, JSON

7.3: Columnar File Formats - Parquet

7.4: Quiz

8.1: Data Normalization & Denormalization

8.2: What is Data Modeling

8.3: Star vs Snowflake Schema, Fact & Dimension Tables

8.4: What is Dimension Modelling?

8.5: Slowly Changing Dimensions (Type 1, 2)

9.1: Problem Statement

9.2: Data Engineering Architecture

10.1: AWS Account Setup

10.2: OLTP Data Understanding

10.3: AWS S3 Overview and Bucket Setup

10.4: Data Ingestion in S3 Data Lake

11.1: Log Files ETL: Local Data Transformation

11.2: AWS Lambda Overview

11.3: Log Files ETL: AWS Lambda Manual Trigger

11.4: Log Files ETL: AWS Lambda Automated Trigger

11.5: CSV Files ETL: AWS Glue

11.6: CSV Files ETL: Automation using S3 Events

11.7: Glue vs Lambda

11.8: Setting up Budget Alerts

12.1: AWS Athena Overview

12.2: Athena Setup

12.3: Ad-Hoc SQL Queries Using Athena

12.4: Amazon Redshift Overview

12.5: Load S3 Data into Redshift

12.6: Build Power BI Dashboard with Redshift Data

12.7: Incremental Data Load in the Data Warehouse

12.8: I need a favour

12.9: Exercise

12.10: Important Note

13.1: How to use this chapter?

13.2: AWS vs Azure: An Analyst’s Overview

13.3: Snowflake: An Analyst’s Overview

13.4: Databricks: An Analyst’s Overview

13.5: Apache Airflow: An Analyst’s Overview

13.6: DBT: An Analyst’s Overview

13.7: Quiz

13.8: Exercise

14.1: Practice 1: Data Masking for PII Protection

14.2: Practice 2: Versioning Data and Pipelines

14.3: Practice 3: Maintaining Proper Documentation

14.4: Practice 4: Effective Data Modelling

14.5: Practice 5: Creating Clear Pipeline Diagrams

14.6: Quiz

14.7: Exercise

15.1: How to use this chapter

15.2: Interview Questions

16.1: Final Quiz

17.1: End Note

18.1: Quiz

19.1: Transitioning to Apache Spark & Databricks

2.1: Basic Operations

2.2: Reading Data from CSV

2.3: Handle Missing Values

2.4: SQL in pyspark

2.5: Joins

3.1: Reading Spark Plans with explain()

3.2: Spark Architecture

3.3: Transformations vs Actions and Lazy Evaluation

3.4: Narrows vs Wide Transformations

3.5: Partitions & Parallelism: Repartition

3.6: Partitions & Parallelism: Coalesce

3.7: Lokis advice to Peter on using AI tools

3.8: Catalyst Optimizer, Predicate Pushdown, Column Pruning

3.9: Joins at Scale: Broadcast, Shuffle Hash and Hints

3.10: Data Skew and Mitigation Techniques

3.11: Adaptive Query Execution (AQE)

3.12: RDD, Dataframes and Datasets

3.13: Managed vs External Tables

3.14: Unity Catalog: Governance & Access Control

3.15: Understanding ACID

3.16: Time Travel Explained

3.17: What is Delta Lake and Delta Format

4.1: Final Quiz

2.1: Create a Free Azure Account

2.2: Databricks Setup in Azure

2.3: Upload Data in ADLS

2.4: Setup Catalog and Azure Connectors

2.5: Define Raw Schema

3.1: Ingest Dimension Data into Bronze Layer

3.2: Bronze to Silver: Data Cleansing and Transformation

3.3: Silver to Gold: Building BI Ready Tables

4.1: Batch vs Stream Processing

4.2: Autoloader and Structured Streaming

4.3: Ingest Fact Data into Bronze

4.4: Bronze to Silver: Data Cleansing and Transformation

4.5: Silver to Gold: Building BI Ready Tables

4.6: Build Daily Summary Table

5.1: Dimensional Pipeline

5.2: Fact Pipeline

5.3: Daily Refresh Job

6.1: Governance through Unity Catalog

6.2: Build BI Dashboard in Power BI

7.1: Exercise Walkthrough

2.1: Spark Setup Guide

3.1: Scenario Question

3.2: Have you completed the problem?

3.3: Q1: Quality Check - 1

3.4: Q1: Quality Check -2

4.1: Scenario Question

4.2: Have you completed the problem?

4.3: Q2: Quality Check - 1

4.4: Q2: Quality Check -2

5.1: Scenario Question

5.2: Have you completed the problem?

