Syllabus for a Business Intelligence Course

 
 
I had the chance to give the course “Business Intelligence” to a few students in the Department of Political Science at Aalborg University during the second half of 2016. Running the course for non-computer-science students has been a good learning process for me and I came to wonder what business intelligence should be like for anyone who never had a course about it.
Indeed, an introductory course of business intelligence can align people from different educational and organizational background into the same common sense about BI and the same pool of terminology, concepts and technologies.
 
The purpose of this blog is to share the syllabus, my observations as well as learnings from the BI course. I wish to get comments, suggestions and helps so that I can make the course even better in the future.
 
My BI course uses “Business Intelligence Guide Book: From Data Integration to Analytics” by Rick Sherman as the primary textbook. I like this book as it covers about 70 percent of my teaching plan. And my students like it because it is also at a reasonable price. The syllabus extends from this textbook to new areas in the BI and Information Management industry over the past 5 years, such as self-service business intelligence, big data, data science, and cloud computing.
 
The course includes 10 separate modules where each module contains 2 hours of teaching and 1.5 hours of exercises. To make the course more interactive and productive, I made quite a few demo sessions using tools like Excel, Power BI, R, Python, Integration Services (SSIS), Analysis Services (SSAS), Toad Data Modeler, Data Cleaner and Azure Machine Learning Studio. The students also got hands-on exercises using these tools.
 
Details of each module are listed in the next.
  • Module 1: Overview of BI Concepts: This module introduces the concept of business intelligence and describes the concept of BI system. Following the categories defined by Wayne Eckerson, the module describes and demonstrates different types of BI applications with using Excel, SQL Server Reporting Services, Power BI, and Tableau.
  • Module 2: Art of Front-end. The module is focused on the design of BI “front-end” applications. After exploring principles, examples and exercises in the design of reports, dashboards, and scorecards, the course elaborates on how BI standardization can help to improve the quality of BI development and brings a discussion of how to choose the right tool for the right group of users.
  • Module 3: Architecture and Process. This module introduces the classical data warehouse and BI architecture frameworks and then presents the BI development process based on the Kimball’s project approach. Here both Inmon’s CIF and Kimball’s BUS architectures are presented with views of the Pro’s and Con’s.
  • Module 4: Governance. This module introduces the organizational aspects of BI applications and how a governance model can influence the success of BI applications. Starting from different implementation models of BI, the lesson goes into deeper discussion on different organization practices around BI. The TDWI BI maturity model is presented to summarize the result of different organization implementation in BI. The lesson then shifts focus to data governance and introduces five key processes in data governance, namely metadata, data quality, data model, data stewardship and master data. A few cases in the real world scenario brings a discussion to the lesson regarding how to ensure the continuous delivery of BI application in different type of organization structures.
  • Module 5: Understanding Data. This module is a deep dive into data and data models. Starting from different formats and forms of data in IT systems, the lesson elaborates on the concept and practices in entity-relationship data modeling. Universal data models in the healthcare and financial industry as well as a generic model of people and organization data are introduced to facilitate the understanding and usage of data model in actual scenarios. Then the lesson puts focus on the dimensional modeling techniques. The lesson completes with an introduction of OLAP cube models and other types of data models in BI applications. This module includes exercises using Toad Data Modeler to read and create simple E/R and dimensional data models.
  • Module 6: Data Processing. This module introduces the data integration technique and the concept of ETL in the BI applications. The lesson starts with an introduction of industry standard ETL tools, followed by a description of the processes that prepares the development of ETL programs. Then the lesson makes a deep dive into the actual development, deployment and maintenance of ETL jobs. The module includes exercises of creating both simple extraction and advanced dimension and fact load using SSIS.
  • Module 7: Self-service BI. This module introduces the concept of self-service business intelligence and how self-service BI can help an organization compared to the traditional BI process. The first part of the lesson starts with the definition of self-service BI, the tools, process and requirements for BI self-service. The lesson will lead into discussions and debates on the following topics. 1) What is the pre-requisite for free-style business exploration, 2) What is the role of IT in self-service BI, 3) When not to use self-service BI.  The lesson includes quite a few hands-on exercises with Excel, SQL Server Management Studio and Power BI.
  • Module 8: Big Data and Cloud. The module introduces the concept of big data, cloud computing and how they are connected to BI and business analytics. The lesson starts with the definition and justification of big data. The core technology in big data, namely Hadoop, Spark and other relevant concepts are then introduced. The second part of the lesson is focused on cloud computing and the essential features of cloud. The module includes both demo and hands-on exercises with Azure platform.
  • Module 9: Data Science. The module introduces the concept, process and typical tools in data science. Example of different algorithms, such as segmentation, classification, validation, regressions, recommendations, are presented with demos. The module includes hands-on exercises using Excel and R to work on histograms, regression, clustering and text analysis. The lesson completes with a discussion on how the algorithms and code in data science is set into production environment and how the maintenance process should be.
  • Module 10: Walk through a BI Lifecycle. This module sums up all the previous lectures based on a mock-up case. By following the standard project development process, the lesson demonstrates the whole lifecycle of how data, started from the different source format, is analyzed, modeled, transformed, presented, maintained, owned and managed in the BI life cycle.
Almost each of these 10 modules is a mixture of presentations, discussions, case stories, demos and hands-on exercises. I have the following learnings and observations from the teaching.
 
  • First, the demos and hands-on exercises are essential for running this course. I always believe that an IT course must have the spirit of “get your hands dirty.” As I can feel from the students, the exercises and demos provide a more “real picture” of how BI is and get them more “engaged” into the course. However, it has been a challenge for the students to create an effective environment to run through all the exercises.
  • Second, the course covers many different topics. The overwhelming new terms and definitions became a challenge for the students. Starting from Module 3, I created a glossary document in order to help the students with the new terms and definitions.
  • Third, although I have used quite a few case stories and customer examples during the course, I am still facing the challenge of lacking detailed cases stories in different industries. Besides the cases and experiences I had in my own career path, it is difficult to get very detailed customer stories just by looking through the vendors’ site or browsing the literature.
 
Above all, besides crashing these colleague students with a thorough education of BI, I myself have enhanced my knowledge of business intelligence after running the 10 modules. Through the discussions, the students are stepping into the BI domain with the new terms and concepts. It is very exciting that they are using BI as the main topic of the semester project. I am looking forward to seeing their project reports.  J