The CBIT Associate Extended Diploma in Data Science is a comprehensive, industry-aligned programme designed to equip learners with the theoretical understanding and practical skills needed to thrive in the fast-evolving field of data science.
This programme covers a wide range of topics, including data literacy, statistical foundations, programming, artificial intelligence, machine learning, deep learning, big data, and data mining.
The CBIT Associate Extended Diploma in Data Science Management is a comprehensive programme that can typically be completed in twelve to eighteen months.
The curriculum is designed around real-world applications and current industry trends, ensuring that learners are well-prepared to handle complex datasets, extract insights, and make informed, data-driven decisions across various sectors.
Aim of the Programme
The CBIT Associate Extended Diploma in Data Science aims to develop learners into competent data science practitioners capable of applying theoretical knowledge to real-world data problems.
The programme blends foundational concepts with advanced methodologies, including data wrangling, statistical analysis, AI, machine learning, and programming.
By the end of the programme, learners will be capable of performing end-to-end data science workflows, from data acquisition and cleaning to analysis, interpretation, and visualisation, with a strong emphasis on ethical practices and industry relevance.
Who is it for?
This programme is ideal for aspiring data scientists and analysts looking to start a career in data science, IT professionals aiming to upskill or transition into data-driven roles, business analysts and decision-makers seeking to leverage data for strategic insights, students and recent graduates in computer science, mathematics, or related fields, and career changers from non-technical backgrounds who are passionate about working with data.
Why Study CBIT Associate Extended Diploma in Data Science (Level 5)
This programme offers a cohesive, end-to-end data science curriculum that blends foundational theory with hands-on practice, ensuring learners graduate ready to extract actionable insights from complex datasets. Learners will study critical domains, including data collection and cleaning, statistical inference, machine learning, deep learning, big-data architectures, and data visualisation alongside essential ethical and governance considerations, completed over a period of 8 to 12 months.
Ideal for aspiring data scientists, IT professionals expanding into analytics, and business leaders seeking to make data-driven decisions, this Extended Diploma combines academic rigour with real-world problem-solving. Through case studies, and lab exercises, you’ll learn not just how algorithms work, but when and why to apply them, and how to communicate results to stakeholders.
Upon completing this programme, learners will be able to:
- Demonstrate mastery of core data science principles—from probability and statistics to neural networks and big-data processing.
- Execute end-to-end data workflows: acquire, clean, explore, model, validate, and visualise data using industry-standard tools (Python, SQL).
- Design, train, and critically evaluate machine learning and deep learning models for tasks such as classification, regression, clustering, and time-series forecasting.
- Develop interactive dashboards and visual stories that translate complex analyses into clear, actionable insights for non-technical audiences.
- Implement data governance best practices: ensure data quality, address privacy and bias, and adhere to ethical and regulatory standards in every project.
- Optimise data pipelines and algorithms for performance and scalability on high-performance and cloud-based platforms.
- Apply natural language processing and text-mining techniques to extract meaning from unstructured data sources.
Programme Benefits
- Gain a solid understanding of key data science principles, tools, and methods.
- Build hands-on skills in data wrangling, analysis, programming, and database management.
- Learn advanced topics including AI, deep learning, big data analytics, and machine learning.
- Develop strong visualisation capabilities to communicate complex findings effectively.
- Apply data mining and text analytics techniques to extract actionable insights.
- Understand ethical, privacy, and data governance challenges in real-world scenarios.
- Become proficient in data structures, algorithms, and statistical reasoning.
- Be prepared for further academic study or employment in data science-related fields.
Career Pathways
Learners who successfully complete the CBIT Associate Extended Diploma in Data Science (Level 5) may pursue various career paths, including but not limited to:
- Data Analyst—Translate business questions into data queries, analyses, and dashboards.
- Junior Data Scientist—Develop predictive models and validate algorithmic solutions.
- Machine Learning Engineer (Entry-Level)—Implement and optimise ML pipelines in production environments.
- Business Intelligence Developer—Design data warehouses and reporting systems.
- AI/ML Research Assistant—Support advanced AI projects in academic or corporate R&D.
- Data Engineer (Junior)—Build and maintain data architectures, ETL processes, and big-data platforms.
