Business Intelligence II

Objectives

The main goal of the Business Intelligence course covered in these two semester units is to give students the knowledge and competency related with decision support capacities provided by Business Intelligence processes, along with supporting Data Warehouse structures. These skills include an awareness of the market forces advocating Digital Transformation initiatives, development methodologies underlying modern Business Intelligence strategies and architectures, along with current information technological development in the fields of Business Analytics, Advanced Data Analysis, and Business Performance Management.

General characterization

Code

200013

Credits

7.5

Responsible teacher

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Basic knowledge in relational databases (SQL). An introductory-level SQL Crash course is offered at the start of the semester, to all students lacking a basic level of knowledge and experience in this subject area.

Bibliography

Turban E.; Sharda R.; Dursun D. and King D. (2011). Business Intelligence: a managerial approach. Second Edition. Prentice Hall, ISBN: 013610066X. ; Howson, C. (2008). Successful business intelligence : secrets to making BI a killer app. McGraw Hill. ISBN 978-0-07-149851-7; Few, Stephen (2006). Information Dashboard Design: The Effective Visual Communication of Data. Sebastopol, CA: O’Reilly Media. ISBN 0596100167; Eckerson, Wayne W. (2006). Performance Dashboards: Measuring, Monitoring, and Managing Your Business. Hoboken, NJ: John Wiley & Sons. ISBN 0750661747; 0

Teaching method

This course will include both lectures and labs.

In the lectures we will present the theoretical concepts, case studies and, possibly, some presentations from leading BI vendors. The applied component of the course will take place in the Labs, where students will be shown how to apply the concepts and theories presented in lectures, by leveraging the use of the Microsoft Business Intelligence Platform (SQL Server, SQL Server Management Studio, Visual Studio, along with limited use of Microsoft Azure cloud-based tools) as the teachers demonstrate and provide practical guidance on the joint development of a class project.

In addition, and so as to further reinforce the applied skills and methodologies that the students are learning, they will be required to implement group-based practical projects of their own choosing, as part of the assessment.

Evaluation method

The assessment includes:

a) Project (50%)

b) Final exam (50%)

To successfully complete the course students must obtain a minimum score of 9.5 in the final exam (both seasons), irrespective of marks obtained in a) or b).

Subject matter

LECTURES

T.1 - Business Analytics and Data Visualization

    1.1 Introduction to the field of Business Analytics ( BA )

    1.2 Online Analytical Processing ( OLAP )

    1.3 Reports and Queries

    1.4 Multidimensionality

    1.5 Advanced Business Analytics

    1.6 Data Visualization

    1.7 Geographic Information Systems

    1.8 Business Intelligence real-time decision support and automated Competitive Intelligence

    1.9 Web Analytics : Web Intelligence and Web Analytics

    1.10 Use, Benefits and Results of Business Analytics

T.2 - Data, Text and Web Mining

    2.1 Concepts and Applications of Data Mining

    2.2 Concepts and Applications of Text Mining

    2.3 Concepts and Applications of Web Mining

T.3 - Business Performance Management

    3.1 Introduction to Business Performance Management ( BPM )

    3.2 Strategy

    3.3 Planning

    3.4 Monitoring

    3.5 Acting and adjusting

    3.6 Performance Measurement

    3.7 BPM Methodologies

    3.8 Architecture and applications of BPM

    3.9 Performance Dashboards

    3.10 Business Activity Monitoring (BAM)

T.4 - Information Dashboard Design

    4.1 Dashboards

    4.2 Dashboard Design Challenges

    4.3 Dashboard Design Best Practices

    4.4 Information Design

    4.5 Bad Practices in Dashboard Design

    4.6 Categorizing Dashboards

    4.7 Typical Dashboard Data

    4.8 Data Visualization Tips

   

LABS:

LAB 01: Overview of BI II Labs and Project Presentation

LAB 02: Introduction to SQL Server Analysis Services (Tabular)

LAB 03: Continuing our Cube adventure with IMSClass

LAB 04: Introduction to SQL Server Reporting Services

LAB 05: Introduction to Self-Service BI

LAB 06: Power BI Solution Development ("Hollywood Movies") Part A

LAB 07: Continuing Power BI with "Hollywood Movies" Part B

LAB 08: Continuing Power BI with "Hollywood Movies" Part C

LAB 09: Exploring Microsoft-based Cloud (Azure) BI Tools