COURSE INFORMATION Course Title: DATA ANALYTICS AND VISUALIZATION

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
BINF 311	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. 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
Teaching Assistant(s) 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 212, Monday, 11:40 - 13:30
Code of Ethics:	Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline"
Attendance Requirement:	70%
Course Description:	Modern data visualization technology is causing a paradigm shift in the way organizations convert raw data into actionable information. Visualization facilitates rapid understanding of trends and outliers within datasets. Moreover, modern data visualization tools are at the forefront of the “self-service analytics” architectures which are decentralizing analytics and breaking down IT bottlenecks for business experts. Therefore, this course will provide students with a formal grounding in data visualization as well as hands-on experience using Tableau, a popular modern software package. These skills will serve students in their early career and continue to pay dividends in the future.
Course Objectives:	This course would enable students to work with real-world raw data in order to preprocess, analyse and visualise it using Python programming.

BASIC CONCEPTS OF THE COURSE

1	What is data and information
2	Introduction to data analytics
3	Data collection
4	Data cleaning
5	Python programming language
6	Descriptive statistics
7	Analysis of varience
8	Visualising huge datasets
9	Advanced data analytics techniques
10	Real-world applications

COURSE OUTLINE

Week	Topics
1	Introduction to Data Analysis
2	Data Preprocessing / Normalisation
3	Exploratory Data Analysis
4	Data Visualisation using Matplotlib
5	Statistical Hypothesis Testing
6	ANOVA (Analysis of Variance)
7	Midterm
8	Advanced Data Analysis Techniques
9	Regression analysis
10	Time series analysis
11	Multidimensional Scaling
12	Principal Component Analysis
13	Real-World Applications
14	Revision

Prerequisite(s):	Basic to Intermediate skills in any programming language
Textbook(s):	Any reference book pertaining to Data Analytics / Data Science with Python
Additional Literature:
Laboratory Work:	Yes
Computer Usage:	Yes
Others:	No

COURSE LEARNING OUTCOMES

1	Understanding the significance of data analytics
2	Learning python programming as a data analytics tool
3	Conducting data cleaning, extracting outliers, duplicates and noises from datasets
4	Using descriptive statistics to analyse data and summerise its characteristics
5	Understanding analyses of varience to analyse differences in data
6	Using matplotlib and seaborn to visualise huge datasets
7	Learning advanced data analytics techniques using python

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.	1
4	Develop an open minded-attitude through continuous learning and team-work.	1
5	Integrate different skills and approaches to be used in decision making and data management.	5
6	Combine computer skills with managerial skills, in the analysis of large amounts of data.	5
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

Method	Quantity	Percentage
Homework	3	10
Midterm Exam(s)	1	15
Presentation	0	0
Project	3	10
Quiz	0	0
Laboratory	0	0
Lab/Practical Exams(s)	0	0
Case Study	0	0
Term Paper	0	0
Final Exam	1	25
Attendance		0
Other	0	0
	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	2	32
Hours for off-the-classroom study (Pre-study, practice)	15	3	45
Mid-terms	1	4	4
Assignments	6	6	36
Final examination	1	8	8
Other	0	0	0
Total Work Load:			125
Total Work Load/25(h):			5
ECTS Credit of the Course:			5

CONCLUDING REMARKS BY THE COURSE LECTURER

N/A