Programme Level
The CBIT Associate Extended Diploma in Data Science is equivalent to Level 5 (Undergraduate Level). It equips learners with mastery-level expertise for executive roles or progression to postgraduate study.
CBIT adopts level descriptors that are consistent with those of other recognised awarding bodies and professional organisations. This approach clearly understands the depth of study and complexity associated with each certification level. Our modules are designed with precise learning outcomes and rigorous assessment criteria, ensuring that learners know what is expected of them. Additionally, these descriptors serve as helpful guides, highlighting the desired learning outcomes while allowing flexibility in the learning process.
Entry Criteria
There are no formal entry requirements to enrol in the CBIT Associate Extended Diploma in Data Science. However, CBIT expect learners to meet the following criteria.
- Learners should be 18 years of age or over.
- The CBIT Associate Extended Diploma is level 5 equivalent, and hence, the learners must be able to complete the programme at this level.
- Learners should have considerable competency in English.
Progression
CBIT programmes are designed to enhance learners' skills and knowledge. Upon successfully completing the CBIT Associate Extended Diploma in Data Science, learners may wish to continue their personal and professional development by exploring other CBIT programmes, such as the CBIT Advanced Data Science programmes, equivalent to Level 7.
Assessment
All assessments are employer-driven, relevant, practitioner-based, and appropriate for business needs.
Assessments consist of a written assignment and a reflective report for each module, offering an excellent opportunity for in-depth learning. There are no exams involved.
To succeed, learners must thoroughly review the assignment brief and fully understand the requirements before beginning their work. This process not only enhances understanding but also encourages personal reflection and growth. Ultimately, this approach enriches the learning experience and helps learners develop essential skills for the future.
To achieve a ' Pass ' for each module, learners must satisfactorily meet all the requirements specified for the assessment criteria and fulfil all the learning outcomes.
Grading Criteria
Grading will be applied to each module as well as to the overall certification.
- Distinction (D) 70% +
- Merit (M) 60-69%
- Pass (P) 40-59%
- Fail (F) 0-39%
Duration
The average amount of time that the learners should contribute to complete the CBIT Associate Extended Diploma in Data Science is provided below:
- 960 hours of Total Programme Time (typically 12 to 18 months) are needed to study the programme.
- Learners should spend considerable time for completing assessments.
Other Courses in the CBIT Associate Extended Diploma in Data Science (Level 5)
If learners are interested in exploring other programmes within the CBIT Associate in Data Science suite, please find them listed below. Choosing a programme that aligns with your career goals and aspirations can help you achieve the progression you desire.
- CBIT Associate Award in Data Science (Level 5)
- CBIT Associate Certificate in Data Science (Level 5)
- CBIT Associate Diploma in Data Science (Level 5)
Learners must complete all 12 modules, with a maximum of 960 hours of Total Programme Time (TPT).
Module : Data Science Literacy
Module code: CBIT- ADS -501
This Module provides learners with a foundational understanding of data science, focusing on core concepts, methods, and tools essential for working with data. The aim is to introduce learners to the data science pipeline, from data collection and processing to basic statistical analysis and visualisation. It also emphasises the importance of data quality and understanding how data can be interpreted to drive insights. By the end of the module, learners will be equipped with the theoretical knowledge necessary to understand how data science is applied in different industries.Module : Statistical Foundations for Data Science
Module code: CBIT- ADS -502
This module provides learners with a strong theoretical foundation in statistics and probability, essential for understanding data science applications. The aim is to introduce key statistical concepts, methods, and frameworks that underpin data analysis in a wide range of real-world contexts. Learners will develop the ability to apply theoretical concepts to statistical problems and interpret the results in the context of data science.Module : Machine Learning and Neural Networks
Module code: CBIT- ADS -503
This module aims to provide learners with an understanding of the fundamental principles of machine learning and neural networks, focusing on the theoretical concepts and methodologies used to extract patterns and predictions from data. Learners will explore key machine learning techniques, the mathematical foundations underlying these methods, and how neural networks are modeled based on biological learning systems. The module will enable learners to gain the knowledge necessary to apply machine learning theories to real-world data challenges in various industries.Module : Data Science Fundamentals
Module code: CBIT- ADS -504
