Become a
Data Engineer
Industry-Taught.
Career-Ready.
Career-Ready.
Learn from Top Industry Experts and unlock your full potential?
Click the link below !
₹25,000| ₹ 30,000 Affordable. Practical. Powerful.

Become a
Data Engineer
Industry-Taught.
Career-Ready.
Career-Ready.
Learn from Top Industry Experts and unlock your full potential?
Click the link below !
₹25,000| ₹ 30,000 Affordable. Practical. Powerful.
Data Engineering Course Features
Why Choose Us?
Get tailored instruction through focused one-on-one sessions, ensuring you receive personalized support every step of the way.
Our curriculum is built with beginners in mind — no prior coding or analytics experience is required to start your journey.
Work on real-world use cases and scenarios that mirror actual industry challenges, preparing you with job-ready skills.
This course is crafted to help you transition smoothly into analytics roles, with a strong emphasis on employability and practical outcomes.
Course Curriculum
- What is Data Engineering?
- The Data Lifecycle: Ingestion, Storage, Processing, and Delivery.
- Roles and Responsibilities of a Data Engineer.
- Key Differences Between Data Engineers, Analysts, and Scientists.
- Overview of Tools & Technologies: Python, SQL, PySpark, AWS, MS Excel.
- Data cleaning and formatting.
- PivotTables and Pivot Charts.
- Lookup functions (VLOOKUP, XLOOKUP).
- Basic dashboards in Excel.
- Introduction to Databases and RDBMS
- Writing basic to advanced SQL queries
- Filtering, Sorting, and Aggregating Data
- Joins (INNER, LEFT, RIGHT, FULL)
- Subqueries, CTEs, and Window Functions
- Creating Views and Stored Procedures
- Python basics: variables, data types, control flow
- Working with libraries: NumPy, Pandas
- Data cleaning and wrangling
- Exploratory Data Analysis (EDA) with Pandas
- Data visualization with Matplotlib and Seaborn
- Handling missing data and outliers
- Simple use of APIs and JSON data
- Introduction to Spark & Architecture.
- Working with Databricks.
- Data Transformations with PySpark - Beginner, Intermediate, and Advanced.
- Data Ingestion and Schema Handling.
- Spark SQL Integration.
- Data Ingestion and Schema Handling.
- Introduction to AWS and its Services.
- Amazon S3 (Simple Storage Service).
- Amazon Redshift.
- AWS Glue.
- Amazon RDS.
- AWS Data Pipeline.
- Amazon EC2.
- Tackle a real-world data engineering problem.
- Build a full data pipeline: ingest → store → transform → load.
- Use tools like Spark and AWS services.
- Implement data quality checks and schema handling.
- Present the pipeline and architecture to a review panel.
- 1:1 Resume + LinkedIn Review Session.
- Interview Prep Bootcamp.
- Exclusive Masterclasses.
- Mock Interview with Feedback.
- Weekly Live Doubt Clearing & Mentor Support.
- Portfolio-Ready Capstone Project.
- Data Pipeline Template Pack.
- Progress Reports.
Course Curriculum
- What is Data Engineering?
- The Data Lifecycle: Ingestion, Storage, Processing, and Delivery.
- Roles and Responsibilities of a Data Engineer.
- Key Differences Between Data Engineers, Analysts, and Scientists.
- Overview of Tools & Technologies: Python, SQL, PySpark, AWS, MS Excel.
- Data cleaning and formatting.
- PivotTables and Pivot Charts.
- Lookup functions (VLOOKUP, XLOOKUP).
- Basic dashboards in Excel.
- Introduction to Databases and RDBMS
- Writing basic to advanced SQL queries
- Filtering, Sorting, and Aggregating Data
- Joins (INNER, LEFT, RIGHT, FULL)
- Subqueries, CTEs, and Window Functions
- Creating Views and Stored Procedures
- Python basics: variables, data types, control flow
- Working with libraries: NumPy, Pandas
- Data cleaning and wrangling
- Exploratory Data Analysis (EDA) with Pandas
- Data visualization with Matplotlib and Seaborn
- Handling missing data and outliers
- Simple use of APIs and JSON data
- Introduction to Spark & Architecture.
- Working with Databricks.
- Data Transformations with PySpark - Beginner, Intermediate, and Advanced.
- Data Ingestion and Schema Handling.
- Spark SQL Integration.
- Data Ingestion and Schema Handling.
- Introduction to AWS and its Services.
- Amazon S3 (Simple Storage Service).
- Amazon Redshift.
- AWS Glue.
- Amazon RDS.
- AWS Data Pipeline.
- Amazon EC2.
- Tackle a real-world data engineering problem.
- Build a full data pipeline: ingest → store → transform → load.
- Use tools like Spark and AWS services.
- Implement data quality checks and schema handling.
- Present the pipeline and architecture to a review panel.
- 1:1 Resume + LinkedIn Review Session.
- Interview Prep Bootcamp.
- Exclusive Masterclasses.
- Mock Interview with Feedback.
- Weekly Live Doubt Clearing & Mentor Support.
- Portfolio-Ready Capstone Project.
- Data Pipeline Template Pack.
- Progress Reports.
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Level-Up Lab
Interview Practice
Simulation
Simulated virtual interviews (Zoom, HireVue-style).
Interview Story Bank Creation
Categorize stories by skill, company value, or role.
Crisis Question Coaching
How to answer gaps, firings, career changes etc.
ATS Optimization
Role-Specific Customization
Create versions of the resume for different job types or industries.
Professional Formatting & Design
Resume Critique & Live Feedback
Entry-Level & Fresher Resume Creation
LinkedIn Revamp
Revamp your LinkedIn profile and give it a completely new look.
Headline Transformation for Visibility
Craft attention-grabbing, keyword-optimized headlines that showcase your expertise and make you stand out in searches.
Strategic Content & Engagement Plan
Connection Strategy for Networking
Networking Concierge
Help clients create and maintain a networking plan.
Job Offer Evaluation Session
Compare compensation, benefits, growth potential, and cultural fit.
Professional Communication Coaching
Focused on email etiquette, Slack culture, and workplace messaging.
Job Boards training
Learn how to make the most use of professional job boards.
Entrepreneurial Path Planning
Explore how to turn your skills into a solo business or side hustle.