ACCA 2027 · Strategic Professional, options
ACCA SDS Data Science Professional
SDS Data Science Professional is a Strategic Professional (options)-level exam in ACCA’s 2027 qualification. It is an entirely new paper with no current-syllabus equivalent. It is a Session CBE, 3 hours 15 minutes, 100 marks exam with a 50% pass mark, and the first exams run from September 2027.
Key facts
- Level
- Strategic Professional (options)
- Format
- Session CBE · 3 hours 15 minutes · 100 marks
- Pass mark
- 50%
- Replaces
- None (entirely new paper)
- First exams
- September 2027 (session CBE, four sittings a year)
- Syllabus
- 7 sections (A, B, C, D, E, F, G), 7 subsections, 36 learning outcomes
- Employability module at this level
- Ethical, Sustainable Leadership
Overview
What SDS covers
SDS Data Science Professional is a completely new options paper with no legacy predecessor. It covers business problem identification, statistical and mathematical methods, programming evaluation (Python and SQL), data science modelling techniques, accounting and finance applications, governance and ethics, and professional data storytelling.
Exam format
| Part | Detail |
|---|---|
| Section A | 1 × 50 marks |
| Section B | 2 × 25 marks |
| Overall | 3 hr 15 min |
| Overall | all compulsory |
| Overall | permitted reference materials provided |
Key changes and notes
- Entirely new paper — no ACCA predecessor
- Covers Python and SQL evaluation (not writing from scratch)
- Machine learning: supervised (classification, regression) and unsupervised (clustering)
- Accounting and finance applications throughout (audit, tax, management accounting, finance)
- Mandatory governance/ethics component in Section A
- Data storytelling always assessed
Syllabus
SDS syllabus breakdown
Every section, subsection and learning outcome in the SDS Data Science Professional syllabus.
ABusiness Problem Identification and Data Sourcing
A1Problem identification
- Identify and define business problems suitable for data science approaches
- Determine data requirements and evaluate data sources
- Understand data collection methods and data pipelines
- Assess data quality and apply data cleansing techniques
BMathematical and Statistical Methods
B1Quantitative methods
- Apply descriptive statistics and probability distributions
- Apply hypothesis testing and confidence intervals
- Apply regression analysis: simple and multiple linear regression
- Apply time series analysis and forecasting methods
- Understand Bayesian inference and its applications
- These methods underpin the whole paper and can appear anywhere in the examination
CProgramming for Data Science
C1Programming
- Evaluate and interpret Python code for data manipulation and analysis
- Evaluate and interpret SQL queries for data extraction
- Understand data structures: arrays, dataframes, dictionaries
- Evaluate code outputs and identify errors
- One Section B question always tests Section C
DData Science Modelling Techniques
D1Modelling
- Apply supervised learning: classification (logistic regression, decision trees) and regression
- Apply unsupervised learning: clustering (k-means) and dimensionality reduction
- Evaluate model performance: accuracy, precision, recall, F1, ROC/AUC
- Understand overfitting, underfitting and cross-validation
- Evaluate the selection and application of appropriate models
EAccounting and Finance Applications
E1Applications
- Apply data science to audit analytics: anomaly detection, sampling, continuous auditing
- Apply data science to financial analysis: credit risk modelling, fraud detection
- Apply data science to management accounting: forecasting, variance analysis
- Apply data science to tax analytics and compliance
- Each Section B question scenario from a different Section E topic
FGovernance, Ethics and Risk Management
F1Governance and ethics
- Evaluate data governance frameworks and policies
- Assess ethical implications of data science: bias, fairness, transparency
- Evaluate privacy regulations and their impact (GDPR)
- Assess AI governance and responsible AI principles
- Evaluate model risk management
- Section A always includes a Section F requirement
GData Storytelling and Communication
G1Data storytelling
- Create effective data visualisations for different audiences
- Structure data-driven narratives and presentations
- Communicate insights from complex analyses to non-technical stakeholders
- Evaluate the effectiveness of data presentations
- A Section G requirement always appears somewhere in the paper
ACCA SDS: frequently asked questions
What is the ACCA SDS exam format?
SDS Data Science Professional is a Session CBE, 3 hours 15 minutes, 100 marks exam. Structure: Section A: 1 × 50 marks · Section B: 2 × 25 marks · 3 hr 15 min · all compulsory · permitted reference materials provided. Pass mark: 50%.
When is the first ACCA SDS exam?
September 2027. New Expertise and Strategic Professional exams start with the September 2027 session. The current syllabus’s last sitting is June 2027.
Which current ACCA paper does SDS replace?
None. SDS Data Science Professional is an entirely new paper in the 2027 qualification.
What changed in SDS compared with the current syllabus?
Entirely new paper — no ACCA predecessor. Covers Python and SQL evaluation (not writing from scratch). Machine learning: supervised (classification, regression) and unsupervised (clustering). Accounting and finance applications throughout (audit, tax, management accounting, finance).
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