A 9-Step Roadmap to Data engineer Certificate
This course provides a comprehensive introduction to Structured Query Language (SQL), the standard language for relational database management and data manipulation. Participants will learn how to create, query, and manage databases using SQL commands. The course covers essential topics including data definition (DDL), data manipulation (DML), filtering and sorting data, using aggregate functions, joins, subqueries, and views. By the end of the course, students will be able to write complex queries, optimize performance, and interact confidently with relational databases.
The course provides a comprehensive and advanced overview of database systems, blending both theoretical foundations and practical applications. It begins by introducing the importance of databases as a solution to traditional file-based systems, highlighting the role of DBMS in organizing, storing, and retrieving data consistently. The course then delves into data modeling, covering Entity-Relationship Diagrams (ERDs) and Enhanced ERDs (EERDs), explaining key concepts like entities, attributes, relationships, supertypes and subtypes, generalization, specialization, disjoint and overlapping constraints, with real-world examples such as university systems and sports leagues. It guides students through converting ERDs into relational databases using keys (primary, foreign, composite) and different relationship types (1:1, 1:M, M:N). It then teaches normalization up to the Boyce-Codd Normal Form (BCNF), addressing data redundancy and anomalies (insertion, deletion, update). The course expands into enterprise-level data modeling, introducing enterprise data models, functional decomposition, and two main system development approaches: SDLC and prototyping. It also highlights roles of key personnel like analysts, data modelers, and DB engineers. Advanced topics include Data Warehousing: understanding OLAP vs. OLTP, ETL processes, fact and dimension tables, star/snowflake/galaxy schemas, and data marts. Finally, the course addresses ethical data practices such as privacy, transparency, consent, non-discrimination, and secure data handling through data governance, quality management, and data catalogs. In short, the course equips learners with the skills to design, implement, and ethically manage powerful, scalable, and trustworthy data systems.
This course is a beginner-friendly introduction to Power BI, Microsoft’s powerful data analytics and visualization tool. Designed for students, graduates, and professionals from all fields, the course focuses on transforming raw data into meaningful insights through interactive dashboards and dynamic reports. Learners will explore the Power BI interface, perform data cleaning with Power Query, build data models, and use DAX (Data Analysis Expressions) for advanced calculations. By the end of the course, participants will be able to design and publish professional dashboards to support business decisions across various domains, including marketing, finance, HR, and education. Whether you're aiming to enhance your career prospects or strengthen your data skills, this course provides the essential tools to thrive in the world of data analysis.
This course provides a comprehensive introduction to Structured Query Language (SQL), the standard language for relational database management and data manipulation. Participants will learn how to create, query, and manage databases using SQL commands. The course covers essential topics including data definition (DDL), data manipulation (DML), filtering and sorting data, using aggregate functions, joins, subqueries, and views. By the end of the course, students will be able to write complex queries, optimize performance, and interact confidently with relational databases.
This comprehensive course introduces learners to ETL (Extract, Transform, Load) processes using Informatica PowerCenter, a leading data integration and transformation tool. Through hands-on labs and real-world use cases, participants will learn how to design, build, and manage robust ETL workflows that enable efficient data movement across systems. The course covers core concepts such as source and target definitions, mappings, transformations, workflow management, error handling, performance tuning, and best practices for enterprise-level ETL. By the end of this course, learners will be able to: Understand ETL architecture and the role of Informatica PowerCenter in data integration. Design and implement mappings using various transformation techniques. Create and schedule workflows to automate data pipelines. Troubleshoot and optimize ETL jobs for better performance. Integrate data from multiple sources including flat files, databases, and cloud systems. Whether you're aspiring to become a data engineer or working on enterprise data warehousing projects, this course equips you with the essential skills to work confidently with Informatica PowerCenter.
This course provides a practical and in-depth introduction to ETL (Extract, Transform, Load) using Microsoft SQL Server Integration Services (SSIS)—a powerful tool for data integration, transformation, and workflow automation within the Microsoft BI stack. Learners will explore the SSIS architecture, build data flow tasks, apply transformations, handle errors, and deploy ETL packages in real-world scenarios. With hands-on labs and guided projects, participants will learn how to design dynamic ETL processes that move and cleanse data across various sources and targets including SQL databases, Excel, flat files, and cloud platforms. By the end of this course, participants will: Understand SSIS architecture and components. Develop ETL solutions using Data Flow, Control Flow, and Transformations. Handle errors, logging, and debugging in SSIS packages. Schedule and deploy SSIS packages using SQL Server Agent. Optimize performance and apply best practices for enterprise data pipelines. Ideal for aspiring data engineers, BI developers, or anyone looking to master data movement and transformation within the Microsoft ecosystem.
This course offers a comprehensive introduction to Data Warehousing, tailored for learners aiming to build a strong foundation in data architecture and analytics systems. Starting with the basics, such as the purpose and lifecycle of DWH, the course gradually explores key distinctions between OLTP and OLAP systems, the difference between databases, data warehouses, and data lakes, and how data marts contribute to business intelligence. The curriculum dives into core concepts like schemas (star and snowflake), fact and dimension tables—including confirmed, junk, and role-playing dimensions—and elaborates on types of fact tables and Slowly Changing Dimensions (SCD). Additionally, learners are introduced to different modeling methodologies (Kimball vs. Inmon) and the strategic role of dimensional modeling in decision-making processes. By the end of this course, students will not only grasp the theoretical constructs of Data Warehousing but will also understand practical design approaches, enabling them to support large-scale data-driven environments effectively.
This hands-on course provides a practical introduction to Tableau, one of the leading tools in the field of data visualization and business intelligence. Designed for beginners, the course walks learners through the entire journey—from launching Tableau and understanding dimensions and measures, to building complex dashboards and sharing interactive visual stories. You'll begin by learning the fundamentals of the Tableau interface, followed by how to create basic visualizations, filter and group data, and use calculated fields. As you progress, you’ll explore advanced features like dashboard creation, publishing, and real-world design tips. The course concludes with a mini-project to apply all your acquired skills in a real-world scenario. By the end of this course, you’ll have the confidence and knowledge to: Navigate Tableau effectively. Create interactive visualizations and dashboards. Transform raw data into insightful visual stories. Share your insights with others professionally. This course is ideal for analysts, students, business users, or anyone interested in visual storytelling through data.