Data Engineering Bootcamp: With Practical Job Placement Support & Virtual Internship
Industry Projects-Based Bootcamp Created by Hiring Managers
Prepare for the emerging role in the tech world - "The Data Engineer" with our Data Engineering Bootcamp (trusted by a community of 1M+ learners). Created by hiring managers, this hands-on program teaches real-world skills using modern tools available and trending in the world of Data Engineering. With practical job support integrated, you'll gain the confidence to successfully launch your Date Engineering career.
Bi-Weekly
Problem Solving Sessions*
5+
Business Projects
1
Virtual Internship
Free
Portfolio Website
Lifetime
Content Validity
0
Enrolled Learners
Join our Bootcamp
Learn In-demand Skills
Get Hired by Top Companies
Bi-Weekly
Problem Solving Sessions*
5+
Business Projects
1
Virtual Internship
Free
Portfolio Website
Lifetime
Content Validity
Why this is the most effective Data Engineering Bootcamp?
-
Unlimited daily doubt clearance support via private Discord community
-
Practical job assistance (Resume & Interview Preparation + Interview Leads + Building Online Credibility)
Hear It From
Our Happy Learners
Our content is rated 4.9/5 from 16576+ Learners
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SQL
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Python
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Being from a non-tech background and I know only economics and statistics, I feel happy because these industry experts made our life so easy. From the bottom of my heart, I congratulate Mr Dhaval Patel for introducing this fantastic course.
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Landed a Job
To be honest, this is the first ever course in my life that I completed 100%(bcuz of quality and the interest that Dhaval Patel brings up through out the course). I brought many courses in Udemy but I never crossed 50% in any of them.
I am currently working as a Data Analyst in top Logistics organization, and this course have gave me much needed and valuable knowledge on SQL. I already recommended [codebasics.io](http://codebasics.io/) (SQL course) to many people who reached out to me when I was half way through this course and will continue to highly recommend this unique master piece.
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The "Python For Beginner and Intermediate Learners"
course was one of the best educational experiences I've had. The course was well-structured. The course covered a wide range of topics and the material was presented in a clear and concise manner.
One of the things I appreciated most about the course was the emphasis on hands-on learning.
Overall, I would highly recommend this course to anyone interested in learning Python . Whether you're a beginner or have some experience in the field, the course offers valuable insights and practical skills that are applicable in a variety of contexts.
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Landed a Job
Amazing course, I was always afraid of writing a query, especially in joins before this course. Now I feel very confident in sql queries. Learned a lot in a very organized way and the simulation of Peter Panday and the way you taught us really is decent in this price.
Thank You Sir...
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The course is very well structured and simply explained. I practiced coding along with the lecturer and it was at a comfortable speed. Nothing was rushed into. I have studied python language earlier, but this course is better than most YouTube content because of the quality content and comfortable speed at which it is taught. The quizzes are fun. The practice exercises were precise.
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Overview
What you'll learn in
this Data Engineering Bootcamp
SQL Beginner to Advanced For Data Professionals
11h:15m:16s on-demand video
|
84 Lectures
5:
SQL Basics: Data Retrieval - Single Table
15 Lectures
-
5.1: Install MySQL: Windows
Free -
5.2: Install MySQL: Linux, Mac
Free -
5.3: Import Movies Dataset in MySQL
Free -
5.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.10: HAVING Clause
Free -
5.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.13: The Data God’s Blessing
Free -
5.14: Quiz
-
5.15: Chapter Summary
8:
SQL Basics: Database Creation & Updates
18 Lectures
-
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:
AtliQ Hardware & Problem Statement
9 Lectures
-
9.1: The Rise of Databases at AtliQ
Free -
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:
SQL Advanced: Finance Analytics
10 Lectures
-
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:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
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:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
12.1: Supply Chain Basics : Simplified
-
12.2: Problem Statement
-
12.3: Create a Helper Table
-
12.4: Database Triggers
-
12.5: Database Events
-
12.6: Temporary Tables & Forecast Accuracy Report
-
12.7: Exercise: CTE, Temporary Tables
-
12.8: Subquery vs CTE vs Views vs Temporary Table
-
12.9: User Accounts and Privileges
-
12.10: Database Indexes: Overview
-
12.11: Database Indexes: Composite Index
-
12.12: Database Indexes: Index Types
-
12.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
12.14: Quiz
Python: Beginner to Advanced For Data Professionals
16h:57m:31s on-demand video
|
108 Lectures
2:
Welcome to the Python Experience
7 Lectures
-
2.1: Course Introduction
Free -
2.2: What Benefits You Get By Learning Python?
Free -
2.3: Why Python is so Popular?
Free -
2.4: Who can Enroll In This Course? Any Prerequisites?
Free -
2.5: How is This Different From Free Codebasics YouTube Playlist?
Free -
2.6: What Kind Of Computer Do I Need For This Course?
