The programme aims to bridge the gap between academic education and the demands of the labour market in the ICT sector. Through the development of an innovative, flexible, and personalised training curriculum, the project seeks to equip students, graduates, and professionals with up-to-date skills in Data Analysis (DA), Machine Learning (ML), Statistics, Programming, and Complex Systems. The programme places particular emphasis on experiential learning and the practical application of knowledge in realistic scenarios and industry-relevant challenges, offering learners meaningful hands-on experience and strengthening their ability to meet the demands of today’s job market.

Learners will be equipped to pursue roles in the rapidly growing fields of Data Analytics and Machine Learning, as well as in related areas that require analytical and computational skills. They will gain the necessary qualifications for positions such as data analyst, machine learning engineer, data scientist, and for supporting roles in a variety of technological projects and environments. The programme is ideal for individuals seeking reskilling or upskilling opportunities as part of lifelong learning, actively supporting professional transition and career development. It is also suitable for beginners or individuals with a non-technical (non-STEM) background, providing them with the necessary tools to integrate analytical and computational techniques into their current work or to transition professionally into the ICT sector.

Upon completion of the course, participants will be able to describe and apply core concepts in statistics, machine learning, and complex systems, including fundamental techniques in data analysis, supervised and unsupervised learning, as well as key characteristics and methods for studying complex networks and systems. They will also be able to use tools such as R, Python, and their basic libraries (e.g., pandas, scikit-learn), as well as other tools, for data preprocessing, analysis, and visualisation, the application of appropriate models, and the evaluation and presentation of their results. Finally, they will be able to apply their knowledge to realistic scenarios and solve real-world problems by identifying appropriate techniques, either independently or collaboratively. They will be able to justify their choices and analysis, and effectively communicate their findings and the ethical implications to both technical and non-technical audiences.

LEAP is aimed at adult learners (18+), holding at least a secondary school leaving certificate, regardless of academic or professional background. Building on a strong interdisciplinary orientation — reflecting the rapid penetration of Data Analytics (DA) and Machine Learning (ML) across a wide range of sectors — the programme primarily targets university students, graduates, and professionals seeking to develop or enhance their digital and analytical skills. However, it is equally open to any adult learner, of any age, who meets the basic educational requirement (high school diploma) and is motivated to acquire new, in-demand competencies. The courses are introductory in nature and do not require prior specialisation. Their online, asynchronous format, combined with personalised and accessible pedagogical approaches, makes the programme suitable for a broad range of learning needs and experience levels, actively promoting inclusive lifelong learning.

Application Form can submit those who are/have:

• Adults (18+)
• High school diploma
• Access to Internet
• Personal email
• Basic computer skills

Selection is made on a first-come, first-served basis, until the maximum number of participants has been reached.

Remote (100% online), asynchronous learning

The programme includes three courses:

1. Data Analytics and Statistics
2. Machine Learning and applications
3. Complex Systems and applications

Each course awards a certificate worth 5 ECTS credits.
Participants may choose to attend any number/combination of the available courses and will be awarded the corresponding number of ECTS credits for each course successfully completed.

Data Analytics and Statistics (108 hours)

This course introduces learners to the fundamental principles of data analysis and statistics, combining theoretical grounding with hands-on practice using real-world data. Using the R programming language and the RStudio environment, participants learn how to process, analyse, and visualise data, applying techniques such as descriptive and inferential statistics, linear and logistic regression, correlation analysis, and dimensionality reduction. Moreover, the course covers essential principles of data management, ethical considerations, and personal data protection (GDPR), as well as foundational knowledge of cloud infrastructures and Big Data technologies (Hadoop, Spark). The course culminates in the development of a comprehensive data analysis project and results presentation, incorporating all taught stages of data processing, analysis, and visualization, with a focus on evidence-based decision-making and effective communication of conclusions to diverse audiences.

Machine Learning and applications (108 hours)

This course introduces participants to the core principles, techniques, and applications of Machine Learning, with an emphasis on both conceptual understanding and practical implementation. Learners use the Python programming language and its core libraries (such as pandas, NumPy, and scikit-learn) for data processing, analysis, and machine learning model development. The content covers a wide range of techniques: from foundational supervised learning algorithms (e.g., linear and logistic regression, SVMs, decision trees, random forests), to unsupervised learning methods (e.g., K-means clustering, PCA, Autoencoders). It then expands into more advanced deep learning topics, including neural networks, convolutional neural networks (CNNs) for image analysis, recurrent networks (RNNs, LSTMs, GRUs) for natural language processing, as well as modern techniques such as Explainable AI (XAI), GANs, Transfer Learning, and Transformers (including GPT models). Teaching is experiential and data-driven, with weekly hands-on programming exercises, and culminates in the development of a final, integrated project based on a realistic scenario aligned with the current demands of the job market. Through practical, real-world activities, learners develop the ability to analyse and interpret results, select appropriate techniques, formulate well-reasoned solutions, and evaluate model performance. They will also strengthen their ability to effectively communicate findings and recommendations to both technical and non-technical audiences, enhancing both their technical and communication competencies. Particular emphasis is placed on the ethical dimension and interpretability of models, equipping students with the knowledge needed for the responsible use of Machine Learning in complex, real-world contexts.