5.3: Q3: Quality Check - 1

5.4: Q3: Quality Check - 2

6.1: More Practice Questions

7.1: Scenario Question

7.2: Quality Check - 1

7.3: Quality Check - 2

8.1: Scenario Question

8.2: Quality Check -1

8.3: Quality Check -2

9.1: Scenario Question

9.2: Scenario Question -1

9.3: Scenario Question -2

10.1: Scenario Question

10.2: Quality Check -1

10.3: Quality Check -2

11.1: Scenario Question

11.2: Scenario Question -1

12.1: Scenario Question

13.1: Scenario Question

14.1: Scenario Question

14.2: Quality Check -1

14.3: Quality Check -2

15.1: Scenario Question

16.1: Scenario Question

16.2: Quality Check -1

16.3: Quality Check -2

17.1: Scenario Question

17.2: Quality Check -1

17.3: Quality Check -2

18.1: Scenario Question

2.1: What is Smart Job Assistance Portal, and why is it important?

3.1: What is ATS and Why it is Important?

3.2: Build an ATS Resume using Resume Builder Checklist

3.3: Improve the ATS Score of your Resume

3.4: Sample Resumes

4.1: Getting Started: Creating a Portfolio Website

4.2: Add Basic Info: Intro Video

4.3: Add Basic Info: Profile Picture

4.4: Add Basic Info: About Yourself

4.5: Add Resume and Skills

4.6: Add Projects & Certifications

4.7: Generate Portfolio and Share it With Potential Recruiters

5.1: Update Headline, Experience, etc, Sections on LinkedIn

5.2: How to Engage Meaningfully on LinkedIn

5.3: How to approach people on LinkedIn

5.4: List of Influencers to Follow

6.1: Practical Resources to Build Domain Knowledge

7.1: Transitioning to Snowflake and Airflow

2.1: What is Snowflake?

2.2: Snowflake Free Trial and Registration

2.3: Your First SQL Queries with Tasty Bytes

2.4: Key Features and Advantages

2.5: Snowflake vs Traditional Datawarehouse

2.6: Snowflake vs Databricks

3.1: Snowflake Architecture

3.2: Virtual Warehouses

3.3: Databases, Schemas, Tables, Views

3.4: Roles, Users, Access Control

4.1: Integrations

4.2: Stages (Internal and External)

4.3: File Pattern and Format

4.4: COPY Command

4.5: Hands on Practice: Storage Integration

4.6: Hands on Practice: Load Data from S3

4.7: Snowpipe

5.1: Time Travel

5.2: Zero Copy Cloning

6.1: Quiz

2.1: Create Your First DAG

2.2: DAG, Task, Operator

3.1: Hook, Provider, Task Group

4.1: Final Quiz

2.1: Massive/Polygon Setup

2.2: Extract Historical Data using Polygon

2.3: Backfill Raw Historical Data in Snowflake

2.4: Transform and Load into Core

2.5: Populate Dim Tables

2.6: Populate Fact Tables

3.1: Daily File Download through Airflow

3.2: Setup Connection with S3

3.3: Upload Data to S3 via DAG

3.4: Create S3 Stage in Snowflake

3.5: Connect Airflow with Snowflake

3.6: Ingest Data in Snowflake RAW

3.7: Transform from RAW to CORE

3.8: Ingest Invalid Records in Reject Table

4.1: Connect Airflow with Slack

4.2: Add Slack Integration in DAG

4.3: What is SA Layer?

4.4: Build Views for SA Layer

4.5: Power BI Dashboard

5.1: Step Into the Practice Room (Snowflake & Airflow)

2.1: Step Into the Practice Room (Snowflake & Airflow)

2.1: Scenario Question

3.1: Scenario Question

4.1: Scenario Question

5.1: Scenario Question

6.1: Scenario Question

7.1: Scenario Question

8.1: Scenario Question

9.1: Scenario Question

10.1: Snowflake practice Question

10.2: Airflow Practice Questions

10.3: What’s Next?

2.1: Producers Deep Dive

2.2: Python Consumers Demo

2.3: Consumer Groups

2.4: Serialization

2.5: Schema Evolution

3.1: Batch vs Stream Processing

3.2: What is Apache Flink?

