Diploma in Data Science

Admission Requirements

• Typical Age: 21+
• Prior Qualifications: Completion of Level 6 studies, such as a Bachelor’s degree in Computing, Information Technology, or a related field.
• Professional Experience: In certain cases, applicants with at least 5 years’ management experience may be considered.
• International Qualifications: Equivalent qualifications may include:
  • A Bachelor’s degree in a relevant discipline or extensive work experience in IT
• English Proficiency (for non-native speakers): IELTS 5, CEFR level B2, or equivalent.
• Additional Guidance: For assistance with qualification equivalency or language requirements, please contact our admissions office.

Structure of the Qualification

• Total Credits: 120 ECTS
• Modules: 9 mandatory units, worth between 8 and 15 credits each, providing advanced knowledge and skills in data science and analytics.

Core Modules

1. Exploratory Data Analysis (8 Credits): Introduces R and Python programming, data management, descriptive statistics, and data visualisation.
2. Statistical Inference (12 Credits): Covers statistical distributions, research hypotheses, parametric and non-parametric tests, and analysis of variance.
3. Fundamentals of Predictive Modelling (15 Credits): Explains the modelling process through multiple linear regression, assumption checking, and model validation.
4. Advanced Predictive Modelling (15 Credits): Develops models for categorical outcomes using logistic regression, along with generalised linear models, survival analysis, and Cox regression.
5. Time Series Analysis (15 Credits): Explores forecasting, stationarity, ARIMA models, and panel data regression.
6. Unsupervised Multivariate Methods (15 Credits): Covers data reduction and segmentation using principal component analysis, factor analysis, multidimensional scaling, and cluster analysis.
7. Machine Learning (15 Credits): Applies methods such as Naïve Bayes, support vector machines, decision trees, random forests, market basket analysis, and neural networks.
8. Further Topics in Data Science (15 Credits): Addresses text mining, interactive dashboards with Shiny, big data and Hadoop, artificial intelligence, and SQL.
9. Contemporary Themes in Business Strategy (10 Credits): Examines digital transformation, the strategic impact of big data and artificial intelligence, disruptive innovation, and ethics.

Explore the complete curriculum for this course to gain a detailed understanding of the topics covered, learning objectives, and key skills you'll develop. See the full curriculum below.

View Curriculum

Assessment and  Regulations 

Assessment Approach

Assessment is based on a combination of practical assignments, projects, and timed examinations, aligned with each unit’s learning outcomes. Assessments include:

• Written Assignments: Weekly formative assignments on technical skills using sample data, plus a literature review for the Contemporary Themes in Business Strategy unit.
• Exams: Formal timed exams testing learners’ technical knowledge.
• Projects and Case Studies: Two individual course projects based on real-world data analytics, requiring critical thinking and problem-solving.

Grading System

Grades are awarded as Pass or Fail.

Completion Requirements

Learners must complete all units and achieve the required pass mark to receive the full diploma. Awarding Body issues certificates to successful learners through their registered centres.

Professional Development and Progression

Progress to Higher-Level Study:

• Advance to a Level 7 and/or Level 8 Diploma.
• Complete a dissertation-only route to a Master’s degree at a Awarding Body partner university, where appropriate.

Employment Opportunities:

This qualification prepares learners for data and artificial intelligence roles, such as:

• Data Scientist
• Data Analyst
• Artificial Intelligence Specialist

Learners gain practical skills and insights applicable to leadership roles in the technology sector, with assignments designed to support career advancement in diverse computing fields.

Work-Based Learning:

Assignments are designed as realistic work-related tasks and draw on real organisations. Part-time learners can apply their learning in the workplace and draw on their own work experience, while full-time learners can take part in formal work placements or part-time employment.