COURSE OUTLINE
Databases for Business Analytics
1. General
School
Academic Unit
Level of Studies
Course code
Semester
Course Title
Idependent Teaching Activities
Weekly Teaching Hours
Credits
Course Type
Prerequite Courses
Language of Instruction and Examinations
Is the course offered to Erasmus Students?
Url (Eclass)
2. Learning Outcomes
Learning Outcomes
Upon successful completion of the course, the student:
- will be able to draw Entity-Relationship diagrams to illustrate the structure and characteristics of a relational database.
- will have the ability to manage relational databases through SQL, specifically: (i) create objects, (ii) fill in tables, (iii) update existing data, and (iv) execute queries on databases.
- will be familiar with the R language and know its basic functions, which are needed to be able to process data stored in a relational database.
- will be able to use econometric methods to focus on the analysis of a business in order to make optimal decisions.
General Competences
Within the framework of the combined skills that the graduate will acquire with the following all the courses of the study program, the course of Databases and Business Analytics aims for the graduate to acquire abilities:
- search for, analysis and synthesis of data and information, with the use of the necessary technologies,
- decision-making
- working independently
- production of free, creative and inductive thinking
- criticism and self-criticism
3. Syllabus
Section 1: SQL & Relational Databases
- What is a relational database and how it is structured
- Understanding table schema: fields, data types, constraints
- Creating and managing tables using CREATE, ALTER, DROP
- Using basic SQL commands: SELECT, WHERE, ORDER BY, DISTINCT, LIMIT
- Aggregate functions and grouping: GROUP BY, HAVING
- Joining tables: INNER JOIN, LEFT JOIN, etc.
- Subqueries: IN, EXISTS, ANY, ALL
- Creating virtual tables (Views) for reporting
- Practical use of SQL Server Management Studio (SSMS)
Section 2: Econometric Tools for Practical Applications
- What is regression and how it is used in economic analysis
- Estimation of simple and multiple linear regression
- Assumption checks and interpretation of results (p-values, R², t-tests)
- Problems and diagnostic tests:
- Multicollinearity
- Heteroskedasticity
- Autocorrelation
- Application of econometric models to financial datasets
Section 3: Data Analysis with R
- Using RStudio for statistical and econometric analysis
- Connecting R to SQL Server (via DBI and odbc)
- Retrieving data from SQL tables directly into R
- Data processing with dplyr: filter(), select(), mutate(), summarise()
- Creating basic visualizations (histograms, line charts, scatterplots)
- Estimating and interpreting regression models using lm()
- Full pipeline for analysis using SQL and R:
From SQL → R → Analysis → Econometric conclusions
4. Teaching and Learning Methods - Evaluation
Delivery
Use of Information and Communications Technology
Each student will use the R programming language as well as the SQL relational database through his/her Personal Computer.
Teaching Methods
Activity
Semester Workload
Student Performance Evaluation
Greek, 80% Final Exam and 20% Project presentation at class (optional)
5. Attached Bibliography
Suggested Bibliography
- Lecture Notes
- Lecture Slides