PRACTICAL PAPER NO. CA 35P: BUSINESS AND DATA ANALYTICS
(COMPUTER-BASED EXAMINATION).



Syllabus


NOTIONAL HOURS: 240


Recommended tool: Excel,R


R is a programming language and open-source software environment specifically designed for statistical computing and data analytics. It provides a wide range of tools and libraries for data manipulation, visualization, and statistical analysis. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and was first released in 1995.



Key features and characteristics of R for data analytics include:



  • Data Manipulation: R offers powerful tools for data cleaning, transformation, merging, and reshaping. The language's data manipulation capabilities are supported by packages like "dplyr" and "tidyr."
  • Data Visualization: R provides numerous packages for creating static and interactive data visualizations, allowing users to create various types of plots and charts to explore and present data effectively. Popular packages for data visualization include "ggplot2" and "plotly."
  • Statistical Analysis: R's foundation lies in statistics, making it a preferred choice for researchers and data analysts. The language offers a vast array of statistical functions and packages for conducting descriptive and inferential statistical analyses.
  • Machine Learning: R has several packages for machine learning tasks, making it suitable for building predictive models and conducting data-driven decision-making. Packages like "caret," "randomForest," and "xgboost" are commonly used in machine learning projects.
  • Community and Packages: R benefits from a large and active community of developers and statisticians, leading to an extensive collection of packages covering almost every area of data analysis and research. These packages are available through the Comprehensive R Archive Network (CRAN) and GitHub.
  • Reproducibility: R promotes reproducible research through the use of R Markdown and Sweave, which allow analysts to combine code, text, and visualizations in a single document, making it easier to share and reproduce analyses.
  • Integration: R can be integrated with other programming languages, such as Python and C++, and can interface with databases and web services, enabling data analysts to work with various data sources.
  • Free and Open Source: R is free to use and distributed under the GNU General Public License, making it accessible to a wide range of users.
  • Due to its statistical capabilities, flexibility, and the extensive community support through packages, R has become a popular choice for data analysts, statisticians, researchers, and data scientists involved in data analytics and data-driven decision-making.
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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


1.0 Introduction to Excel



2.0. Introduction to data analytics



3.0. Core application of data analytics



4.0. Application of data analytics in specialised areas



5.0. Emerging issues in data analytics






Business Data Analytics


Table of contents

Business Data Analytics - Past Papers