Become an expert in Data Analytics with our

Postgraduate Diploma in

Data Analytics

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What distinguishes us ?
  • Personalized 1:1 Career Coaching and Mentorship
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  • Over 200 Hours of Guided Learning
  • Real-World Industry Projects and Case Studies
  • 15+ Tailored Industry Sessions for a Personalized Experience
  • Interview Preparation and Mock Interviews for Career Success
  • Hands-On Experience with Building Your Own Product (BYOP)
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  • Euclea• Pathway to WES CANADA AND UK NARIC Recognized, Equated to Canadian Masters and British bachelor’s degree.

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Britts Imperial University College

Britts Imperial University College (UAE) is renowned for its outstanding technology and business education. The College, with its exceptional offerings, has gained immense popularity as one of the most desirable institutions in the Middle East and beyond. With a student population of over 3000 students, Britts Imperial University college has established a solid reputation in the field of Education. The institution prioritises student placements and strives to make them more accessible by partnering with a wide range of organisations.

Eucléa Business School, in collaboration with Britts Imperial University College 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.

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Why Choose Us

Postgraduate Diploma In Data Analytics

The Postgraduate Diploma in Data Analytics provides a comprehensive exploration of essential concepts in the scope of data analysis and Big Data technologies. The curriculum reveals with a practical focus on data wrangling and cleaning, ensuring students master the intricacies of preprocessing, cleaning, and transforming data for accurate and reliable analysis.

The Postgraduate Diploma in Data Analytics is meticulously designed to arm students with the requisite knowledge and skills essential for success in the dynamic field of data analytics. This comprehensive program immerses students in a learning experience that emphasizes key areas, including statistical analysis, data wrangling, machine learning, and data visualization.


Career Opportunities

  • Business Intelligence Analyst
  • Data Scientist
  • Machine Learning Engineer
  • Big Data Engineer
  • Data Analyst
  • Database Administrator
  • Business Analyst

Program Objectives

Practical Data Management

Graduates with this program develop practical skills in data wrangling and cleaning, ensuring participants can preprocess and transform data accurately for meaningful analysis

Advanced Analytics

Students will explore advanced statistical methods and machine learning techniques, enabling participants to extract valuable insights and predictions from complex datasets.

Data Visualization

Visual data interpretation enhances students' analytical skills by assisting them in identifying patterns, trends, and correlations within datasets, which are crucial in both academic and real-world scenarios.

Big Data Proficiency

The objective aims to equip graduates with the ability to handle large and complex datasets using advanced Big Data technologies like Hadoop and Spark.


Course Structure

PGDIP – PG Diploma in Data Analytics (RNCP 36129)
Year BIG Code Course Code Course Name Credits
MSCC001 MICC Managing Innovation and Change in Computing 10 Credits
MSCC002 SDUE Systems Development and User Experience (UX) 10 Credits
MSCC003 IMCS Implementing and Managing Cyber Security 10 Credits
MSCC004 BIS Business Intelligence Systems 10 Credits
MSCC005 IP Independent Project 10 Credits
MSCC006 DABD Data Analytics and Big Data 10 Credits
MSCC007 CBRM Computer Based Research Methods 10 Credits
Total Credits (ECTS) 70 Credits
1.5K+ Active Students

Progression Pathways

Programme Name

MSc Computing

Year of Entry

2nd Year

Total Credits Required


Total Credits Earned


Progression Route

Postgraduate Diploma in Data Analytics

Awarding body

Euclea Business School,


Tools and Technologies


Programming Languages

  • Python : is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis.
  • R : is widely used in data science by statisticians and data miners for data analysis and the development of statistical software.

Data Analysis and Visualization Tools

  • Jupyter Notebooks : is a web-based interactive computing platform.
  • Tableau : it's can help anyone see and understand their data. Connect to almost any database.
  • Power BI : A business analytics service by Microsoft.

Statistical Analysis and Machine Learning Libraries

  • NumPy :can be used to perform a wide variety of mathematical operations on arrays.
  • pandas : is a data manipulation package in Python for tabular data.
  • scikit-learn : implement machine learning models and statistical modelling.
  • TensorFlow : end-to-end open source machine learning platform for everyone.

Big Data Technologies

  • Apache Hadoop :is a data processing engine for big data sets.
  • Apache Spark : is an open-source, distributed processing system used for big data workloads.

Database Management Systems

  • SQL : is a programming language for storing and processing information in a relational database.
  • MySQL : is a relational database management system based on SQL.
  • MongoDB : stores data as JSON documents in a more flexible format.

Data Wrangling and Cleaning

  • OpenRefine : working with messy data: cleaning it, transforming it from one format into another.
  • Trifacta : is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis.

Version Control

  • Git :Essential for collaborative development and code versioning.

Program Timeline


Frequently Asked Question

No prior experience is required. The program caters to individuals with varying levels of experience, including beginners. Foundational courses are included to accommodate those new to data analytics.
The program is designed to be flexible, offering both full-time and part-time options to cater to the diverse schedules and commitments of students.
Practical experience is integral to the program. Students engage in hands-on projects and a capstone project, applying learned concepts to real-world scenarios, ensuring practical proficiency.
The program covers industry-standard tools like Python, R, and Tableau. In most cases, access to these tools is provided as part of the program, eliminating the need for separate purchases.
The program undergoes regular updates to align with industry trends. Graduates typically have access to alumni resources, including updated content and webinars, to stay current with industry advancements.
Yes, the program offers specialized tracks to cater to various interests. Students can choose tracks such as healthcare analytics or financial analytics based on their career goals.
Absolutely. Networking opportunities, online forums, and collaboration with industry professionals are encouraged, fostering a supportive learning community.
Yes, mentorship programs and interactions with industry experts are often integrated, providing students with valuable insights and guidance.
Yes, the program provides a solid foundation for further education. Graduates can seamlessly transition into advanced degrees if they choose to deepen their knowledge in data science.