Undergraduate Studies
Academic Year 2025-26
Databases for Business Analytics
Files and Links
- Course Outline .pdf (Greek)
- Course Outline .pdf (English)
- Full Description @courses.xrh
- Link to e-class
5th or 7th Semester
ΧΡΒΔΕΑ
Course id
7,5
ECTS
Specialised and Skills Development
Course type
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.
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
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
