20th August 2023
Why to Join This Course
Join Our Data Engineer Today!
Ready to embark on a transformative learning journey? Don’t miss this opportunity to gain valuable skills and elevate your career. Enroll in our comprehensive course today and join a community of learners dedicated to excellence. Take the first step towards unlocking your full potential!
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Curriculum for Data Engineer
Module 1: Advanced Data Manipulation and ETL (10 hours)
- Overview of ETL processes
- Advanced data manipulation techniques
- Data validation and quality assurance
- Error handling and exception management
- Automation of ETL workflows
Module 2: Database Design and Management (10 hours)
- Database architecture and design principles
- Relational database management systems (RDBMS)
- Indexing and query optimization
- Data modeling and normalization
- Performance tuning and monitoring
Module 3: Big Data Technologies and Distributed Computing (10 hours)
- Introduction to big data technologies
- Distributed file systems (e.g., Hadoop Distributed File System)
- Big data processing frameworks (e.g., Apache Spark)
- Scalable data storage and retrieval
- Parallel processing and distributed computing
Module 4: Data Warehousing and Data Integration (10 hours)
- Data warehousing concepts and architecture
- Dimensional modeling and star schema design
- ETL for data integration and transformation
- Data staging and data mart development
- Metadata management and data lineage
For admission to this Professional Certificate course in Data Science Engineer Course, candidates should have:
- Basic Programming Knowledge
- Database Fundamentals
- Data Analytics Basics
- Mathematics and Statistics (recommended but not mandatory)
- Data Analysis Tools (e.g., Pandas, NumPy, SQL) (recommended but not mandatory)
Upon completion of this course, students will be able to:
- Perform advanced data manipulation and transformation using ETL techniques.
- Design and implement databases with optimal performance and scalability.
- Utilize big data technologies and distributed computing frameworks to process and analyze large-scale datasets.
- Develop strategies for data warehousing and integrating data from multiple sources.
- Create end-to-end data engineering solutions to meet business requirements.