COURSE INFORMATION Course Title: DATA WAREHOUSING AND BUSINESS INTELLIGENCE

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BIDS 402	B	2	3	0	2	4	9

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	Dr. Edlir Spaho espaho@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Edlir Spaho espaho@epoka.edu.al , Thursday 18:00-21:00
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Xhezmije Palushi xhpalushi@epoka.edu.al
Language:	English
Compulsory/Elective:	Compulsory
Study program: (the study for which this course is offered)	Master of Science in Business Intelligence and Data Science
Classroom and Meeting Time:	Thursday 18:00-21:00
Teaching Assistant(s) and Office Hours:	NA
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	75% Mandatory
Course Description:	Business Intelligence is the transformation of data into actionable information. This information is used by businesses to drive high-level decision making. This course is concerned with extracting data from the information systems that deal with the day-to-day operations and transforming it into data that can be used for decision making. Students will learn how to design and create a data warehouse, and how to utilize the process of extracting, transforming, and loading (ETL) data into data warehouses. Students will design and construct dynamic reports using the data warehouse and multi-dimensional online analytical processing (OLAP) cubes as the data source.
Course Objectives:	At the completion of this course, students will: 1. Understand the business reasons for creating data warehouses and OLAP cubes. 2. Understand the characteristics of online transaction processing databases, data warehouses, 3. Understand, design, and construct an ETL solution. 4. Design and construct a BI system that includes a data warehouse and an OLAP cube. 5. Understand, design, and construct a solution that will populate a data warehouse and an OLAP cube. 6. Design and construct a dynamic reporting solution.

BASIC CONCEPTS OF THE COURSE

1	Business Intelligence (BI): The process of collecting, analyzing, and presenting data to support business decision-making.
2	Data Warehouse: A centralized repository of integrated, structured, and historical data from multiple sources to support BI and reporting.
3	ETL (Extract, Transform, Load): The process of extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse.
4	Dimension: A descriptive attribute or characteristic of a data set, such as time, geography, or product.
5	Dimension: A descriptive attribute or characteristic of a data set, such as time, geography, or product. Fact: A measurable or numerical value representing a specific aspect of business performance, such as sales, revenue, or customer count.
6	OLAP (Online Analytical Processing): A technology that enables multidimensional analysis of data, allowing users to navigate and analyze data from different perspectives.
7	Data Mart: A subset of a data warehouse that is focused on a specific business function or department, providing a more specialized view of data.
8	Dashboard: A visual representation of key performance indicators (KPIs) and metrics, providing a concise overview of business performance.

COURSE OUTLINE

Week	Topics
1	Introduction to the Course. Overview / Refresher of Relational Database Theory. Overview / Refresher of Data Modeling in 3rd Normal Form (3NF). Using Entity Relationship Diagramming (ERD)
2	Data Warehousing Design: Introduction, Data Marts, Inmon’s Methodology, Kimball’s Methodology, Dimensional Design, Star Schema, Dimension Tables.
3	Data Warehousing Design (2): Keys and History, Fact Tables, Surrogate Keys vs Natural Keys, Rich Dimensions, Slowly Changing Dimensions (Type 1, 2, 3, Hybrid), Multiple Stars, Conformed Dimensions, Snowflakes, Outriggers
4	Data Warehousing Design (3): OLAP Cubes, 3D, Hypercubes, Slicing, Dicing, Drill Up / Down, Rollup,
5	Pivot Variations of Cube Architectures: MOLAP Cubes, ROLAP Cubes, HOLAP Cubes.
6	Pivot Variations of Cube Architectures (2):OLAP Cubes, DOLAP Cubes, RTOLAP Cubes
7	Business Case Examples of Data Warehouse Designs
8	Midterm Exam
9	Architectures for Data Warehousing Topics include: General Architecture Principles, SAP BW, Teradata, Hadoop
10	Extract, Transform, Load (ETL)
11	Data Visualization using Tableau Topics include: Overview of Data Visualization, Getting Data: Connections, Extracts, Metadata, Joins, Blends, Filters.
12	Data Visualization using Tableau(2): Common Visualizations; Bar Charts, Treemaps, Area Charts, Pie Charts, Circle Charts, Box and Whisker, Histograms, Scatterplots, Line Charts, Geographic Visualizations, Dashboards, Storyboards
13	Business Intelligence using SAP Business Objects Topics include: Universe Design: Connections, Data Foundations, Business Layer, Folders, Dimensions, Measures
14	Business Intelligence using SAP Business Objects (2) Topics include: OLAP Universes: OLAP Cube Queries NoSQL Data Queries from Hadoop

Prerequisite(s):	While this course has no pre-requisites nor co-requisites, students without prior Information Technology exposure and/or database exposure will face a steeper learning curve.
Textbook(s):	1- Lecture slides in PPT format 2- Data Warehouse Systems, Design and Implementation, Second Edition, Springer, 2022.
Additional Literature:	1. Data Warehouse Requirements Engineering: A Decision Based Approach, Naveen Prakash,Deepika Prakash (auth.), Springer, 2018. 2. The history of ETL : Compact history of Extract , Transform and Load software, Henri van Maarseveen, 2023. 3. Data Modeling with Snowflake: A practical guide to accelerating Snowflake development using universal data modeling techniques, Serge Gershkovich and Kent Graziano, 2023
Laboratory Work:	Yes
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Students will get to know the Big Data Analytics Domain
2	Students will be able to frame and solve Business Analytical problems
3	Students will be capable of building predictive models
4	Students will get hands on data mining skills to monetize data
5	Students will learn to interpret and communicate the key business insights obtained from model building
6	Students will become familiar with Data Warehousing
7	Students will be able to Envision, Manage and Lead Analytical Projects (Entry Level)

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Master of Science in Business Intelligence and Data Science Program
1	Demonstrate understanding the value of data driven decision making.	5
2	Graduates will acquire the ability to make informed decisions based on data analysis and interpretation.	5
3	Identify the basic concepts that underpin today’s organizational IT infrastructures, such as concepts of databases, information systems, operations and processes, cloud computing, data warehousing and Big Data, Data Mining and Machine Learning.	5
4	Students will develop advanced skills in data analysis techniques, including statistical analysis, data mining, data visualization, and predictive modeling.	5
5	Apply data mining/analytics (statistical and machine-learning) in order to solve real-world business problems.	3
6	Develop skills related to data analytics pipeline from collection, processing, analysis and interpretation	5
7	Graduates will develop strong communication skills to effectively present complex data analysis findings to diverse stakeholders.	5
8	Effectively communicate to top management the results and implications arising from data analytics, security risk assessments, and emerging technologies.	4
9	Demonstrate professionalism and leadership by taking initiatives within their domain of responsibility while working effectively with other team members.	3
10	The program offers practical training and exposure to industry-standard software and tools used in business intelligence and data analysis.	3

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Project	2	15
Final Exam	1	40
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	3	48
Mid-terms	1	15	15
Assignments	1	10	10
Final examination	1	15	15
Other	1	89	89
Total Work Load:			225
Total Work Load/25(h):			9
ECTS Credit of the Course:			9

CONCLUDING REMARKS BY THE COURSE LECTURER

Students should uphold the code of ethics in all academic endeavors. Cheating in any form is strictly prohibited. Please be aware that any misbehavior report will result in an automatic evaluation of zero points for the respective exam.