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The CBIT Associate Extended Diploma in Data Science (Level 5) is a comprehensive, industry-aligned course designed to equip you with the theoretical understanding and practical skills needed to thrive in the fast-evolving field of data science.
This course 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 course comprising twelve modules that can typically be completed in twelve to eighteen months.
The curriculum is designed around real-world applications and current industry trends, ensuring that you are well-prepared to handle complex datasets, extract insights, and make informed, data-driven decisions across various sectors.
This coursee offers a cohesive, end-to-end data science curriculum that blends foundational theory with hands-on practice, ensuring you graduate ready to extract actionable insights from complex datasets. You 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.
Full access for 24 months from enrolment.
The CBIT Advanced Extended Diploma in Data Science (Level 7) with Integrated Real-world Experience is designed for senior data professionals looking to strengthen their strategic data science expertise through hands-on, industry-relevant project work.
CBIT (the Centre for Business and Information Technology) creates industry-focused business and IT certifications aimed at building workplace-ready skills and advancing careers. Its business programmes carry recognition from the Chartered Management Institute (CMI), and selected IT programmes hold BCS Tech10 Accreditation from BCS — The Chartered Institute for IT.
CBIT also runs standalone certifications which, although not externally approved, go through robust standardisation and quality assurance to stay current with industry needs. Every certification is designed by experienced industry practitioners and passes through thorough internal quality checks, so each programme delivers on relevance and standard of delivery.
Key reasons to choose CBIT Certifications:
To enrol in the CBIT Associate Extended Diploma in Data Science (Level 5), learners must meet the following entry requirements.
Be 18 years of age and over.
Possess the ability to complete the Level 5 programme.
The course is offered in English. You must possess considerable competency in English.
For each module you study, you will complete a written assignment of 2,000–2,500 words and submit it online via your MyLearnDirect learning portal. Your submitted assignments will be assessed by a dedicated tutor who will provide feedback to support your progress.
You must complete all 12 modules, with a maximum of 960 hours of Total Programme Time (TPT).
Learners must request before enrolment to interchange unit(s) other than the preselected units shown in the SBTL website because we need to make sure the availability of learning materials for the requested unit(s). SBTL will reject an application if the learning materials for the requested interchange unit(s) are unavailable.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 areas, and the broader context of machine learning in data science.
Our online learning gives you the flexibility to study when and where you want, with rich online learning resources and study guides developed by our expert tutors to provide structure, keep you motivated, and strengthen your understanding of the subject.
If you want a high level of structured guidance and support but can't attend scheduled live classes, then online learning is the perfect study method. You study at your own pace and set your own flexible schedule. We are dedicated to offering extensive learning support to simplify your learning journey, so we provide the option to schedule online personal meetings with your tutor at your convenience.
Our simplified online learning facilitates you to achieve high career growth without stress and dilemmas.
We are committed to providing unwavering support throughout your educational journey. Our dedicated support team is a crucial link between tutors and learners, ensuring that guidance, assessment feedback, and additional study assistance are delivered promptly and effectively.
We focus on providing a sophisticated, flexible learning experience. Our courses are designed with 100% online learning so your studies fit around your full-time work and other commitments, letting you study from anywhere, at your own pace.
You'll also get dedicated tutor support, scheduled personal tutor meetings whenever you need them, and access to our Dedicated Support Desk portal to raise queries or request a call back.
Our online learning gives you the flexibility to study when and where you want, with rich online learning resources and study guides developed by our expert tutors to provide structure, keep you motivated, and strengthen your understanding of the subject.
If you want a high level of structured guidance and support without attending scheduled classes, then online learning is the perfect study method for you. You study at your own pace and set your own flexible schedule. Our simplified online learning helps you achieve high career growth without stress and dilemmas.
You can schedule online personal tutor meetings whenever you want, which will help you get the most out of your studies and provide guidance, support and encouragement. When it comes to support, we have a dedicated support desk team so you can raise queries, request tutor support, and ask for a callback whenever you need guidance or assistance.
You will have access to unlimited dedicated tutor support through our centralised Support Desk portal. You can schedule personal tutor meetings whenever needed and raise queries via the MyLearnDirect learning portal at any time.
What are the payment methods?
The fee can be paid in either of the following methods:
Debit Card/Credit Card
PayPal
Bank Transfer
Do you offer a monthly payment plan?
Yes. We offer flexible, low-cost and interest-free payment plans across all our courses and study methods, so you can spread the cost monthly and focus on learning.
Can I interchange the units?
As an approved centre, SBTL preselects the units for each course from those listed in the awarding body's specification, and the preselected units are shown on every course's details page.
If you'd like different units, you must request this before enrolment so we can confirm the learning resources are available. Once enrolment is complete, unit interchange requests can no longer be accommodated.
What are the steps I need to follow once I decide to join you?
What does the allocated tutor provide to me?
CBIT Data Science programmes run from Associate (Level 5) to Advanced (Level 7), each matched to different career positions and responsibilities. The key difference between options is the number of modules completed.
CBIT Associate in Data Science (Level 5)
Covers data analysis, machine learning, artificial intelligence, big data, statistical techniques and programming. Ideal for aspiring data scientists, IT professionals extending their expertise and business analysts using data for strategic decision-making.
Combining theory with hands-on applications and real-world projects, it prepares learners to analyse large datasets, derive meaningful insights and make data-driven decisions.
CBIT Advanced in Data Science (Level 7)
For learners seeking advanced expertise, covering deep learning, time series analysis, cloud computing and data mining. It builds both theoretical understanding and the practical skills to tackle complex data challenges and drive innovation.
With a focus on the latest industry trends and technologies, it prepares learners to lead in the field, whether advancing a career or moving into a data-centric role.