3.3: ksqlDB vs Flink

3.4: Hands on Flink in Confluent

3.5: Watermarks

3.6: Flink: Real World Use-cases

3.7: Flink Architecture

3.8: Checkpoint and Fault Tolerance

4.1: Schema-on-Write vs Schema-on-Read

4.2: Schema Validation in Python Producer

4.3: Flink SQL: Creating Sink Tables

5.1: Final Quiz

2.1: Setup Environment: Topic, Registry, Cluster

2.2: Producer (Vehicle simulator)

3.1: Flink: Filter Stream Data

3.2: Flink: Collect Alerts in a Common Topic

3.3: Notifications Consumer

3.4: Setup Twilio Account

3.5: Send WhatsApp Notifications using Twilio

3.6: End Note

4.1: Hot Path: Implementation

4.2: Cold Path: Implementation

4.3: Doubt-Clearing Session

2.1: Practice Questions

3.1: Practice Questions

2.1: More Data Engineering Projects for your Portfolio

3.1: Taking You One Step Closer To Your Job

4.1: 3 Ways Of Job Application

4.2: List of companies hiring for Data Engineer roles

4.3: List the best portals, and platforms to apply for the jobs

4.4: Knowledge Check

4.5: Checkpoint

5.1: Introduction to the Job Application Playbook

6.1: Transition to Interview Preparation

2.1: Interview Question Bank: Technical

3.1: Introduction to the Interview Preparation Playbook

4.1: Fill This Form When You Land a Job

5.1: Transition to Virtual Internship (Trust the Process)

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: Your First Task

2.6: Getting Help From Your Seniors / Fellow Team Members

2.7: Incoming Task Email 1

2.8: Have you completed this task?

2.9: Quality Check 0

2.10: Quality Check 1

2.11: Quality Check 2

2.12: Quality Check 3

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

2.14: You Need to understand Scrum in Jira

2.15: Incoming Task Email 2

2.16: Have you completed the assigned task?

2.17: Scrum & Jira Knowledge Check

2.18: Congratulations you have completed week 1 Successfully.

3.1: Let's dive right into Week 2!

3.2: Metadata Logging

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: Congratulations on finishing this task!

3.8: Incoming Task Email 2

3.9: Have you completed this task?

3.10: Quality Check 1

3.11: Quality Check 2

3.12: Quality Check 3

3.13: Quality Check 4

3.14: Congratulations on successfully completing the task!

3.15: Critical Presentation Deck

3.16: Incoming Task Email 3

3.17: Have you completed this task?

3.18: Congratulations you have completed week 2 successfully

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

4.2: Can You Handle the FinSight Corp Project?

4.3: Incoming Task Email: Capstone Project

4.4: Please don't share datasets

4.5: Have you completed the task?

4.6: Quality Check 1

4.7: Quality Check 2

4.8: Quality Check 3

4.9: Implement data quality & validation framework

4.10: Incoming Task Email

4.11: Have you completed the assigned task?

4.12: Quality Check 1

4.13: Quality Check 2

4.14: Quality Check 3

4.15: Quality Check 4

4.16: Incoming Task Email: Handling Schema Drift & Column Chaos

4.17: Have you handled the schema drift in your solution?

4.18: Quality Check 1

4.19: Quality Check 2

4.20: Quality Check 3

4.21: Quality Check 4

4.22: Quality Check 5

4.23: Incoming Task Email: Pipeline Optimization

4.24: Have you completed this task?

4.25: Presentation Submission

4.26: End Note

4.27: 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: Backlog Grooming Meeting: Gross Sales Report

2.2: User-Defined SQL Functions

2.3: Exercise: User-Defined SQL Functions

2.4: Gross Sales Report: Monthly Product Transactions

2.5: Gross Sales Report: Total Sales Amount

2.6: Exercise: Yearly Sales Report

2.7: Stored Procedures: Monthly Gross Sales Report

2.8: Stored Procedure: Market Badge

2.9: Benefits of Stored Procedures

2.10: Quiz

3.1: Problem Statement and Pre-Invoice Discount Report

3.2: Performance Improvement # 1

3.3: Performance Improvement # 2

3.4: Database Views: Introduction

3.5: Database Views: Post Invoice Discount, Net Sales

3.6: Exercise: Database Views

3.7: Top Markets and Customers

3.8: Exercise: Top Products

3.9: The Two Most Important Skills for the Data Analyst

3.10: Quiz

4.1: Supply Chain Basics : Simplified

4.2: Problem Statement

4.3: Create a Helper Table

4.4: Database Triggers

4.5: Database Events

4.6: Temporary Tables & Forecast Accuracy Report

4.7: Exercise: CTE, Temporary Tables

4.8: Subquery vs CTE vs Views vs Temporary Table

4.9: User Accounts and Privileges

4.10: Database Indexes: Overview

4.11: Database Indexes: Composite Index

4.12: Database Indexes: Index Types

4.13: Quiz

2.1: Problem Statement & Tech Architecture

2.2: Database CRUD Operations

2.3: Automated Tests Setup for CRUD

2.4: Expense Management: Backend (FastAPI)

