Diploma in Data Science

Admission Requirements

• Typical Age: 16+
• Entry Qualifications: Applicants should possess Level 2 qualifications, such as GCSEs, or relevant experience in a related field.
• International Qualifications: For international applicants, equivalent qualifications include:
  • High school diploma or other recognised pre-university qualifications (e.g., Grade 12 in Asia, Senior Certificate in Africa, EQF Level 4 in Europe, or a high school diploma in the US/Canada).
• English Proficiency (for non-native speakers): IELTS score of 6.0 or equivalent.
• Additional Guidance: For assistance with qualification equivalency or language requirements, please contact our admissions office.

Structure of the Qualification

• Total Credits: 60 ECTS
• Modules: 14 mandatory units, worth between 3 and 9 credits each, providing a broad introduction to data science concepts and practices.

Core Modules

1. The Field of Data Science (6 Credits): Introduces the development of data science, big data, artificial intelligence, machine learning, and deep learning, along with their applications and challenges.
2. Python for Data Science (9 Credits): Teaches Python from the basics, including data types, data structures, control flow statements, and functions.
3. Creating and Interpreting Visualisations in Data Science (3 Credits): Covers good visualisation practice and how to create and interpret common charts using Python libraries.
4. Data and Descriptive Statistics in Data Science (6 Credits): Explores data types and how to calculate and interpret measures of centre, spread, symmetry, and joint variability.
5. Fundamentals of Data Analytics (3 Credits): Introduces analytics processes, data roles, the data ecosystem, data quality, and data transformation.
6. Data Analysis with Python (3 Credits): Uses Python tools such as Pandas to load, wrangle, clean, transform, and explore data.
7. Machine Learning Methods and Models in Data Science (3 Credits): Provides an overview of supervised, unsupervised, and reinforcement learning and their use cases.
8. The Machine Learning Process (3 Credits): Explains how to prepare data, select algorithms, evaluate models, and address class imbalance, bias, and variance.
9. Linear Regression in Data Science (3 Credits): Covers the theory, calculations, and evaluation of linear regression, including building models in Python.
10. Logistic Regression in Data Science (3 Credits): Explains logistic regression as a classification method and how to build, evaluate, and interpret models in Python.
11. Decision Trees in Data Science (3 Credits): Introduces decision tree theory, the ID3 algorithm, entropy, and information gain, with model building in Python.
12. K-means Clustering in Data Science (3 Credits): Explores this unsupervised algorithm, including choosing the number of clusters and building, visualising, and evaluating models in Python.
13. Synthetic Data for Privacy and Security in Data Science (6 Credits): Examines data privacy, anonymisation, differential privacy, and synthetic data, including creating anonymised and fake data in Python.
14. Graphs and Graph Data Science (6 Credits): Introduces graph theory, graph data models, the graph ecosystem, and graph algorithms.

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

Assessments include: Assessment is based on formative and summative written assignments, aligned with each unit’s learning outcomes. Assessments include:

• Formative Assignments: Assessing knowledge and understanding of technical skills using sample data.
• Summative Assignments: A single assessment for each unit after learning is complete, with questions covering each learning outcome and the relevant theory.
• Technical and Practical Tasks: Calculations and Python tasks that require learners to apply their knowledge and solve problems, where relevant to the unit.

Grading System

Grades are awarded based on the successful completion of unit-specific assessment criteria:

• Distinction: 70% and above
• Merit: 60%–69%
• Pass: 50%–59%
• Fail: 0%–49%

Completion Requirements

Learners must pass all units by meeting all required learning outcomes and assessment criteria. All assessments are marked internally by the approved centre and externally moderated by Awarding Body to ensure quality and fairness.

Professional Development and Progression

Progress to Higher-Level Study:

• Advance to our progression course, Level 4 Diploma in Data Science.
• Apply for entry to an undergraduate degree at a UK university.

Employment Opportunities:

This qualification prepares learners for entry-level data roles, such as:

• Junior Data Analyst
• Data Assistant
• Reporting Analyst
• Business Intelligence Assistant

Work-Based Learning:

• Practical exercises using sample data and Python within the modules support skill development and readiness for data-focused roles in various industries.