The aim of this module is to provide learners with a comprehensive understanding of the fundamental concepts, tools, and techniques in data science. This module will introduce learners to the core data science pipeline, from data collection to processing, analysis, and visualisation, enabling them to apply statistical methods to real-world data. Learners will gain insights into the theoretical underpinnings of data science while exploring how these principles are applied across different sectors.Module : Artificial Intelligence and Big Data
Module code: CBIT- ADS -505
This module aims to introduce learners to the core principles of Artificial Intelligence (AI) and Big Data, focusing on the theoretical understanding of machine learning and data analysis techniques. Learners will explore how AI and Big Data technologies are applied in various sectors and gain knowledge of the challenges involved in handling and analysing large datasets.Module : Artificial Intelligence and Deep Learning
Module code: CBIT- ADS -506
This module aims to introduce learners to the fundamental concepts of Artificial Intelligence (AI) and Deep Learning (DL). Learners will explore the theoretical underpinnings of AI models and deep learning architectures, focusing on how these models are structured, trained, and applied to solve real-world problems. The module will also examine ethical considerations in AI and deep learning.Module : Introduction to Programming & Database Management System
Module code: CBIT- ADS -507
This module introduces students to core programming concepts, data structures, and database management techniques, with a focus on developing secure and efficient software solutions. It also explores key concepts of database management systems (DBMS), data modeling, and the essential security considerations required when handling data in an organisational context.Module : Data Analysis and Visualisation
Module code: CBIT- ADS -508
This module aims to provide learners with a strong foundational knowledge of data analysis and visualisation techniques. Students will understand the role of data analysis in industry and research, learn to handle data preprocessing, and apply visualisation techniques to interpret data insights. Emphasis will be on theoretical applications and the conceptual underpinnings of data-driven decision-making.Module : Data Structures and Algorithms
Module code: CBIT- ADS -509
This module aims to equip learners with a foundational understanding of data structures and algorithms, focusing on theoretical aspects, complexity analysis, and the application of various algorithmic strategies. Through studying these topics, learners will gain insights into selecting appropriate data structures and algorithms for problem-solving, as well as analysing their efficiency and scalability.Module : Introduction to Probability and Statistics
Module code: CBIT- ADS -510
This module provides learners with foundational knowledge in probability and statistics, essential for understanding data analysis, inferential statistics, and decision-making under uncertainty. It aims to equip learners with the skills to analyse and interpret probabilistic data, understand statistical models, and apply various sampling and estimation methods in a theoretical context.Module : Data Wrangling and Exploration
Module code: CBIT- ADS-511
This module aims to provide learners with a foundational understanding of data wrangling and exploration processes, emphasising theoretical knowledge and techniques essential for transforming raw data into structured formats suitable for analysis. Learners will develop insight into the key stages of data acquisition, cleaning, structuring, and preliminary analysis, preparing them to understand the complexities of handling diverse data types and sources.Module : Data and Text Mining
Module code: CBIT- ADS -512
This module provides learners with a deep understanding of the theoretical foundations, concepts, and techniques involved in data and text mining. Learners will explore approaches to transform unstructured and semi-structured data into actionable insights. The module focuses on building knowledge of data-mining methodologies, natural language processing (NLP), feature extraction, and data indexing, while introducing learners to contemporary techniques for analysing complex datasets.Module : Applied Machine Learning
Module code: CBIT- ADS -513
This module provides learners with a theoretical foundation in machine learning concepts and methodologies. It focuses on understanding core algorithms, their applications, and the critical considerations involved in selecting and evaluating machine learning models. Learners will gain insights into supervised and unsupervised learning, key application areasThe programme is offered online through CBIT, creating a dynamic learning experience tailored to fit the unique lifestyles of each learner. With our flexible learning approach, individuals can engage fully in their studies from any location and at a pace that aligns with their personal needs and preferences. This adaptability fosters a more enriching educational journey.
The programme is also available through CBIT-approved partner centres. To learn more about our partner centres, reach out to us directly. We will help you find an approved partner centre that is conveniently located for you.
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- Duration
Typically, 12 to 18 months - Support Period
24 Months - Study mode
Online at CBIT or through a partner centre. - Certificate
CBIT Certificate on completion. - Dedicated, expert tutors
Unlimited Tutor Support - Support team
Dedicated student support team - Learning Resources
Structured Learning Resources - Assessment feedback
Comprehensive assessment feedback to improve learning.