QUALIFI Level 3 Diploma in Data Science


The QUALIFI Level 3 Diploma in Data Science aims to offer learners a comprehensive introduction to data science. This Level 3 Diploma provides a modern and all-encompassing overview of data science, artificial intelligence, and machine learning. It covers the evolution of artificial intelligence and machine learning from their beginnings in the late 1950s to the emergence of the "big data" era in the early 2000s. It extends to the current AI and machine learning applications, including the associated challenges.

In addition to covering standard machine learning models like linear and logistic regression, decision trees, and k-means clustering, this diploma introduces learners to two emerging areas of data science: synthetic data and graph data science. Moreover, the diploma familiarizes learners with the landscape of data analysis and the relevant analytical tools. It includes introducing Python programming so learners can effectively analyse, explore, and visualize data and implement fundamental data science models.

  • Acquire the essential mathematical and statistical knowledge necessary for conducting fundamental data analysis.
  • Cultivate analytical and machine learning proficiency using Python.
  • Foster a solid grasp of data and its related processes, encompassing data cleaning, data structuring, and data preparation for analysis and visualisation.
  • Gain insight into the expansive data science landscape and ecosystem, including relational databases, graph databases, programming languages like Python, visualisation tools, and various analytical instruments.
  • Develop expertise in comprehending the machine learning procedures, including the ability to discern which algorithms are suited for distinct problems and to navigate the steps involved in constructing, testing, and validating a model.
  • Attain an understanding of contemporary and emerging facets of data science and their applicability to modern challenges
  • This course module prepares learners for higher-level Data science positions through personal and professional development. 
  • We will ensure your access to the first-class education needed to achieve your goals and dreams and to maximize future opportunities.
  • Remember! The assessment for the Qualification is done based on assignments only, and you do not need to worry about writing any exam.
  • With the School of Business and Technology London, you can complete the Qualification at your own pace, choosing online or blended learning from the comfort of your home.
  • Learning and pathway materials and study guides developed by our qualified tutors will be available around the clock in our cutting-edge learning management system.
  • Most importantly, at the School of Business and Technology London, we will provide comprehensive tutor support through our dedicated support desk. 
  • If you choose your course with blended learning, you will also enjoy live sessions with an assigned tutor, which you can book at your convenience.

Upon completing the QUALIFI Level 3 Diploma in Data Science, learners can advance their studies or pursue employment opportunities.

  • Data Analyst with an estimated average salary of £39,445 per annum
  • Business Intelligence Analyst with an estimated average salary of £40,000 per annum
  • Data entry specialist with an estimated average salary of £22,425 per annum
  • Database Administrator with an estimated average salary of £44,185 per annum

QUALIFI, recognised by Ofqual awarding organisation has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Leadership, Hospitality & Catering, Health and Social Care, Enterprise and Management, Process Outsourcing and Public Services. They are liable for awarding organisations and thereby ensuring quality assurance in Wales and Northern Ireland.

  • Outstanding tutor support that gives you supportive guidance all through the course accomplishment through the SBTL Support Desk Portal.
  • Access our cutting-edge learning management platform to access vital learning resources and communicate with the support desk team.
  • Quality learning materials such as structured lecture notes, study guides, and practical applications, which include real-world examples and case studies, will enable you to apply your knowledge. Learning materials are provided in one of the three formats: PDF, PowerPoint, or Interactive Text Content on the learning portal.
  • The tutors will provide Formative assessment feedback to improve the learners’ achievements.
  • Assessment materials are accessible through our online learning platform.
  • Supervision for all modules.
  • Multiplatform accessibility through an online learning platform facilitates SBTL in providing learners with course materials directly through smartphones, laptops, tablets or desktops, allowing students to study at their convenience.
  • Live Classes (for Blended Learning Students only)
  • Time-constrained scenario-based assignments
  • No examinations
  • The qualification has been intentionally designed to ensure accessibility without imposing artificial barriers that limit entry. To enrol in this qualification, applicants must be 18 years of age or older.
  • Admittance to the qualification will be managed through centre-led registration processes, which may involve interviews or other appropriate procedures.
  • Despite the presence of advanced mathematics and statistics in higher-level data science courses, encompassing subjects such as linear algebra and differential calculus, this Level 3 Diploma only requires learners to be comfortable with mathematics at the GCSE level. The diploma's mathematical and statistical concepts are based on standard mathematical operations like addition, multiplication, and division.
  • Before commencing the Level 3 Diploma in Data Science, learners are expected to meet the following minimum requirements: i) GCSE Mathematics with a grade of B or higher (equivalent to the new level 6 or above); and ii) GCSE English with a grade of C or higher (equivalent to the new level 4 or above).
  • Furthermore, prior coding experience is not mandatory, although learners should be willing and comfortable with learning Python. Python has been selected for its user-friendly and easily learnable nature.
  • In exceptional circumstances, applicants with substantial experience but lacking formal qualifications may be considered for admission, contingent upon completing an interview and demonstrating their ability to meet the demands of the capability.

