Course overview
Using personal computers as effective data management and problem-solving tools for the present and the future is demonstrated and implemented. This course builds on code and nocode tools and techniques.
Learning modules:
• Understanding the role and architecture of DB systems.
• Acquiring knowledge and skills of DB modelling, design and programming.
• Gain experience in installing, programming and managing a Database Management System.
• Setting up for Data Analytics: loading, cleaning, initial exploration, saving and preparing data for in-depth analysis.
• Introduction to basic imperative visual analytics principles; visual analytics advances science and technology developments in analytical reasoning, interaction, data transformations and representations for computation and visualization, analytic reporting, and technology transition.
• Introduction to the fundamentals of statistical programming in R language.

Learning outcomes
By the end of the first week, participants will be able to demonstrate a working knowledge of the concepts and principles that underpin how databases work, identify and explain the different types of core technology and management systems used in databases, identify and interpret basic SQL statements and commands, plan and design a simple relational database system.
In the second week, participants will learn how to take their data from Excel into visualization and statistical software, transform it to easy-to-understand dynamic graphics, interactively explore what-if scenarios, and build data-driven strategies. The main idea is to provide an effective use of spreadsheets to process, manipulate, analyze, and visualize numeric and textual information.

Course content
The course covers the following topics:

Databases (Ιnstructor Prof. Dr. Georgia Garani)
• Introduction to Database Management Systems
• Architecture of a DBMS
• Data modeling and the Entity-Relationship Model
• Relational model
• Converting the Entity-Relationship Model to the Relational model
• Relational Algebra
• SQL language (data definition and data manipulation languages)
• Familiarity with a Relational Database System
• Functional Dependencies and Normalization

Data analytics (Ιnstructor Prof. Dr. Kyriaki Tsilika)
• Visual analytics
Basic plotting and visualization. Fundamental concepts for developing intuitive interactive visualizations. A variety of visualization techniques will be covered, including interactive representations, visual reports, web-based visualizations.
• Essential data handling methodologies in R
This part introduces computer programming in the R language focused on the management of data for analysis. Setting up for Data Analytics: loading, cleaning, initial exploration, saving and preparing data for in-depth analysis. Implement Data Analytics in practice: distribution fitting, regression, correlation metrics, inferential statistics.

Instructional method
Lectures and hands-on training
Hands-on exercises and relevant materials will be provided for participants to try out the applications, and to experiment with setting up their own relational databases and analytical reasoning in reports.

Required course materials
Technical prerequisites: Participants will need a desktop/laptop. Free software should be installed. Software installation and downloads can be provided live during the course for participants that bring their PC.

Assessment
Project-based assessment and online quizzes