Course Overview:
Python & SQL Based Data Engineering & Analytics is designed to train technical freshers (B.Tech/MCA / BCA) in industry-oriented Data Engineering and Data Analytics. The program focuses on practical implementation of data pipelines, database engineering, ETL workflows, and business intelligence systems aligned with current market requirements.
Students will gain hands-on experience in data processing, warehouse design, reporting systems, and analytics dashboards, while understanding how modern AI systems depend on structured data pipelines.
Training Duration
The industry-oriented training program consists of 150 hours of comprehensive learning.
The training is conducted in a hybrid format, allowing students to join either online or in person, focusing on hands-on implementation and real-world use cases.
Course Fee
₹ 30,500
Limited time offer
Course Structure
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book_2 Module 1: Python Programming for Data Engineering
- Python syntax, structured programming, and modular coding with functions.
- Object-Oriented Programming (OOP) fundamentals and Data structures.
- File handling (CSV, JSON), exception handling, and logging.
- Working with APIs for data ingestion and writing clean code.
- Data Libraries: Pandas, NumPy, and Matplotlib.
- Hands-on: Data cleaning, preprocessing, and automating scripts.
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book_2 Module 2: SQL & Relational Database Engineering
- Relational database fundamentals, architecture, and ER diagram design.
- Normalization techniques, transactions, and data integrity.
- Advanced SQL: Joins, Subqueries, Window functions, and Aggregations.
- Indexing, query optimization, and execution plans.
- Practical Implementation: Designing reporting databases and analytical queries.
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book_2 Module 3: Data Processing & Database Integration
- Connecting Python with relational databases to execute queries.
- Automating ETL scripts and batch data processing techniques.
- Data validation rules and error handling in workflows.
- Mini Project: Automated Data Processing System (File/API → Transform → Database).
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book_2 Module 4: Data Warehousing & ETL Fundamentals
- OLTP vs OLAP systems and Data Warehouse architecture.
- Star and Snowflake schemas, Fact and Dimension tables.
- ETL lifecycle, workflow design, and data quality strategies.
- Introduction and demonstration of Apache Airflow.
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book_2 Module 5: Data Analytics & Business Intelligence
- Exploratory Data Analysis (EDA) and KPI design.
- Data storytelling and visualization best practices.
- Power BI dashboard development.
- Projects: Sales, Marketing, and HR Analytics Dashboards.
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book_2 Module 6: Data Engineering in AI Systems
- Role of data pipelines and training data lifecycle in AI.
- Feature engineering and handling structured vs unstructured data.
- Overview of RAG architecture, Embeddings, and Vectorization.
- Conceptual exposure to OpenAI API and LangChain.
Capstone Project (Industry-Focused)
Students will design and implement an end-to-end Data Engineering & Analytics system: Data Ingestion → ETL → Data Warehouse → Reporting Queries → Business Dashboard.
Certification as Trainee Data Engineer:
On successful completion of the course, students get certified as Junior Data Engineer / Data Analyst, jointly by Ejobindia and the development firm Sysalgo Technologies.
