BUSINESS AND DATA ANALYTICS(COMPUTER-BASED EXAMINATION)

What Will You Learn?

• NOTIONAL HOURS: 240
• Recommended tool: Excel, R
• UNIT DESCRIPTION
• This course is aimed at enabling the candidate to use information technology to support
• decision making through business analytics. The candidate is expected to demonstrate
• digital competency in preparation and analysis of financial statements, forecasting and
• related areas in data analytics.
• PREREQUISITE
• To attempt this paper, a candidate shall be required to have passed all other
• examination papers within the CPA qualification.
• Candidates will be required to have core knowledge of quantitative techniques, financial
• accounting and reporting and financial management. Candidates are also expected to
• have knowledge in their specialisation areas of management accounting, audit, tax and
• public financial management.
• The paper will be attempted over three hours in a controlled, computerized environment
• (examination centres with computer laboratories).
• 1.0 LEARNING OUTCOMES
• A candidate who passes this paper should be able to:
• - Discuss fundamental aspects of big data and data analytics from the CRISP (crossindustry standard process for data mining) framework, data visualisation and
• emerging issues.
• - Apply data analytics in preparation of financial statements, financial statements
• analysis and forecasting, carrying out sensitivity/scenario analysis and presenting
• financial data and metrics using dashboards.
• - Apply data analytics in financial management principles that include time value of
• money analysis, evaluate capital projects, carry out sensitivity/scenario analysis and
• present information using dash boards.
• - Apply data analytics in management accounting to estimate product costs,
• breakeven analysis, budget preparation, sensitivity/scenario analysis and flexible
• budgets.
• - Apply data analytics in auditing techniques including key financial trends, fraud
• detection, tests of control, model reviews and validation issues.
• - Apply data analytics in estimating tax payable and in public sector financial
• management.
• CONTENT
• 1.0Introduction to Excel
• - Utilising the keyboard shortcuts in Excel
• - Conducting data analysis using data tables, pivot tables and other common
• functions
• - Using advanced formulas and functions to enhance the functionality of financial
• models
• 2.0Introduction to data analytics
• 2.1The CRISP (cross-industry standard process for data mining) framework
• for data analytics
• - Data concepts - conceptual, logical, physical data models
• - Stages in data lifecycle: identifying data sources, modeling data
• requirements, obtaining data, recording data, using data for making business
• decision, removing data
• 2.2Big data and data analytics
• - Definition of big data
• - The 5Vs of big data
• - Types of data analytics: descriptive analytics, prescriptive analytics and
• predictive analytics
• 2.3 Tools for data analytics
• - Data cleaning tools (Alteryx, SSIS, Datastage, others)
• - Data Management (Storage/DBA): SQL, Oracle, Cloud Computing
• (AWS,AZURE), others
• - Reporting/Visualization : Excel, PowerBI, Tableau, Microstrategy, others
• 2.4Data visualization in Excel
• - Definition of data visualization
• - Benefits of data visualization
• - Types of visualization; comparison, composition and relationships
• - Qualities of good data visualization
• 3.0Core application of data analytics
• 3.1Financial accounting and reporting
• - Prepare financial statements; statement of profit or loss, statement of
• financial position and statement of cash flow for companies and groups
• - Analyse financial statements using ratios, common size statements, trend
• and cross-sectional analysis, graphs and charts
• - Prepare forecast financial statements under specified assumptions
• - Carry out sensitivity analysis and scenario analysis on the forecast financial
• statements
• - Data visualization and dash boards for reporting
• 3.2Financial Management
• - Time value of money analysis for different types of cash flows
• - Loan amortization schedules
• - Project evaluation techniques using net present value - (NPV), internal rate
• of return (IRR)
• - Carry out sensitivity analysis and scenario analysis in project evaluation
• - Data visualisation and dashboards
• 4.0 Application of data analytics in specialised areas
• 4.1Management accounting
• - Estimate cost of products (goods and services) using high-low and
• regression analysis method
• - Estimate price, revenue and profit margins
• - Carry out break-even analysis
• - Budget preparation and analysis (including variances)
• - Carry out sensitivity analysis and scenario analysis and prepare flexible
• budgets
• 4.2Auditing
• - Analysis of trends in key financial statements components
• - Carry out 3-way order matching
• - Fraud detection
• - Test controls (specifically segregation of duties) by identifying combinations
• of users involved in processing transactions
• - Carry out audit sampling from large data set
• - Model review and validation issues
• 4.3Taxation and public financial management
• - Compute tax payable for individuals and companies
• - Prepare wear and tear deduction schedules
• - Analyse public sector financial statements using analytical tools
• - Budget preparation and analysis (including variances)
• - Analysis of both public debt and revenue in both county and national
• government
• - Data visualisation and reporting in the public sector
• 5.0 Emerging issues in data analytics
• - Skepticism and challenges in data analytics
• - Ethical issues in data analytics
• - Data Security / Data Protection
• - Performance (Limitations within analytic tools)