COURSE INFORMATION Course Title: DATA SCIENCE FOR BUSINESS

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BINF 301	B	5	2	0	2	3	5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	M.Sc. Taskyn Rakhym trakhym@epoka.edu.al , Thursday 12:40-14:40
Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Study program: (the study for which this course is offered)	Bachelor in Economics (3 years)
Classroom and Meeting Time:	Monday 11.45
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	75%
Course Description:	This course is about how to think about extracting information from data in order to solve problems better. We will examine how data analysis technologies can be used to improve business problem-solving and decision-making. We will study the fundamental principles, techniques, hands-on tools and “conceptual tools” of data science and business analytics, and we will examine real-world examples and cases to place data- mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
Course Objectives:	1.Approach (business) problems data-analytically. Think carefully & systematically about whether & how data can improve performance, to make better-informed decisions for management, marketing, operations, investment, etc. 2. Be able to interact competently on topic of data science and analytics. Know the fundamental principles of data science, that are the basis for data mining processes, machine learning algorithms & analytic systems. Understand these well enough to work on data science projects and interact with everyone involved. Envision new opportunities. 3. Have had hands-on experience mining data. Be prepared to follow up on ideas or opportunities that present themselves, e.g., by performing pilot studies. 4. Be able to work in team and collaborate fairly during the group task and Project.

BASIC CONCEPTS OF THE COURSE

1	What is Data
2	Data types
3	Data collection
4	Data cleaning
5	Data Pre processing
6	Descriptive statistical analysis
7	Midterm
8	Regression analysis
9	Classification analysis
10	Performance analysis

COURSE OUTLINE

Week	Topics
1	Introduction to the Course Introduction to Data Analytics&Science Tools for Business Analytics – Conceptual and Practical
2	Data-Information-Knowlagde What is Data and information ?! What does a business Analyst do?! Declaring Business Question Practical Task 1 Defining Business Task
3	Types of Data Data Collection Types of Data Collection and it’s Digital tools Practical Task 2 Collecting the data from the organisation
4	Data Cleaning. Extracting outliers, duplicates and Noises from data Practical Task 3 Cleaning the collected data
5	Data Preprocessing: Formatting Normalising Practical Task 4 Pre-processing the cleaned Data
6	Descriptive Statistical analysis Practical Task 5 Conducting Descriptive Analysis on pre-processed Data
7	Midterm Exam
8	Regression, Predicting Continuous values Practical Task 6 Building Regression model on Data
9	Classification, Predicting categorical values Practical Task 7 Build Classifier for dataset
10	Model performance analytics II Ranking, true positives, false positives, profit, lift Accuracy, Precision Recall Practical Task 8 Conduct performance analysis on Classification and regression Models
11	Visualisation(types of graphs, chartres and their application)
12	Storytelling with Data and reporting
13	Exercises, case studies from industry
14	Project presentation skills How to make productive presentation

Prerequisite(s):	NA
Textbook(s):	Anthony Sarkis, Training Data for Machine Learning: Human Supervision from Annotation to Data Science, O'Reilly Media, 2022.
Additional Literature:
Laboratory Work:	8
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Understand difference between Data And Information
2	Types of Data
3	Able to collect data
4	Conduct Data Cleaning, Extracting outliers, duplicates and Noises from data
5	Able to pre-Preprocess
6	Able to conduct Descriptive Statistical analysis
7	Midterm Exam
8	Build Regression models, Predicting Continuous values
9	Build Classification models, Predicting categorical values
10	Conduct Model performance analytics

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

No	Program Competencies	Cont.
Bachelor in Economics (3 years) Program

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Quiz	2	10
Laboratory	8	5
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)	14	2	28
Mid-terms	1	8	8
Assignments	8	4	32
Final examination	1	9	9
Other			0
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER