Epoka University

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BINF 312	C	6	2	0	2	3	6

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	Dr. Nurul Retno Nurwulan nnurwulan@epoka.edu.al
*Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature)* and Office Hours:**	M.Sc. Mohammad Ziyad Kagdi mkagdi@epoka.edu.al , 08:40AM to 4:30PM
*Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature)* and Office Hours:**	NA
Language:	English
Compulsory/Elective:	Elective
Study program: (the study for which this course is offered)	Bachelor in Business Informatics (3 years)
Classroom and Meeting Time:	E 312, Wednesday, 8:40AM - 10:30AM
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:	70%
Course Description:	-
Course Objectives:	The Data Science course aims to provide individuals with the skills, knowledge, and tools necessary to extract actionable insights from data by developing skills in machine learning algorithms and techniques for building predictive models and making data-driven decisions.

BASIC CONCEPTS OF THE COURSE

1	Introduction to Data Science, Problems and Techniques
2	Understanding Supervised and Unsupervised Learning
3	Understanding Principles of Machine Learning
4	Regression Analysis for Predicting Continuous Variables
5	Logistic Regression and Decision Trees for Classification Problems
6	Random Forests for Classification Problems
7	Support Vector Machines (SVM) for Classification and Regression Problems
8	K-means Clustering and Hierarchical Clustering for Unsupervised Learning
9	Principle Component Analysis (PCA) for Dimensionality Reduction
10	K-Nearest Neighbors (KNN) Algorithm for Classification and Regression Problems

COURSE OUTLINE

Week	Topics
1	Introduction to Data Science, Problems and Techniques
2	Supervised and Unsupervised Learning
3	Principles of Machine Learning
4	Regression Analysis
5	Logistic Regression and Decision Trees
6	Random Forest Classification
7	Midterm
8	Support Vector Machines (SVM)
9	K-means Clustering
10	Hierarchical Clustering
11	Principle Component Analysis (PCA)
12	K-Nearest Neighbors (KNN) Algorithm
13	Artificial Neural Network
14	Revision

Prerequisite(s):	Knowledge of Statistics, Mathematics, Python Programming Language and Data Analytics
Textbook(s):
Additional Literature:
Laboratory Work:	2 Hours
Computer Usage:	Python Programming
Others:	No

COURSE LEARNING OUTCOMES

1	Introduction to Data Science, Problems and Techniques
2	Understanding Supervised and Unsupervised Learning
3	Understanding Principles of Machine Learning
4	Regression Analysis for Predicting Continuous Variables
5	Logistic Regression and Decision Trees for Classification Problems
6	Random Forests for Classification Problems
7	Support Vector Machines (SVM) for Classification and Regression Problems
8	K-means Clustering and Hierarchical Clustering for Unsupervised Learning
9	Principle Component Analysis (PCA) for Dimensionality Reduction
10	K-Nearest Neighbors (KNN) Algorithm for Classification and Regression Problems

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

No	Program Competencies	Cont.
Bachelor in Business Informatics (3 years) Program
1	Identify activities, tasks, and skills in management, marketing, accounting, finance, and economics.	3
2	Apply key theories to practical problems within the global business context.	3
3	Demonstrate ethical, social, and legal responsibilities in organizations.	5
4	Develop an open minded-attitude through continuous learning and team-work.	5
5	Integrate different skills and approaches to be used in decision making and data management.	1
6	Combine computer skills with managerial skills, in the analysis of large amounts of data.	1
7	Provide solutions to complex information technology problems.	2
8	Recognize, analyze, and suggest various types of information-communication systems/services that are encountered in everyday life and in the business world.	4

COURSE EVALUATION METHOD

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	2	32
Hours for off-the-classroom study (Pre-study, practice)	16	4	64
Mid-terms	0	0	0
Assignments	2	22	44
Final examination	1	10	10
Other	0	0	0
Total Work Load:			150
Total Work Load/25(h):			6
ECTS Credit of the Course:			6

CONCLUDING REMARKS BY THE COURSE LECTURER

N/A