Upon successful completion of the QUALIFI Level 3 Diploma in Data Science, learners will have several opportunities:

  1. Progress to QUALIFI Level 4 Diploma in Data Science: Graduates can advance their education and skills by enrolling in the QUALIFI Level 4 Diploma in Data Science, which offers a more advanced and comprehensive study of the field.
  2. Apply for Entry to a UK University for an Undergraduate Degree: This qualification opens doors to higher education, allowing learners to apply for entry to a UK university to pursue an undergraduate degree in a related field, such as data science, computer science, or a related discipline.
  3. Progress to Employment in an Associated Profession: Graduates of this program can enter the workforce and seek employment opportunities in professions related to data science, artificial intelligence, machine learning, data analysis, and other relevant fields.

These progression options provide learners with a diverse range of opportunities for further education, career advancement, and professional development in the dynamic and rapidly evolving field of data science

This suite of qualifications provides enormous opportunities to learners seeking career and professional development.

The highlighting factor of this qualification is that:

  • The learners attain career path support who wish to pursue their career in their denominated sectors;
  • It helps provide a deep understanding of the health and social care sector and managing the organisations, which will, in turn, help enhance the learner's insight into their chosen sector.

The qualification provides a real combination of disciplines and skills development opportunities. The Learners attain in-depth awareness concerning the organisation's functioning, aims and processes. They can also explore ways to respond positively to this challenging and complex health and social care environment. The learners will be introduced to managing the wide range of health and social care functions using theory, practice sessions and models that provide valuable knowledge. As a part of this suite of qualifications, the learners will be able to explore and attain hands-on training and experience in this field. Learners also acquire the ability to face and solve issues then and there by exposure to all the Units. The qualification will also help to

  • Apply scientific and evaluative methods to develop those skills.
  • Find out threats and opportunities.
  • Develop knowledge in managerial, organisational and environmental issues.
  • Develop and empower critical thinking and innovativeness to handle problems and difficulties.
  • Practice judgement, own and take responsibility for decisions and actions.
  • Develop the capacity to perceive and reflect on individual learning and improve their social and other transferable aptitudes and skills

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.

Learners are not allowed to make any request to interchange unit(s) once enrolment is complete.


Reference No : H/650/4951

Credit : 6 || TQT : 60

This unit provides learners with an introduction to the field of data science, tracing its origins from the emergence of artificial intelligence and machine learning in the late 1950s, through the advent of the "big data" era in the early 2000s, to its contemporary applications in AI, machine learning, and deep learning, along with the associated challenges.

Reference No : J/650/4952

Credit : 9 || TQT : 90

This unit offers learners an introductory approach to Python programming tailored for data science. It begins by assuming no prior coding knowledge or familiarity with Python and proceeds to elucidate Python's fundamentals, including its design philosophy, syntax, naming conventions, and coding standards.

Reference No : K/650/4953

Credit : 3 || TQT : 30

This unit initiates learners into the realm of fundamental charts and visualisations, teaching them the art of creating and comprehending these graphical representations. It commences by elucidating the significance of visualisations in data comprehension and discerns the characteristics distinguishing effective visualisations from subpar ones.

Reference No : L/650/4954

Credit : 6 || TQT : 60

The primary objective of this unit is to acquaint learners with the foundational concepts of descriptive statistics and essential methods crucial for data analysis and data science.

Reference No : M/650/4955

Credit : 3 || TQT : 30

This unit will enable learners to distinguish between the roles of a Data Analyst, Data Scientist, and Data Engineer. Additionally, learners can provide an overview of the data ecosystem, encompassing databases and data warehouses, and gain familiarity with prominent vendors and diverse tools within this data ecosystem.

