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Strategic ProfessionalOptionsEntirely New Paper
SDS — Data Science Professional
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.
FormatSession CBE · 3 hours 15 minutes · 100 marks
Pass Mark50%
Legacy PaperNone — entirely new paper
Sections7 sections (A, B, C, D, E, F, G)
Current-syllabus equivalentOur paper mapping does not identify a current ACCA paper for SDS.
Transition Planner Exam Format
Section A: 1 × 50 marks · Section B: 2 × 25 marks · 3 hr 15 min · all compulsory · permitted reference materials provided
Key Changes vs Legacy Paper
- 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 Breakdown
A
Business Problem Identification and Data Sourcing
A1
Problem 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
B
Mathematical and Statistical Methods
B1
Quantitative 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
C
Programming for Data Science
C1
Programming
- 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
D
Data Science Modelling Techniques
D1
Modelling
- 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
E
Accounting and Finance Applications
E1
Applications
- 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
F
Governance, Ethics and Risk Management
F1
Governance 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
G
Data Storytelling and Communication
G1
Data 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