CA35P Business Data Analytics
- 6 Sections
- 33 Lessons
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1 Lesson
Topic 1
28 Lessons
- Introduction to Excel
- 2ND SEP : INTRODUCTION TO BDA COURSE OUTLINE (MR. LUSENEKA EVENING CLASS)
- 7TH SEP :TOPIC 1- INTRODUCTION TO MICROSOFT EXCEL (MR. LUSENEKA EVENING CLASS)
- 9TH SEP SUBTOPIC : WHAT IF ANALYSIS
- 11TH SEP: DATA ANALYSIS TOOLPACK
- 14TH SEP : SOLVER AND DATA CLEANING
- 16TH SEP SUBTOPIC: CELL REFFERENCE
- 18TH SEP SUBTOPIC : KEYBOARD SHORTCUTS
- 21ST SEP MON(EVENING CLASS) BY MR. LUSENEKA- SUBTOPIC: FORMULAS AND FUNCTIONS
- 23RD SEP WED(EARLY MORNING CLASS) BY MR. LUSENEKA - SUBTOPIC: FORMULA AND FUNCTIONS
- 23RD SEP WED( EVENING CLASS) BY MR. LUSENEKA - SUBTOPIC: FORMULA AND FUNCTIONS
- 25TH SEP FRI( EVENING CLASS) BY MR. LUSENEKA -SUBTOPIC: FORMULA AND FUNCTION 2
- 28TH SEP MON(EVENING CLASS) BY MR. LUSENEKA - SUBTOPIC: FINANCIAL FUNCTIONS 2
- 30TH SEP WED(EVENING CLASS) BY MR. LUSENEKA - SUBTOPIC: FINANCIAL MODELLING
- 2ND OCT FRI(EVENING CLASS) BY MR. LUSENEKA - TOPIC: FINANCIAL MODELLING 2
- 3RD OCT SAT(EVENING CLASS) BY MR. LUSENEKA - TOPIC: BDA IN FR(PREPARATION OF FINANCIAL STATEMENTS
- 5TH OCT MON(EVENING CLASS) BY MR. LUSENEKA - TOPIC: BDA APPS IN FR(PREPARATION OF F.S 2
- 7TH OCT WED(EARLY MORNING/ EVENING CLASS) BY MR. LUSENEKA - TOPIC: BDA APPS IN T.P(3.1)
- 9TH OCT FRI( EVENING CLASS) BY MR. LUSENEKA- SUBTOPIC: PREPARATION OF F.S 4
- 10TH OCT SAT(EVENING CLASS) BY MR. LUSENEKA - SUBTOPIC: SCENARIO ANALYSIS (REVISION)
- 12TH OCT MON(EVENING CLASS) BY MR. LUSENEKA- SUBTOPIC:PREPARATION OF F.S 5
- 14TH OCT WED(EVENING CLASS) BY MR. LUSENEKA - SUBTOPIC: GROUP ACCOUNT/PROFIT OR LOSS
- 16TH OCT FRI(EVENING CLASS) BY MR. LUSENEKA - SUBTOPIC: GROUP ACCOUNTING 2
- 19TH OCT MON(EVENING CLASS) BY MR. LUSENEKA - TOPIC: ANALYSIS OF FINANCIAL STATEMENTS
- 21ST OCT WED(EVENING CLASS) BY MR. LUSENEKA - TOPIC: FORECASTING OF F.S
- 23RD OCT FRI(EVENING CLASS) BY MR. LUSENEKA - TOPIC: FORECASTING OF F.S
- 24TH OCT SAT(EVENING CLASS) BY MR. LUSENEKA - FORECASTING OF F.S(CONTINUATION)
- 26TH OCT MON(EVENING CLASS) BY MR. LUSENEKA - TOPIC: BDA APPS IN FM
Topic 2
1 Lesson
Topic 3
1 Lesson
Topic 4
1 Lesson
Topic 5
1 Lesson
CA35P: Business Data Analytics 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.
PRACTICAL PAPER NO. CA 35P: BUSINESS AND DATA ANALYTICS
(COMPUTER-BASED EXAMINATION).
- NATIONAL HOURS: 240
Recommended tool: Excel, R
- 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 specialization areas of management accounting, audit, tax and public financial management.
- The paper will be attempted over three hours in a controlled, computerized environment (examination centers with computer laboratories).
- CONTENT
- 1.0 Introduction to Excel
- Utilizing 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
- 2.0 Introduction to data analytics
- The CRISP (cross-industry standard process for data mining) framework for data analyticsData concepts - conceptual, logical, physical data modelsStages in data lifecycle: identifying data sources, modeling data requirements, obtaining data, recording data, using data for making business decision, removing data
- 2.2 Big 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, Power BI, Tableau, MicroStrategy, others
- 2.4 Data visualization in Excel
- Definition of data visualization
- Benefits of data visualization
- Types of visualization; comparison, composition and relationships
- Qualities of good data visualization
- 3.0 Core application of data analytics
- Financial accounting and reportingPrepare financial statements; statement of profit or loss, statement of financial position and statement of cash flow for companies and groupsAnalyze 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
- 3.2 Financial 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 visualization and dashboards
- 4.0 Application of data analytics in specialized areas
- Management accountingEstimate cost of products (goods and services) using high-low and regression analysis methodEstimate price, revenue and profit marginsCarry out break-even analysisBudget preparation and analysis (including variances)Carry out sensitivity analysis and scenario analysis and prepare flexible budgets
- 4.2 Auditing
- 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
- 4.3 Taxation and public financial management
- Compute tax payable for individuals and companies
- Prepare wear and tear deduction schedules
- Analyze 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 visualization 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)
The recommended study and reading materials will be shared in the final version of the syllabus.
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