Course Description

Course Overview

The Data Management course provides a comprehensive understanding of the principles and practices involved in effectively managing and organizing data. The course covers various aspects of data management, including data storage, data integration, data quality, and data governance. Students will learn how to design and implement data management strategies, leverage database systems, and ensure the integrity and security of data.

Prerequisites

• Basic understanding of databases and SQL
• Familiarity with data modeling concepts
• Some programming skills (preferred but not mandatory)
• Curiosity and interest in data management practices

Methodology

The course adopts a combination of theoretical lectures, hands-on exercises, and practical labs to provide a comprehensive learning experience. The lectures cover the fundamental concepts and methodologies of data management. The hands-on exercises allow students to apply the learned techniques in real-world scenarios, while the labs provide opportunities to work on data management projects. The course encourages critical thinking, problem-solving skills, and an understanding of data governance principles.

Course Outline

Introduction to Data Management

Overview of data management principles and practices

Importance of data quality and data governance

Ethical considerations in data management

Data Modeling and Design

Entity-relationship (ER) modeling and normalization

Conceptual, logical, and physical data models

Design considerations for relational databases

Database Systems and Technologies

Relational database management systems (RDBMS)

NoSQL databases and their use cases

Introduction to cloud-based databases

Data Integration and ETL Processes

Data integration techniques (e.g., extract, transform, load)

Data migration and synchronization

ETL tools and frameworks

Data Quality and Master Data Management

Data quality assessment and improvement strategies

Data profiling and cleansing techniques

Master data management concepts and practices

Data Security and Privacy

Data security principles and best practices

Access control and user authentication

Compliance with data privacy regulations

Data Governance and Metadata Management

Data governance frameworks and practices

Establishing data governance policies and procedures

Metadata management and data lineage

Big Data Management

Introduction to big data and its challenges

Hadoop ecosystem and distributed file systems

Data management techniques for big data analytics

Outcome

Upon completing the course, students will:

• Understand the fundamental principles, practices, and methodologies of data management.
• Be proficient in data modeling, database systems, and SQL query languages.
• Gain hands-on experience with database management systems and data integration tools.
• Develop skills in ensuring data quality, data security, and data privacy.
• Understand the principles of data governance and metadata management.
• Apply data management techniques to handle structured and unstructured data effectively.

Labs

The course includes hands-on labs and projects to reinforce the theoretical concepts and provide practical experience. The labs may include:

• Designing and implementing a relational database schema for a given scenario.
• Writing SQL queries to retrieve and manipulate data from a database.
• Building an ETL pipeline to integrate and transform data from multiple sources.
• Performing data profiling and cleansing tasks on real-world datasets.
• Implementing data security measures (e.g., user authentication, access control) in a database system.
• Designing a data governance framework and establishing data governance policies.