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)	Dr. Aida Dhima abitri@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 212, Monday, 11:40 - 13:30
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%
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	Introducing the significance of data analytics and data science
2	Employing python's pandas library to manipulate dataframes of large datasets
3	Data preparation, Data Cleaning, Outliers removal and Data transformation
4	Visualising prepared data using python's matplotlib and seaborn library
5	Employing descriptive statistics to gain understanding of large datasets
6	Learning advanced data analytics techniques using python
7	Using principal component analysis to reduce dimensionality of large datasets
8	Understanding analyses of varience to analyse differences in data
9	Using statistical hypothesis testing to draw conclusion about theories
10	Using regression analysis to predict outcomes

COURSE OUTLINE

Week	Topics
1	Introduction to Data Analysis: Data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions.
2	Manipulating dataframes using python's Pandas library: Pandas is a Python library used for working with large data sets and it has functions for analysing, cleaning, exploring, and manipulating data.
3	Data Preprocessing / Normalisation: Data preprocessing is an important step in the data analysis process and it contains important steps that include data cleaning, data integration, feature selection and data transformation in order to make the data ready for analysis. The goal of data preprocessing is to improve the quality of the data and to make it more suitable for the specific data analysis task.
4	Principal Component Analysis: PCA is an unsupervised linear dimensionality reduction technique that can be utilised for extracting information from a high-dimensional space. It preserves the essential parts in a dataset that have more variation of the data and remove the non-essential parts with fewer variation.
5	Exploratory Data Analysis (EDA): EDA is for seeing what data can tell us beyond further data modeling. It is an act of performing initial investigations on data to discover patterns using summary statistics and graphical representations.
6	Data Visualisation using Matplotlib and Seaborn: A lot of data is being generated on a daily basis. And sometimes to analyse this data for certain trends, patterns may become difficult if the data is in its raw format. Data visualisation provides a good, organized pictorial representation of the data which makes it easier to understand, observe, analyze.
7	Visualising Time Series Data: A time series is a set of data points that are collected over a period of time, usually at regular intervals. The most common type of time series data is financial data, such as stock prices or exchange rates. Time series line graphs are the best way to visualise data that changes over time. This is because line graphs show how a variable changes from one point in time to another, making it easy to see trends and patterns.
8	Midterm
9	Advanced Data Analysis Techniques: Employing statistical techniques such as correlation, regression analysis and cluster analysis techniques to discover trends, patterns and groups in large datasets
10	Statistical Hypothesis Testing: A statistical hypothesis test is a method of statistical inference used to decide whether the data sufficiently supports a particular hypothesis.
11	T-statistic vs F-statistic: The t-test is used to compare the means of two groups and determine if they are significantly different, while the F-test is used to compare variances of two or more groups and assess if they are significantly different.
12	ANOVA (Analysis of Variance): ANOVA is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means.
13	Regression analysis: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory variables or features).
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 noise from datasets
4	Using principal component analysis to reduce dimensionality of large datasets
5	Using descriptive statistics to analyse data and summerise its characteristics
6	Using matplotlib and seaborn to visualise huge datasets
7	Learning advanced data analytics techniques using python
8	Understanding analyses of varience to analyse differences in data
9	Using statistical hypothesis testing to draw conclusion about theories
10	Using regression analysis to predict outcomes

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	1	20
Midterm Exam(s)	0	0
Presentation	0	0
Project	4	20
Quiz	0	0
Laboratory	0	0
Lab/Practical Exams(s)	0	0
Case Study	0	0
Term Paper	0	0
Final Exam	0	0
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)	12	4	48
Mid-terms	0	0	0
Assignments	4	10	40
Final examination	1	5	5
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

The course would enable students to computationally apply statistical techniques to analyse and visualise large datasets.