Tips for Teaching Data Analytics

Many resources are available to help you and your students learn this vital topic.

September 21, 2021

By Cheryl Meyer

In March 2021, EY announced the 2021 Better Working World Data Challenge, a global competition tasking 8,500 students and young professionals to create systems to help "streamline the process of bushfire mapping for bushfire authorities". This project focused on wildfire management required the use of advanced analytic tools and data science, and it drew competitors from China, France, Ghana, Poland, and 111 other countries. Winners were announced this summer.

This competition — launched by one of the Big Four accounting firms — marked just one example of how data analytics is a hot topic among university students. It's also a reflection of what's going on professionally, in both accounting and finance, where data analytics has grown substantially in prominence over the past several years.

Data analytics skills “are very necessary nowadays in audits, so there's a big demand" for graduates with such skills, noted Michael Werner, Ph.D., an associate professor in accounting and information systems at the University of Amsterdam in the Netherlands. Last year, the university hired Werner, an AIS expert and former senior consultant and operations manager at PwC, specifically to teach data analytics and reshape the school's focus on this growing area.

In response to the rising importance of data analytics, professional bodies have also taken steps to help educators incorporate the topic into accounting and finance curricula. In 2019, CIMA made data analytics part of the CGMA Finance Leadership Program and the CIMA syllabus. In the US, data analytics was integrated into the CPA Exam and the CPA Evolution Model Curriculum.

Faculty face several challenges when teaching data analytics. They may wonder how to fit this topic into their already-packed courses. They may also struggle to find the time to learn data analytics software with adequate proficiency to teach it to their students.

Fortunately, many resources have surfaced in the past couple of years to assist faculty with teaching this skill. Professors with experience in teaching data analytics offer the following tips for bringing this topic into your classroom:

  • Learn data analytics yourself. There are many formal and informal ways to learn about data analytics and become conversant with different types of software. Luz Parrondo, Ph.D., a professor of finance and accounting and director of the Finance and Control Department at the UPF Barcelona School of Management in Spain, is taking a Python course at her school and with her students. You can also find many courses online. "Google any of these [software] packages and you will find them," she said.

Faculty can also learn the topic by attending professional workshops or by co-teaching with a more experienced colleague, Werner suggested. Ideally, he noted, at least one faculty member per department should be “willing to get expertise in the data analytics area". That person can then spearhead a departmental effort to incorporate data analytics into the accounting and finance curriculum, as Werner is doing in his new role.

  • Tap online resources. Accounting and finance faculty can utilise many online sites to help them teach data analytics. Sources such as, Google Finance, Google Trends, Crunchbase, LinkedIn Learning, and the World Bank are good places to start, advised Kelvin Leong, CPA (Hong Kong), Ph.D., a professor of financial technology and data analytics at the University of Chester in the UK. Leong's students download data from these sources and then use various techniques to analyse the information "according to data type or needs," he said. Students thus "experience how to fulfil different information needs” and can compare the techniques they used, he noted.

The University of Illinois – Deloitte Foundation Center for Business Analytics makes data analytics teaching resources available to faculty (free registration required). Faculty can also find data analytics materials on the AICPA Academic Resource Database (free registration required).

Professors and students can also download free data to analyse from sites in the UK and Germany, or pay for downloads on sites in France, Spain, and Italy, among other countries, Parrondo said.

  • Choose your software. Parrondo, Werner, and Leong all use different technologies to teach students how to analyse data. Parrondo's students use Excel primarily, for individual analysis, though some master's students go on to more sophisticated tools. Werner's students employ Microstrategy, a type of business intelligence software; Rapid Miner, a type of data-mining software; and Disco, a process mining tool, he said. And Leong's students become skilled in Python, a sophisticated programming language with built-in data structures, in addition to SQL, another programming language designed for managing data.

Today's tools, Werner noted, have become so user friendly that many students can teach themselves how to use them.  

  • Explain why data analytics matters. It is vital, Werner said, "to illustrate to the students how data analytics relates to accounting and auditing". He invites auditors from the Big Four firms, along with data analytics software company representatives, to address his classes. "Guest speakers show [students] that what we teach is indeed important and used in practice," he said.

Parrondo downloads companies’ financial statements and analyses them in class, explaining how the data reveals organizations' strengths and weaknesses, along with their potential for future growth. She stresses the relevance of data analytics by having students review these and other real-world examples. Students download filings on companies they choose, often on the US Securities and Exchange Commission's website. Then they research the financial data and ask specific questions related to investment decision-making, potential earnings management, or fraudulent actions. Students answer their own questions as well, with her guidance. This helps them "understand the whole process" of analysing data, she said.

  • Don't overwhelm students. Leong advocates "micro-learning", or teaching students small bits of data analytics at a time so they "can obtain instant achievement", he said. He advises introducing one concept for every two-hour section, and having students work on one thing only, so they do not get overwhelmed. "Learning and programming involves progression of knowledge of one skill to another skill," he said.

Cheryl Meyer is a freelance writer based in California. To comment on this article or to suggest an idea for another article, contact senior editor Courtney Vien at