The Postgraduate Diploma in Data Analytics provides learners with an in-depth, interdisciplinary understanding of the application of data analytics to solve real-world business and technological problems.
The programme combines the principles of data science, artificial intelligence, cybersecurity, and innovation management to prepare learners for the dynamic and evolving needs of data-centric industries.
The curriculum integrates professional, analytical, and technical skills with ethical considerations and global perspectives. Learners engage with statistical techniques, machine learning, big data technologies, and decision-making frameworks, underpinned by contemporary academic and industry practices. Practical components ensure graduates are industry-ready, with the competence to handle, analyse, and communicate data effectively
Eucléa Business School is a higher education institution that is a member of the Collège de Paris, specialised in business, technology and alternating management. With four campuses strategically located in Strasbourg, Metz, Mulhouse and Reims, Eucléa offers a complete range of training, ranging from Post-Bac to Bac+5 level. Our school is dedicated to pedagogical excellence and personal development. Euclea’s goal is to help you develop your skills, explore new perspectives and prepare for a bright future while fostering a student life rich in opportunities.
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Component No. |
Full Module Code |
Module Title |
Compulsory / Core / Optional |
NQF Level |
Credits |
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PGDA01 |
COM101 |
Managing Innovation and Computing |
Core |
Level 7 |
10 |
|
PGDA01 |
DAT105 |
Business Intelligence Systems |
Core |
Level 7 |
10 |
|
PGDA01 |
SEC121 |
Implementing and Managing Cybersecurity |
Core |
Level 7 |
10 |
|
PGDA01 |
SDU124 |
System development and Use Experience(UX) |
Core |
Level 7 |
10 |
|
PGDA01 |
AI105 |
Artificial Intelligence |
Core |
Level 7 |
10 |
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PGDA01 |
BDA109 |
Big Data Analytics |
Core |
Level 7 |
10 |
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PGDA01 |
ML123 |
Machine Learning |
Core |
Level 7 |
10 |
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PGDA01 |
DIV101 |
Data Insights and Visualisation |
Core |
Level 7 |
10 |
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PGDA0 |
BDA110 |
Business Data Analytics |
Core |
Level 7 |
10 |
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A. Knowledge and understanding |
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Learning outcomes |
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A1 |
Demonstrate critical understanding of the role of innovation, cybersecurity, and business intelligence in organisational computing. |
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A2 |
Evaluate statistical, algorithmic, and visualisation techniques for data-driven insight and evidence-based decision-making. |
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A3 |
Apply core and emerging data science and AI techniques, including machine learning and big data, within business contexts. |
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A4 |
Appraise and integrate cloud-based and enterprise-level tools for managing, analysing, and visualising structured and unstructured data. |
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Learning methods |
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Lectures and Seminars, Workshops and Labs, Case Studies and Simulations, Individual and Group Projects, Industry Guest Lectures, Online Learning and VLE |
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Assessment methods |
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Essays, case analyses, timed assessments, research reports |
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B. Intellectual/cognitive skills |
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Learning outcomes |
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B1 |
Analytical reasoning in business/data problems |
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B2 |
Ethical/legal frameworks in cybersecurity and AI |
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B3 |
Critical engagement with innovation and analytics strategy |
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Learning methods |
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Lectures and Seminars, Workshops and Labs, Case Studies and Simulations, Individual and Group Projects, Industry Guest Lectures, Online Learning and VLE |
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Assessment methods |
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Essays, case analyses, timed assessments, research reports |
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C. Practical and professional skills |
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Learning outcomes |
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C1 |
Statistical and visual analysis (Data Analysis & Visualisation) |
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C2 |
Machine learning implementation (Machine Learning) |
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C3 |
Big data tools like Hadoop/Spark (Big Data Analytics) |
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C4 |
Cybersecurity management (Cybersecurity) |
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Learning methods |
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Lectures and Seminars, Workshops and Labs, Case Studies and Simulations, Individual and Group Projects, Industry Guest Lectures, Online Learning and VLE |
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Assessment methods |
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Essays, case analyses, timed assessments, research reports |
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D. Key Skills |
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Learning outcomes |
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D1 |
Communication |
Communicate effectively in oral and written forms, including academic reports, presentations, and visual data interpretations, tailored to diverse audiences. |
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D2 |
Information Technology |
Use a variety of digital tools and platforms including data analytics software, databases, coding environments, and cloud services in solving real-world data problems. |
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D3 |
Numeracy |
Apply quantitative and statistical methods to analyse data, interpret trends, and generate valid conclusions using appropriate mathematical models. |
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D4 |
Problem solving |
Identify, investigate, and resolve complex problems using data-driven approaches, algorithmic thinking, and critical analysis. |
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D5 |
Working with others |
Operate effectively within teams, contributing to group objectives, resolving conflict, and collaborating in diverse professional and intercultural settings. |
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D6 |
Improving own learning and performance |
Demonstrate autonomy and reflective practice in setting learning goals, monitoring performance, and engaging in continuous professional development.
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Learning methods |
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Lectures and Seminars, Workshops and Labs, Case Studies and Simulations, Individual and Group Projects, Industry Guest Lectures, Online Learning and VLE |
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Assessment methods |
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Essays, case analyses, timed assessments, research reports |
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A Bachelor’s degree (2:2 or above) in Computing, Business, Engineering, Mathematics, or a related field.
English language proficiency equivalent to IELTS 6.0 or higher.
Recognition of Prior Learning (RPL) is available for candidates with a minimum of 5 years of industry experience in relevant domains.
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