Overview
A foundation in accounting analytics with an emphasis on data preparation, visualisation, and analysis using common data analytics tools such as Excel, Power BI and SPSS.
Accounting analytics involves evaluating accounting and related data to address business questions, and support evidence informed decision making using analytical tools, statistical methods and decision processes. The aims of this course are (a) to equip students with an understanding of how financial and non-financial data can inform accounting and business decisions and (b) to develop analytical skills and techniques in order to prepare, analyse, interpret, and report on valuable insights in an accounting and auditing context. There are various software packages that can help but we will focus on tools based around Excel, PowerBi and SPSS. We will also briefly highlight alternative tools such as R, and Alteryx.
About this paper
| Paper title | Introduction to Accounting Analytics |
|---|---|
| Subject | Accounting |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $912.00 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- BSNS 115
- Recommended Preparation
- INFO 130 or one of (BSNS 112, STAT 110, STAT 115)
- Schedule C
- Commerce
- Notes
- May not be credited with ACFI299 passed in 2020 or 2021
- Eligibility
Some accounting, Excel, data wrangling and statistical knowledge is helpful. However, we can give motivated student access to resources to fill gaps they may have.
- Contact
- Teaching staff
- Paper Structure
In terms of the software skills development, the course is structured into three complementary modules, namely, Excel/ Power Query Module, Statistical Analysis Module and the Power BI Module.
The later part of the course develops interpretation, communication and presentation skills.- Teaching Arrangements
This course is computer lab based.
- Textbooks
Data Analytics for Accounting (2e) by V. Richardson, R. Teeter, and K. Terrell (McGraw Hill) (ebook accessed through the Otago Library resources).
Course resources are also provided on Blackboard.- Graduate Attributes Emphasised
Critical thinking, Information literacy, Independent learning, Specialist Business Knowledge, Written Communication, Oral Communication, Team work
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will be able to:
- Develop an accounting analytic mind-set
- Develop an understanding of the fundamentals of data and analytics and when it is appropriate to use these concepts
- Tidy data and data wrangling: find, extract, transform, and load accounting data into data analytics tools for analysis and reporting
- Demonstrate competence in using data analytics tools such as Excel, PowerQuery, PowerPivot, Power BI, Capital IQ, and SPSS (R) software to address accounting analytic problems
- Create dashboards using accounting related information
- Interpret and communicate (data story telling) the results of accounting related dashboards and analysis
- Apply statistical techniques to examine company and industry trends and unusual patterns in accounting
Timetable
Overview
A foundation in accounting analytics with an emphasis on data preparation, visualisation, and analysis using common data analytics tools such as Excel, Power BI and SPSS.
Accounting analytics involves evaluating accounting and related data to address business questions, and support evidence informed decision making using analytical tools, statistical methods and decision processes. The aims of this course are (a) to equip students with an understanding of how financial and non-financial data can inform accounting and business decisions and (b) to develop analytical skills and techniques in order to prepare, analyse, interpret, and report on valuable insights in an accounting and auditing context. There are various software packages that can help but we will focus on tools based around Excel, PowerBi and SPSS. We will also briefly highlight alternative tools such as R, and Alteryx.
About this paper
| Paper title | Introduction to Accounting Analytics |
|---|---|
| Subject | Accounting |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $937.50 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- BSNS 115
- Recommended Preparation
- INFO 130 or one of (BSNS 112, STAT 110, STAT 115)
- Schedule C
- Commerce
- Notes
- May not be credited with ACFI299 passed in 2020 or 2021
- Eligibility
Some accounting, Excel, data wrangling and statistical knowledge is helpful. However, we can give motivated student access to resources to fill gaps they may have.
- Contact
- Teaching staff
Dr Dinithi Ranasinghe
- Paper Structure
In terms of the software skills development, the course is structured into three complementary modules, namely, Excel/ Power Query Module, Statistical Analysis Module and the Power BI Module.
The later part of the course develops interpretation, communication and presentation skills.- Teaching Arrangements
This course is computer lab based.
- Textbooks
Data Analytics for Accounting (2e) by V. Richardson, R. Teeter, and K. Terrell (McGraw Hill) (ebook accessed through the Otago Library resources).
Course resources are also provided on Blackboard.- Graduate Attributes Emphasised
Critical thinking, Information literacy, Independent learning, Specialist Business Knowledge, Written Communication, Oral Communication, Team work
View more information about Otago's graduate attributes.- Learning Outcomes
Students who successfully complete the paper will be able to:
- Develop an accounting analytic mind-set
- Develop an understanding of the fundamentals of data and analytics and when it is appropriate to use these concepts
- Tidy data and data wrangling: find, extract, transform, and load accounting data into data analytics tools for analysis and reporting
- Demonstrate competence in using data analytics tools such as Excel, PowerQuery, PowerPivot, Power BI, Capital IQ, and SPSS (R) software to address accounting analytic problems
- Create dashboards using accounting related information
- Interpret and communicate (data story telling) the results of accounting related dashboards and analysis
- Apply statistical techniques to examine company and industry trends and unusual patterns in accounting