COURSE INFORMATION Course Title: BUSINESS ANALYTICS AND INTELLIGENCE

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BIDS 403	B	1	3	0	2	3	9

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	Dr. Erind Bedalli ebedalli@epoka.edu.al
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Dr. Erind Bedalli ebedalli@epoka.edu.al , Thursday 16:30 - 17:30
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
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:	E311 & Lab2
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:
Course Description:	Business Intelligence (BI) refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of business intelligence is to support better business decision making. This course provides an overview of the technology of BI and the application of BI to an organization’s strategies and goals
Course Objectives:	Business Intelligence (BI) refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of business intelligence is to support better business decision-making. This course provides an overview of BI technologies and architectures, combined with practical analytics for data preprocessing, visualization, and predictive modeling. Students will apply BI tools to extract actionable insights supporting strategic and operational decisions.

BASIC CONCEPTS OF THE COURSE

1	Data Management and Preprocessing
2	BI Architecture and Infrastructure
3	Data Warehousing and Dimensional Modeling
4	Data Integration and ETL Tools
5	Business Reporting and Dashboards
6	Descriptive and Predictive Analytics

COURSE OUTLINE

Week	Topics
1	Introduction to Business Intelligence and Analytics
2	Data Management and Preprocessing for BI
3	BI Architecture and Infrastructure
4	Data Warehousing and Dimensional Modeling
5	Data Integration and ETL Tools
6	Business Reporting and Dashboards
7	Midterm Review and Case Study
8	Midterm Exam
9	Descriptive Analytics
10	Predictive Analytics in BI
11	Prescriptive Analytics and Optimization
12	AI and Machine Learning for Business Intelligence
13	Big Data and Cloud BI
14	Final Project Presentations and General Review

Prerequisite(s):
Textbook(s):	"Data Analytics and Business Intelligence Computational Frameworks, Practices, and Applications" by Vincent Charles, Pratibha Garg, Neha Gupta, Mohini Agarwal, CRC Press 2023
Additional Literature:	"Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios" by Felix Weber, 2023
Laboratory Work:	Yes
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Explain the concepts, components, and architecture of Business Intelligence systems.
2	Apply data preprocessing, integration, and exploratory analysis.
3	Use BI tools and visualization libraries to design dashboards and reports.
4	Build predictive and prescriptive analytics models for decision-making.
5	Evaluate cloud-based and AI-enhanced BI systems for business use cases.
6	Assess data governance, ethics, and implementation challenges in BI adoption.

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.	2
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.	3
8	Effectively communicate to top management the results and implications arising from data analytics, security risk assessments, and emerging technologies.	3
9	Demonstrate professionalism and leadership by taking initiatives within their domain of responsibility while working effectively with other team members.	2
10	The program offers practical training and exposure to industry-standard software and tools used in business intelligence and data analysis.	5

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	25
Project	1	30
Final Exam	1	40
Attendance		5
	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	5	80
Mid-terms	1	20	20
Assignments	1	47	47
Final examination	1	30	30
Other			0
Total Work Load:			225
Total Work Load/25(h):			9
ECTS Credit of the Course:			9

CONCLUDING REMARKS BY THE COURSE LECTURER

The students engagement is great, especially in the application part.