2.5: Expense Management: Logging

2.6: Streamlit Introduction

2.7: Expense Management: Frontend (Streamlit)

2.8: Analytics: Backend (FastAPI)

2.9: Analytics: Frontend (Streamlit)

2.10: README and Requirements.txt

2.11: Exercise

3.1: Technical Architecture of the Project

3.2: Installation of Necessary Libraries

3.3: Extract text from a pdf document

3.4: Thresholding in OpenCV

3.5: Regular Expressions or Regex

3.6: Regex Exercise

3.7: Python class for prescription

3.8: Code Refactoring

3.9: Unit Tests using PyTest

4.1: Write patient details class yourself

4.2: Extract name from a document

4.3: Extract remaining fields from a document

4.4: Write patient details class

4.5: Integrate doc parser classes into an extractor

5.1: FastAPI Basics

5.2: Write FastAPI server for our project

5.3: Integration With Frontend And Deployment

2.1: Assessment Introduction

3.1: Join Discord for doubt clearing support & networking

3.2: Setting Video Resolution and Video Speed Globally

4.1: Why LinkedIn?

4.2: 3 Steps to Build Your Online Credibility on LinkedIn

4.3: Quiz

5.1: Profile Picture & Banner Image

5.2: Headline, About & Experience

5.3: Featured Section & Other Key Areas

5.4: Quiz

5.5: Activity: Redesign your profile using Hero Checklist

6.1: Commenting the right way

6.2: Templates for Insightful Comments

6.3: The Positive Influencers List

6.4: Approaching People the Right Way

6.5: Quiz

6.6: Activity: Comment on 5 posts

7.1: Mental Model of Content Creation

7.2: 6 Fundamental ways to create a post with real examples

7.3: Effective Template for Posting

7.4: 10 plug-n-play post templates

7.5: Treating your comments like Posts

7.6: I need a favor

7.7: Quiz

7.8: Activity: Create Your First Post

7.9: Activity: Write 3 comments like a Post

8.1: Your Online Persona

8.2: Choosing the Right Audience

8.3: Content Source

8.4: Leveraging FREE tools

8.5: Quiz

9.1: Why does a Portfolio Matter?

9.2: Creating Portfolio: Link tree, Notion, Lovable

9.3: Quiz

9.4: Activity: Build your first version of your online portfolio

10.1: Strategic networking online & offline

10.2: Public speaking opportunities that build visibility

10.3: Quiz

10.4: Activity: List 2 online / offline activities and achieve them in 3 months

11.1: How to use the Action Book

12.1: I don’t get any engagements in my posts, what should I do?

12.2: Is spending this much time and being active on LinkedIn worth it?

12.3: I’m not an expert—what do I even talk about?

12.4: Is LinkedIn Premium required to grow on LinkedIn?

12.5: Do I really need a personal brand if I’m not trying to become an influencer?

12.6: Isn’t LinkedIn just for job seekers? I’m not looking for a new job.

12.7: How long does it take before I start seeing results?

12.8: What if my current employer doesn’t like me being active on LinkedIn?

12.9: Can I build a brand if I’m a freelancer/consultant and not in a full-time role?

12.10: How do I create content when I don’t have time?

12.11: Is it necessary to create content, or can I build a brand just by engaging with others?

12.12: What should I do if I receive negative comments or criticism on my posts?

13.1: Final Quiz

2.1: Bootcamp Monthly Webinar - September 2025 NEW

2.2: Bootcamp Monthly Webinar - August 2025

2.3: Bootcamp Monthly Webinar - June & July 2025

3.1: Bootcamp Monthly Webinar - October 2025 NEW

3.2: Bootcamp Monthly Webinar - September 2025

3.3: DS Bootcamp Monthly Webinar - August 2025

4.1: Beginners Guide to Job Seeking - Sep 23

4.2: 6 Free Internet Tools to Get an Interview Call - Oct 23

4.3: Smart Job Assistance Portal & Expert Resume Insights

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

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

5.1: How to use Discord

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

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

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

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

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

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

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

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

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

6.10: Data Analytics Freelancing Essentials - Santhanalakshmi Ponnurasan

6.11: How to differentiate your work - Ashish Babaria

7.1: Sequence Learning with RNN: A Practical Approach to Retail Sales Forecasting NEW

7.2: Production-Ready RAG: Build, Evaluate & Scale AI Systems for Enterprises

7.3: How to Stand Out in the Job Market Through Open Source Contributions