EPISODE 0: Module Introduction [Slide 1 — Title] Welcome to Data Warehousing and Business Intelligence. This course takes you from data warehouse architecture and dimensional modeling, through data mining, OLAP analysis, and BI dashboards, with tutorials and practicals throughout. This course is presented by Sandaruwan Herath. Over the next twelve sessions we will follow one story: how raw business data becomes a decision someone can act on. Let's begin. [Slide 3 — What this module is about] Data warehousing and data mining are two powerful, complementary techniques for storing and analyzing data. Data warehousing is about compiling and organizing data in a common database. Data mining is the process of extracting important patterns from that data. Notice the dependency: data mining relies on the data warehouse to identify patterns and draw relevant conclusions. The diagram shows three parts working together. The data warehouse collects, cleans, integrates, and stores. Data mining and OLAP discover patterns, classify, cluster, and associate. Business intelligence delivers dashboards and reports so people make better decisions. The module focuses on four things: data warehouse concepts and application design, data mining theories and methods, business intelligence concepts, and hands-on design using the Microsoft BI and Power BI platforms and Oracle BI. [Slide 4 — Learning outcomes] There are four learning outcomes. LO1 is data warehousing. You will explain data warehouse architecture, techniques and usage; apply Slowly Changing Dimension techniques; design ETL processes and perform ETL testing; and apply data cleansing techniques while explaining why de-normalization matters. LO2 covers data mining foundations. You will explain what data mining is and why it is used; describe the Knowledge Discovery in Databases process, KDD, and its steps; distinguish OLAP and OLTP workloads; and explain the basic principles of data mining methods. LO3 is tools and BI practice. You will use mining tools and techniques on real datasets; develop dashboards and reports with Power BI, Oracle BI or Tableau; and apply Business Intelligence in organizational contexts. LO4 is design and evaluation. You will design data warehousing and data mining systems for real-world applications; weigh the pros and cons of data warehouse and data mining models; and discuss new trends, including Microsoft Azure-based data warehouse implementation. [Slide 5 — Course map] Here is the course map: twelve sessions of four hours each. Session 1 is the introduction to data warehousing and the evolution of decision support. Session 2 covers data warehouse architecture: OLTP, OLAP and ETL. Session 3 is data stores and storage design. Session 4 is business intelligence and analytics. These are all lectures, linked to LO1, except Session 4, which links to LO3. Session 5 is data warehouse design and dimensional modeling part one, covering facts and grain, as lecture plus lab. Session 6 is dim
EPISODE 0: Module Introduction [Slide 1 — Title] Welcome to Data Warehousing and Business Intelligence. This course takes you from data warehouse architecture and dimensional modeling, through data mining, OLAP analysis, and BI dashboards, with tutorials and practicals throughout. This course is presented by Sandaruwan Herath. Over the next twelve sessions we will follow one story: how raw business data becomes a decision someone can act on. Let's begin. [Slide 3 — What this module is about] Data warehousing and data mining are two powerful, complementary techniques for storing and analyzing data. Data warehousing is about compiling and organizing data in a common database. Data mining is the process of extracting important patterns from that data. Notice the dependency: data mining relies on the data warehouse to identify patterns and draw relevant conclusions. The diagram shows three parts working together. The data warehouse collects, cleans, integrates, and stores. Data mining and OLAP discover patterns, classify, cluster, and associate. Business intelligence delivers dashboards and reports so people make better decisions. The module focuses on four things: data warehouse concepts and application design, data mining theories and methods, business intelligence concepts, and hands-on design using the Microsoft BI and Power BI platforms and Oracle BI. [Slide 4 — Learning outcomes] There are four learning outcomes. LO1 is data warehousing. You will explain data warehouse architecture, techniques and usage; apply Slowly Changing Dimension techniques; design ETL processes and perform ETL testing; and apply data cleansing techniques while explaining why de-normalization matters. LO2 covers data mining foundations. You will explain what data mining is and why it is used; describe the Knowledge Discovery in Databases process, KDD, and its steps; distinguish OLAP and OLTP workloads; and explain the basic principles of data mining methods. LO3 is tools and BI practice. You will use mining tools and techniques on real datasets; develop dashboards and reports with Power BI, Oracle BI or Tableau; and apply Business Intelligence in organizational contexts. LO4 is design and evaluation. You will design data warehousing and data mining systems for real-world applications; weigh the pros and cons of data warehouse and data mining models; and discuss new trends, including Microsoft Azure-based data warehouse implementation. [Slide 5 — Course map] Here is the course map: twelve sessions of four hours each. Session 1 is the introduction to data warehousing and the evolution of decision support. Session 2 covers data warehouse architecture: OLTP, OLAP and ETL. Session 3 is data stores and storage design. Session 4 is business intelligence and analytics. These are all lectures, linked to LO1, except Session 4, which links to LO3. Session 5 is data warehouse design and dimensional modeling part one, covering facts and grain, as lecture plus lab. Session 6 is dim
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This course guides you from raw data to informed decisions, covering essential topics like data warehousing, mining, and business intelligence (BI). You'll build core capabilities in architecture, ETL processes, and data cleansing, while exploring key concepts such as KDD and OLAP. The program includes practical applications, allowing you to design and evaluate real systems over 12 sessions, utilizing platforms like Power BI, Oracle BI, and Azure.