Data Analytics for Accountants

Data analytics for accounting work, from question to decision: the analytics mindset and cycle, ERP data structures and data quality, profiling and reconciling extracts to the ledger, cleaning and standardizing data in spreadsheets, Power Query and Python, joining, reshaping and aggregating tables, SQL for the general ledger with window functions, CTEs and exception queries, describing skewed populations and diagnosing margin changes with price, volume and mix analysis, honest visualization and waterfall charts, dashboards on tested star schemas, regression, forecasting and classification, audit data analytics under PCAOB and AICPA standards including the technology-assisted analysis amendments, substantive analytical procedures, Benford analysis and journal entry testing, and data governance, US privacy law including section 7216, ethics and AI, following one invented distributor throughout.

ACCT-341 Year 3 3 credits 12 lessons · 5 h 50 min

Offered by HSM University · Accounting

This is the American version of this course. See the Canadian version.

What you'll learn

Frame accounting problems as specific analytical questions with hypotheses, and distinguish descriptive, diagnostic, predictive and prescriptive analytics.
Identify the sources and structures of accounting data, including master, transaction and log data in ERP tables, and assess data quality.
Profile data and reconcile extracts to the general ledger and other control totals before analysis.
Clean and standardize accounting data with repeatable, logged steps in spreadsheets, Power Query and Python, flagging rather than deleting exceptions.
Join, reshape and aggregate tables correctly, controlling for keys, cardinality and grain, and use anti joins to find exceptions.
Write SQL queries with joins, grouping, conditional aggregation, common table expressions and window functions to analyze a general ledger.
Describe skewed accounting populations and diagnose changes with ratios, drill-down and price, cost, volume and mix decomposition.
Design honest, accessible charts suited to the analytical question, including waterfall charts, and recognize misleading visuals.
Design dashboards with defined KPIs on a tested star schema, apply governance to them, and communicate findings that lead with the answer.
Build and evaluate regression, forecasting and classification models using holdout data, and explain their limits.
Apply audit data analytics under PCAOB and AICPA standards, including the technology-assisted analysis amendments, substantive analytical procedures, full-population tests, Benford analysis and journal entry testing.
Apply data governance, US privacy requirements including section 7216, professional ethics and AI risk controls to an end-to-end analytics engagement.

Course content

12 lessons · 5 h 50 min total · final exam

1 The analytics mindset: from question to insight 29 min · quiz
2 Accounting data: sources, structures and quality 30 min · quiz
3 Preparing data I: cleaning and standardizing 28 min · quiz
4 Preparing data II: joining, reshaping and aggregating 28 min · quiz
5 SQL for accountants I: querying the ledger 30 min · quiz
6 SQL for accountants II: window functions, CTEs and exception queries 32 min · quiz
7 Descriptive and diagnostic analytics: distributions, ratios and variances 28 min · quiz
8 Visualization principles for accounting data 26 min · quiz
9 Dashboards and communicating insights 30 min · quiz
10 Predictive analytics: regression, forecasting and classification 30 min · quiz
11 Analytics in audit: risk assessment, testing and anomaly detection 30 min · quiz
12 Governance, ethics and AI, with a capstone engagement 29 min · quiz
E Final exam 20 questions

Requirements

Recommended background. You can start any course at any time.

Description

Part of the Business stream, typically taken in year 3.