IE 6700. Data Management for Analytics (4 Hours)

Course Outline:

1. Database Management for Data Analytics

• • Fundamental concepts of database management systems (DBMS)
  • Role of databases in supporting analytics, data mining, and machine learning

1. Database Design and Modeling

• • Entity-relationship (E-R) and object-oriented data modeling
  • User-centric information requirements and data sharing

1. Relational Databases and NoSQL Databases

• • Overview of relational and NoSQL database systems
  • Choosing the right database for specific applications

1. Data Integration and Quality

• • Techniques for integrating data from various sources
  • Ensuring data quality in analytics processes

1. Data Governance and Big Data

• • Evolving concepts in data governance
  • Handling and processing big data for analytics

1. Data Processing for Analytics

• • Techniques for efficient data processing
  • Overview of data warehousing

Assignments and Assessments:

• Database design projects
• Data integration and quality assessments
• Midterm exam
• Final project: Implementing a data management system for a specific analytics scenario

Lesson Summary

This course, IE 6700: Data Management for Analytics, focuses on fundamental concepts of database management systems (DBMS) and their role in supporting analytics, data mining, and machine learning. The course covers topics such as database design and modeling, relational databases and NoSQL databases, data integration and quality, data governance and big data, and data processing for analytics.

The course begins with an overview of database design and modeling, including entity-relationship (E-R) and object-oriented data modeling. It explores user-centric information requirements and data sharing, emphasizing the importance of designing databases that meet the needs of users and promote data sharing.

The course then delves into relational databases and NoSQL databases, providing an overview of both types of database systems. It discusses the factors to consider when choosing the right database for specific applications, helping students understand the strengths and limitations of each type of database.

Next, the course covers data integration and quality techniques. It teaches students how to integrate data from various sources and emphasizes the importance of ensuring data quality in analytics processes. Students will learn how to effectively manage and process data to support analytics tasks.

The course also addresses evolving concepts in data governance and the challenges of handling and processing big data for analytics. It explores the role of data governance in ensuring data integrity, security, and privacy. Students will gain an understanding of the unique considerations and techniques involved in managing big data for analytics purposes.

In addition to lectures and discussions, the course includes assignments and assessments to reinforce learning. Students will work on database design projects, participate in data integration and quality assessments, and take a midterm exam. The final project for the course involves implementing a data management system for a specific analytics scenario, allowing students to apply their knowledge and skills in a practical setting.