Complex Systems and applications (96 hours)

This course introduces learners to the fundamental concepts and methodologies of complexity science, exploring how the interactions between individual components of a system give rise to the system’s overall behaviour. Starting with the shift from the reductionist approach of classical science to the systems-oriented perspective of complexity, the course covers key concepts such as chaos, bifurcations, fractals, and sensitivity to initial conditions. Students will examine the core characteristics of complex systems, such as self-organisation, adaptability, and emergence, along with the foundations of graph theory and network analysis. Through real-world examples (e.g. social networks, ecosystems, smart cities, infrastructure systems, IoT), they will apply techniques from Data Analysis, Machine Learning, and Complexity Science to analyse, model, and explain the behaviour of such systems. This module serves as the culmination of the programme, treating complex systems as a source of real-world problems and scenarios that learners are expected to tackle. In doing so, they will synthesise and apply the knowledge and skills acquired in the two previous modules (Statistics/Data Analysis and Machine Learning), as well as those gained in this final one, using methodologies from all the relevant fields.

Course material (text-based):

• Lecture notes, presentation slides (e.g., PowerPoint or PDF), and e-books.
• Fully worked step-by-step exercises.
• User guides and tutorials for software, programming environments (e.g., Python, R), or platforms used in the courses.
• Schematics, diagrams, flowcharts, and images to support visual learning.
• External resources, such as academic articles, blog posts, or written material created by guest lecturers or industry professionals.

Audio-visual material:

• Pre-recorded lectures developed by the course instructors.
• Guest lectures or insights from industry professionals, offering real-world context and connections to the labour market.
• Screen recordings or software demonstrations illustrating the use of tools, interpretation of results, or code development.

Supportive and Exploratory Resources:

• Curated lists of external content (e.g., videos, articles, podcasts, websites) allowing learners to dive deeper into topics or explore different perspectives.
• Simulations and interactive environments that can be embedded at various points in the course in different ways, promoting active engagement with concepts and methods through visualisation, experimentation, and hands-on exploration—thus supporting deeper and more meaningful learning.
• Virtual office hours, either synchronous (in real time) or asynchronous (via discussion boards), offering direct access to instructors and opportunities for questions and guidance.

For Practice, Consolidation, and Self-assessment:

• Quizzes
• Hands-on coding exercises
• Experiential and collaborative activities (e.g., participation in forums, debates, peer review, peer support, pairing, as well as case-based, problem-based, inquiry-based, project-based, or research-based activities), aiming to:
  • Bridge theory and practice
  • Provide opportunities for practical training and simulation of real working environments
  • Develop collaborative skills
  • Strengthen critical thinking and problem-solving abilities

Upon successful completion of each course, participants will receive a Certificate of Training, corresponding to 5 ECTS credits.

Upon successful completion of all three courses, participants will be awarded a combined Certificate of Training worth 15 ECTS credits.

Obligations of Trainees:

1. Participation in the programs of K.E.DI.BI.M. of the AUTH implies full acceptance of the program’s Study Guide and the Internal Regulation of Operation of the K.E.DI.BI.M. of the AUTH.
2. The presence/attendance of trainees in the educational programmes is mandatory. In the programmes which are realized in physical presence, as in those implemented using the modern method of tele-education, the study is generally mandatory and the limit of absences cannot exceed 10% of the prescribed training hours. The monitoring of asynchronous education is implemented according to the study schedule set by each programme.
3. For the successful completion of the programme, the participants should:                not exceed 10% absences of the scheduled training hours,  have passed the exams

Rights of Trainees:

1. Trainees are informed by the Department of Administrative Support of K.E.DI.BI.M. for all kinds of information related to the operation of the Centre.
2. Trainees are supported electronically through the educational platform by the trainers, as part of the educational process, to resolve questions and provide clarifications related to the thematic units of the programme.
3. Students’/Trainees’ work is protected under the Copyright Regulations/Provisions.

After successful completion of the program, participants receive a Certificate of Training, which is issued by the Center for Education and Lifelong Learning of the AUTH and signed by the President of the Center.

Participants, who have attended but not successfully completed the program, will be awarded a Certificate of Attendance.

“Upon successful completion of each course, participants will receive a Certificate of Training, corresponding to 5 ECTS credits.

Upon successful completion of all three courses, participants will be awarded a combined Certificate of Training worth 15 ECTS credits.

For further information, please contact Dr Maria Tsouchnika at leaperasmusplus@gmail.com