3 Months Program
Data Engineering
Gain in-demand skills to collect, clean, and transform data with our comprehensive Data Engineering program. Ideal for career switchers and upskillers alike, this course covers SQL, Python, cloud platforms, modern data pipelines, and an introduction to AI-powered data workflows—preparing you to turn raw data into actionable insights.
12
Weeks program
100%
Hands-on learning
About Program
This Data Engineering program is designed to equip learners with the technical skills and tools needed to collect, clean, transform, and deliver data for analytics and machine learning purposes. In today’s data-driven world, businesses rely on Data Engineers to build the infrastructure and pipelines that turn raw data into valuable insights.
Whether you’re transitioning into tech or upskilling from a data-related field, this course will guide you through the foundational and advanced tools in the modern data stack leveraging SQL, Python, cloud platforms, data pipelines, and an introduction to AI for automating and optimizing data workflows.
What You Will Learn
By the end of the program, learners will be able to:
- Build ETL (Extract, Transform, Load) pipelines using Python
- Design and query relational databases using SQL
- Work with cloud data services (AWS/GCP)
- Understand data warehousing and dimensional modeling
- Apply basic machine learning models using Python and integrate them in data workflows
- Use Apache Airflow or similar tools for orchestrating pipelines
- Create dashboards and reports using Power BI
- Implement basic MLOps principles for AI deployment in data pipelines
3 Month Curriculum Breakdown (Frontend to Backend with Node.js)
Month 1: Foundation of Data Engineering
- Role of a Data Engineer
- Overview of Data Ecosystem
- Data Engineer vs Data Scientist vs Analyst
- Python Programming Basics (RECAP)
- Working with Pandas and Numpy
- File I/O, CSV, JSON, APIs
- Data Cleaning Techniques
- SQL Essentials (SELECT, WHERE, JOIN, GROUP BY)
- Database Design Concepts (Normalization, Keys)
- PostgreSQL / MySQL
- Project: Design and Query a Mini Database
- Introduction to Power BI
- Connecting to Data Sources
- Dashboard Design
- Data Modeling & DAX
- Project: Build a Reporting Dashboard
Month 2: Building Data Pipelines
- Concepts of ETL vs ELT
- Building ETL with Python
- File-based ETL to Databases
- Star and Snowflake Schema
- Data Lakes vs Warehouses
- Using BigQuery, Redshift, or Snowflake
- Dimensional Modeling
- DAGs and Workflow Management
- Scheduling and Monitoring Tasks
- Integrating ETL Jobs
- Overview of AWS/GCP
- Using S3 or Google Cloud Storage
- Cloud Data Transfers & Storage
- Cost Management & Scalability
Month 3: Introduction to AI & Capstone
- Supervised vs Unsupervised Learning
- Regression and Classification Basics
- Data Preprocessing for ML
- Building Models with Scikit-learn
- Model Versioning & Experiment Tracking
- Deploying Models using FastAPI
- Introduction to Model Monitoring
- Defining Use Case
- Data Collection & Cleaning
- Building Pipelines for Analysis
- End-to-End Pipeline from Raw Data to Dashboard or ML Model
- Final Presentation & Review
Final Project
Learners will build and present an end-to-end data pipeline integrating Python, SQL, Cloud Services, and a Machine Learning model. This will simulate a real-world data engineering project.
Table of content
Enroll For Data Engineering Internship
Join the next Volt Cohort starting First Monday of next Month. Application closes on the 26th of every Month.