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Data Engineer — Master’s Course
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Course Features

Duration Self-paced
Level Beginner
Language English
Mode Online
Data Engineering

Data Engineering Course Online — Python, ETL Pipelines & Cloud Data

Build the data infrastructure skills companies need most with this online data engineering master course. You will master Python and R for scripting, SQL for database management, and core data libraries including NumPy, Pandas, Matplotlib, Seaborn, and SciPy. The course covers data pipeline design, ETL/ELT workflows, data modelling, and machine learning integration with scikit-learn — equipping you for modern data engineering roles. Includes 1:1 mentorship and mock interview preparation.

Last updated December 2025
Next cohort starts Oct 1st
$1,200.00 $1,500.00
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Become a job-ready Data Engineer. Build and operate robust data platforms—batch and streaming—from ingestion to storage, transformation, governance, and analytics enablement.

  • Foundations: SDLC, Agile vs Waterfall; the data engineering role and core terminology
  • Programming: Python & R for pipelines and data ops; SQL for modeling and transformations
  • Tooling: PyCharm & Jupyter; NumPy/Pandas; visualization with Matplotlib/Seaborn
  • ML enablement: SciPy & scikit-learn workflows; PyTorch basics for model serving contexts
  • BI: Tableau, Power BI, and QlikView reporting for downstream stakeholders
  • Big Data: Hadoop (HDFS/MapReduce), Hive, Apache Spark (Core & Spark SQL)
  • ETL/Orchestration: Sqoop for data transfer and Apache Airflow for workflow management
  • Cloud: AWS data services incl. S3 and Redshift
  • Databases: SQL Server, PostgreSQL, MongoDB
  • Streaming: Apache Kafka for real-time pipelines
  • Ops & Quality: performance tuning, data quality, security, compliance & governance
  • Professional: Git/GitHub collaboration, MS Office reporting, and a real capstone project

Graduate with a capstone that ingests, processes, warehouses, and serves data to analytics—deployed and demo-ready.

1:1 Personalized Mentorship
Mock Interview Preparation
SDLC, Agile & Waterfall foundations
Python, R & SQL for data engineering
PyCharm & Jupyter workflows
NumPy, Pandas, Matplotlib, Seaborn
SciPy, scikit-learn & PyTorch (basics)
Tableau, Power BI & QlikView
Hadoop, HDFS & MapReduce
Apache Spark (Core & Spark SQL)
Hive data warehousing
ETL with Sqoop & orchestration with Airflow
AWS S3 & Redshift
SQL Server, PostgreSQL & MongoDB
Apache Kafka streaming
Data cleaning & feature engineering
Git & GitHub collaboration
Performance tuning & resource management
Security, governance & compliance
MS Office/Excel reporting
Capstone project
Overview of Data Engineering
Roles & Responsibilities
Importance in Business
Key Concepts & Terminologies
Software Development Life Cycle (SDLC)
Phases of SDLC & Data Projects
Principles of Agile & Agile in Data
Waterfall Phases & Comparison with Agile
Python: Basic Syntax & Structures
Python: NumPy & Pandas for Data Manipulation
R: Basics & Data Manipulation
SQL: Queries & Advanced Techniques
PyCharm: Setup, Write & Debug
Jupyter: Interactive Analysis & Visualization
NumPy Array Operations
Pandas DataFrame Manipulation
Matplotlib Core Plots
Seaborn Advanced Visualizations
SciPy for Scientific Computing
scikit-learn ML Workflows
PyTorch: Intro & Model Training Basics
Tableau: Interactive Dashboards
Power BI: Reports & Dashboards
QlikView: Visualization & Reporting
Visualization Best Practices
Hadoop Architecture & Components
HDFS & MapReduce
Hive: Warehousing Concepts
Apache Spark Overview
Spark Core: RDDs & DataFrames
Transformations & Actions
Spark SQL: Writing Queries
Integrating Spark with Hive
ETL Concepts & Patterns
Sqoop: Import/Export between Hadoop & RDBMS
Apache Airflow: DAGs & Scheduling
AWS: Accounts & Resource Setup
S3 for Data Storage & Ingestion
Redshift for Data Warehousing
SQL Server (SSMS): Setup & Advanced SQL
PostgreSQL: Setup & Advanced SQL
MongoDB: NoSQL Concepts & CRUD
Data Warehouse Architecture
ETL Processes for Warehousing
Serving Data to BI & Analytics
Batch vs Stream Processing
Kafka: Topics, Producers, Consumers
Building Real-Time Pipelines with Kafka
Handling Missing Values
Transformations & Standardization
Feature Engineering: Scaling & Normalization
Git: Commands & Concepts
Branching & Merging
GitHub: Repos, PRs & Reviews
Indexing & Query Optimization
Performance Tuning for ETL
Spark Resource Management
Data Security Best Practices
Encryption & Data Masking
Data Quality Management
Governance & Compliance (GDPR/HIPAA)
MS Office for Documentation & Reporting
Excel: Advanced Techniques & Analysis
Defining Scope & Architecture
Building & Orchestrating Pipelines
Warehousing & Serving Data
Deployment, Demo & Outcomes
Project: Capstone: End-to-End Data Platform