Free -
2.7: Course Content Overview
Free
8:
Python Basics: Functions, Dictionaries, Tuples and File Handling
9 Lectures
-
8.1: Functions
-
8.2: Dictionary and Tuples
-
8.3: Modules and Pip
-
8.4: File Handling
-
8.5: Quiz: Functions, Dictionaries, Tuples and File Handling
-
8.6: Peter’s Request to Tony
-
8.7: Exercise: Functions, Dictionaries, Tuples and File Handling
-
8.8: Two Deadly Viruses Infecting Learners
-
8.9: Chapter Summary
17:
Project 2: Expense Tracking System
11 Lectures
-
17.1: Problem Statement & Tech Architecture
-
17.2: Database CRUD Operations
-
17.3: Automated Tests Setup for CRUD
-
17.4: Expense Management: Backend (FastAPI)
-
17.5: Expense Management: Logging
-
17.6: Streamlit Introduction
-
17.7: Expense Management: Frontend (Streamlit)
-
17.8: Analytics: Backend (FastAPI)
-
17.9: Analytics: Frontend (Streamlit)
-
17.10: README and Requirements.txt
-
17.11: Exercise
22:
Bonus Medical Data Extraction Project: Prescription Document
10 Lectures
-
22.1: Technical Architecture of the Project
Free -
22.2: Installation of Necessary Libraries
-
22.3: Extract text from a pdf document
-
22.4: Thresholding in OpenCV
-
22.5: Regular Expressions or Regex
-
22.6: Regex Exercise
-
22.7: Python class for prescription
-
22.8: Code Refactoring
-
22.9: Unit Tests using pytest
-
22.10: I Need a Favour
Data Engineering Basics
00:00 on-demand video
|
0 Lectures
SQL Beginner to Advanced For Data Professionals
11h:15m:16s on-demand video
|
84
Lectures
11h:15m:16s on-demand video
|
84 Lectures
5:
SQL Basics: Data Retrieval - Single Table
15 Lectures
-
5.1: Install MySQL: Windows
Free -
5.2: Install MySQL: Linux, Mac
Free -
5.3: Import Movies Dataset in MySQL
Free -
5.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.10: HAVING Clause
Free -
5.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.13: The Data God’s Blessing
Free -
5.14: Quiz
-
5.15: Chapter Summary
8:
SQL Basics: Database Creation & Updates
18 Lectures
-
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:
AtliQ Hardware & Problem Statement
9 Lectures
-
9.1: The Rise of Databases at AtliQ
Free -
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:
SQL Advanced: Finance Analytics
10 Lectures
-
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:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
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:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
12.1: Supply Chain Basics : Simplified
-
12.2: Problem Statement
-
12.3: Create a Helper Table
-
12.4: Database Triggers
-
12.5: Database Events
-
12.6: Temporary Tables & Forecast Accuracy Report
-
12.7: Exercise: CTE, Temporary Tables
-
12.8: Subquery vs CTE vs Views vs Temporary Table
-
12.9: User Accounts and Privileges
-
12.10: Database Indexes: Overview
-
12.11: Database Indexes: Composite Index
-
12.12: Database Indexes: Index Types
-
12.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
12.14: Quiz
Python: Beginner to Advanced For Data Professionals
16h:57m:31s on-demand video
|
108
Lectures
16h:57m:31s on-demand video
|
108 Lectures
2:
Welcome to the Python Experience
7 Lectures
-
2.1: Course Introduction
Free -
2.2: What Benefits You Get By Learning Python?
Free -
2.3: Why Python is so Popular?
Free -
2.4: Who can Enroll In This Course? Any Prerequisites?
Free -
2.5: How is This Different From Free Codebasics YouTube Playlist?
Free -
2.6: What Kind Of Computer Do I Need For This Course?
Free -
2.7: Course Content Overview
Free
8:
Python Basics: Functions, Dictionaries, Tuples and File Handling
9 Lectures
-
8.1: Functions
-
8.2: Dictionary and Tuples
-
8.3: Modules and Pip
-
8.4: File Handling
-
8.5: Quiz: Functions, Dictionaries, Tuples and File Handling
-
8.6: Peter’s Request to Tony
-
8.7: Exercise: Functions, Dictionaries, Tuples and File Handling
-
8.8: Two Deadly Viruses Infecting Learners
-
8.9: Chapter Summary
17:
Project 2: Expense Tracking System
11 Lectures
-
17.1: Problem Statement & Tech Architecture
-
17.2: Database CRUD Operations
-
17.3: Automated Tests Setup for CRUD
-
17.4: Expense Management: Backend (FastAPI)
-
17.5: Expense Management: Logging
-
17.6: Streamlit Introduction
-
17.7: Expense Management: Frontend (Streamlit)
-
17.8: Analytics: Backend (FastAPI)
-
17.9: Analytics: Frontend (Streamlit)
-
17.10: README and Requirements.txt
-
17.11: Exercise
22:
Bonus Medical Data Extraction Project: Prescription Document
10 Lectures
-
22.1: Technical Architecture of the Project
Free -
22.2: Installation of Necessary Libraries
-
22.3: Extract text from a pdf document
-
22.4: Thresholding in OpenCV
-
22.5: Regular Expressions or Regex
-
22.6: Regex Exercise
-
22.7: Python class for prescription
-
22.8: Code Refactoring
-
22.9: Unit Tests using pytest
-
22.10: I Need a Favour
Data Engineering Basics
00:00 on-demand video
|
0
Lectures
00:00 on-demand video
|
0 Lectures
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