Reference No : R/650/4956

Credit : 3 || TQT : 30

This unit initiates learners into the fundamentals of data analysis using Python. It acquaints them with essential concepts like Pandas Data Frames and Series and the techniques of merging and joining data.

Reference No : R/650/4956

Credit : 3 || TQT : 30

This unit initiates learners into the fundamentals of data analysis using Python. It acquaints them with essential concepts like Pandas Data Frames and Series and the techniques of merging and joining data.

Reference No : T/650/4957

Credit : 3 || TQT : 30

This unit explores the practical applications of various methods in addressing real-world problems. It provides a summary of the key features of these different methods and highlights the challenges associated with each of them.

Reference No : Y/650/4958

Credit : 3 || TQT : 30

This unit provides an introduction to the numerous steps and procedures integral to the construction and assessment of machine learning models.

Reference No : A/650/4959

Credit : 3 || TQT : 30

This unit offers a foundational understanding of simple linear regression models, a crucial concept for predicting the value of one continuous variable based on another. Learners will gain the capability to estimate the best-fit line by computing regression parameters and comprehend the accuracy associated with this line of best-fit.

Reference No : H/650/4960

Credit : 3 || TQT : 30

This unit introduces logistic regression, emphasizing its role as a classification algorithm. It delves into the fundamentals of binary logistic regression, covering essential concepts such as the logistic function, Odds ratio, and the Logit function.

Reference No : J/650/4961

Credit : 3 || TQT : 30

This unit offers an introductory exploration of decision trees' fundamental theory and practical application. It elucidates the process of constructing basic classification trees employing the standard ID3 decision-tree construction algorithm, including the node-splitting criteria based on information theory principles such as Entropy and Information Gain. Additionally, learners will gain hands-on experience in building and assessing decision tree models using Python.

Reference No : K/650/4962

Credit : 3 || TQT : 30

This unit initiates learners into unsupervised machine learning, focusing on the k-means clustering algorithm. It aims to give learners an intuitive understanding of the k-means clustering method and equip them with the skills to determine the optimal number of clusters.

Reference No : L/650/4963

Credit : 6 || TQT : 60

This unit is designed to introduce learners to the emerging field of data science, specifically focusing on synthetic data and its applications in enhancing data privacy and security.

Reference No : M/650/4964

Credit : 6 || TQT : 60

This unit offers a beginner-friendly introduction to graph theory, a foundational concept that underlies modern graph databases and graph analytics.

Delivery Methods

School of Business & Technology London provides various flexible delivery methods to its learners, including online learning and blended learning. Thus, learners can choose the mode of study as per their choice and convenience. The program is self-paced and accomplished through our cutting-edge Learning Management System. Learners can interact with tutors by messaging through the SBTL Support Desk Portal System to discuss the course materials, get guidance and assistance and request assessment feedbacks on assignments.

We at SBTL offer outstanding support and infrastructure for both online and blended learning. We indeed pursue an innovative learning approach where traditional regular classroom-based learning is replaced by web-based learning and incredibly high support level. Learners enrolled at SBTL are allocated a dedicated tutor, whether online or blended learning, who provide learners with comprehensive guidance and support from start to finish.

The significant difference between blended learning and online learning methods at SBTL is the Block Delivery of Online Live Sessions. Learners enrolled at SBTL on blended learning are offered a block delivery of online live sessions, which can be booked in advance on their convenience at additional cost. These live sessions are relevant to the learners' program of study and aim to enhance the student's comprehension of research, methodology and other essential study skills. We try to make these live sessions as communicating as possible by providing interactive activities and presentations.

Resources and Support

School of Business & Technology London is dedicated to offering excellent support on every step of your learning journey. School of Business & Technology London occupies a centralised tutor support desk portal. Our support team liaises with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receives a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and streamline all the support processes efficiently.

Quality learning materials made by industry experts is a significant competitive edge of the School of Business & Technology London. Quality learning materials comprised of structured lecture notes, study guides, practical applications which includes real-world examples, and case studies that will enable you to apply your knowledge. Learning materials are provided in one of the three formats, such as PDF, PowerPoint, or Interactive Text Content on the learning portal.

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