Database to AI: Practical Data Analytics Integration

Northeastern University via Coursera

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Overview

"Database to AI: Practical Data Analytics Integration" is a hands-on course designed for data professionals, analysts, and AI enthusiasts looking to bridge the gap between traditional database management and advanced AI-driven analytics. This course focuses on integrating structured data from databases with AI workflows to unlock deeper insights and predictive capabilities. You’ll begin by mastering techniques for extracting, transforming, and loading (ETL) data from various databases, including SQL and NoSQL systems, into AI-compatible formats. The course then dives into data preprocessing,...

Syllabus

Module 1: Fundamental Concepts of Database Management

In this module, you'll explore the foundational principles of database management, focusing on the differences between file-based and database approaches to data management. You will learn about the key elements of a database system and the advantages of using database management systems (DBMS) to organize, store, and manipulate data. Through this module, you'll develop skills in database design and administration, gaining a deeper understanding of how DBMS enhances data management and supports professional work in fields like data analytics.

Module 2: Architecture and Categorization of Database Management Systems (DBMSs)

This module covers the architecture and categorization of Database Management Systems (DBMS). Here, you will learn the key components of DBMS architecture, including the query processor and storage manager, and how they interact to manage data. You will also learn to categorize DBMSs based on factors like data models, architecture, and usage, highlighting their characteristics and real-world applications. This module also provides resources and prompts for discussion to deepen understanding of DBMS types and their use cases in data management.

Module 3: Conceptual Data Modeling, Part 1

In this module, you'll explore the foundational steps of database design, focusing on conceptual data modeling using the Entity Relationship (ER) model. You will learn how to gather business requirements, identify key entity and relationship types, and develop a conceptual data model. This model serves as the blueprint for database design before transitioning to logical and physical designs. You'll also examine the limitations of the ER model and how to address them. By the end of this module, you will understand how to translate real-world business processes into a clear, organized conceptual data model.

Module 4: Conceptual Data Modeling, Part 2

In this module we will learn three additional semantic data modeling concepts: specialization/generalization, categorization, and aggregation. These concepts enhance and extend the ER model discussed in the previous module. We will introduce an alternative conceptual model: the Unified Modeling Language (UML) class diagram. The UML is a modeling language that assists in the specification, visualization, construction, and documentation of artifacts of a software system. The UML can offer case diagrams, sequence diagrams, package diagrams, and deployment diagrams, etc. Here we use the UML for conceptual data modeling.