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COURSE OUTLINE

Databases for Business Analytics

1. General

School

School of Finance and Statistics

Academic Unit

Department of Banking and Financial Management

Level of Studies

Undergraduate

Course code

ΧΡΒΔΕΑ

Semester

5th or 7th

Course Title

Databases for Business Analytics

Idependent Teaching Activities

Weekly Teaching Hours

Credits

Lectures and laboratory exercises
4
7,5

Course Type

Specialised and Skills Development

Prerequite Courses

None. Basic knowledge of statistics is useful.

Language of Instruction and Examinations

Greek

Is the course offered to Erasmus Students?

No

Url (Eclass)

https://eclass.unipi.gr/modules/auth/courses.php?fc=64

2. Learning Outcomes

Learning Outcomes

Upon successful completion of the course, the student will be able to:

  • describe and design the structure of a relational database (tables, fields, data types, primary and foreign keys, integrity constraints).
  • manage relational databases through SQL, specifically: (i) create objects, (ii) populate tables, (iii) update existing data and (iv) run queries using filters, aggregations, table joins, subqueries and views.
  • estimate and interpret simple and multiple linear regression models, and carry out hypothesis tests and diagnostic tests (multicollinearity, heteroskedasticity, autocorrelation) on financial data.
  • use the R language and RStudio to retrieve data directly from a relational database, process it (dplyr), visualise it and estimate econometric models.
  • present data and results in Business Intelligence tools (Power BI) and spreadsheets (Excel).
  • implement a complete end-to-end analysis workflow, from the database to well-founded business conclusions.

 

General Competences

Within the combined skills that graduates acquire through the full study programme, the course Databases and Business Analytics aims to develop the following competences:

  • searching for, analysing and synthesising data and information, using the necessary technologies
  • decision-making
  • working independently
  • project planning and management
  • adapting to new situations
  • promoting free, creative and inductive thinking
  • criticism and self-criticism

3. Syllabus

Unit 1: SQL & Relational Databases

  1. What a relational database is and how it is structured
  2. Table schema, fields, data types and constraints
  3. Creating and managing tables with CREATE, ALTER, DROP
  4. Basic SQL statements: SELECT, WHERE, ORDER BY, DISTINCT, TOP/LIMIT
  5. Aggregate functions and grouping (GROUP BY, HAVING) – computing KPIs
  6. Table joins (INNER JOIN, LEFT JOIN, etc.)
  7. Subqueries (IN, EXISTS, ANY, ALL)
  8. Views for reporting
  9. Hands-on use of SQL Server Management Studio (SSMS)

Unit 2: Econometric Tools for Practical Applications

  1. What regression is and how it is used in economic analysis
  2. Estimation of simple and multiple linear regression
  3. Hypothesis testing and interpretation of results (p-values, R², t-tests, confidence intervals)
  4. Problems and diagnostic tests: multicollinearity, heteroskedasticity, autocorrelation
  5. Applying econometric models to financial data

Unit 3: Data Analysis with R and Business Intelligence

  1. Using RStudio for statistical and econometric analysis
  2. Connecting R to SQL Server (via DBI and odbc) and retrieving data directly from SQL tables
  3. Data processing with dplyr: filter(), select(), mutate(), summarise()
  4. Data visualisation with ggplot2 (histograms, line charts, scatter plots)
  5. Introduction to Power BI: connecting to SQL Server and building dynamic dashboards
  6. Excel for business analysis: pivot tables, XLOOKUP, SUMIFS, COUNTIFS
  7. Estimating and interpreting regression models with lm()
  8. Combining SQL and R for end-to-end analysis: from the SQL database to R, the analysis and the econometric conclusions – project presentations

4. Teaching and Learning Methods - Evaluation

Delivery

Face-to-face (in-person attendance)

Use of Information and Communications Technology

  • In teaching: lectures with electronic slides and live coding demonstrations.
  • In laboratory training: each student uses SQL Server with SQL Server Management Studio, R with RStudio, Power BI Desktop and Excel on their own computer, working on the course’s shared stock-market database.
  • Remote practice: weekly assignments on the DataCamp platform (the course’s DataCamp Classroom).
  • In communication with students: eclass (materials, announcements, assignment submission) and email.

Teaching Methods

Activity

Semester Workload

Lectures
26
Laboratory exercises
26
Exercises on the DataCamp platform
35
Project work
60
Course Total
187,5

Student Performance Evaluation

Language of evaluation: Greek.

  • 60% Final written exam (theory & code): theory and short-answer questions, writing SQL queries and R code, problem solving and interpretation of econometric results.
  • 40% Project – End-to-End Analytics Pipeline: a complete analysis workflow from the database (SQL) to R and econometric analysis, through to business conclusions. Students submit a written report and code, and present the project in class.
  • Bonus points: completion of the weekly assignments on the DataCamp platform.

5. Attached Bibliography

Suggested Bibliography
  1. Lecture notes and slides, course SQL and R code (eclass).
  2. Elmasri, R., & Navathe, S. B. (2015). Fundamentals of Database Systems. Pearson.
  3. Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach. Cengage Learning.
  4. Wickham, H., & Grolemund, G. (2016). R for Data Science. O’Reilly Media.
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