Financial Statement Analysis: A Data Analytics Approach: 2024 Release ISE
1266133070
·
9781266133077
Financial Statement Analysis: A Data Analytics Approach introduces students to financial statement analysis (FSA) topics while developing the key data analytic skills in demand by today’s employers. Using an FSA-oriented framework and the AMPS mode…
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Ever thought about teaching financial statement analysis with a data analytics twist? We’re thrilled to introduce our new text, Financial Statement Analysis: A Data Analytics Approach. Watch this recording by our US colleagues with authors Bob Resutek and Vern Richardson as they walk through the highlights of the text, featuring labs using Excel and Tableau in McGraw Hill Connect®.
Hear more from Vern on the labs
McGraw Hill author and award-winning educator Vern Richardson shares his interactive approach to teaching data analytics. Through immersive labs using Excel, Tableau, Power BI, and Gen AI tools like ChatGPT, he demonstrates how students can go beyond the basics to develop career-ready skills.
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1 Contemporary Financial Statement Analysis and the Need for Data Analytics
2 Using Financial Statements
3 Alternative Data Sources
4 Financial Ratio Analysis
5 Accounting Quality and Working Capital Analysis
6 Evaluation of Noncurrent Assets and Liabilities to Assess Balance Sheet Quality
7 Analysis of Cash Flows
8 Forecasting Pro Forma Financial Statements
9 Introduction to Equity Valuation
10 Quantitative and Qualitative Sensitivity Analysis to Assess Forecast Assumptions
2 Using Financial Statements
3 Alternative Data Sources
4 Financial Ratio Analysis
5 Accounting Quality and Working Capital Analysis
6 Evaluation of Noncurrent Assets and Liabilities to Assess Balance Sheet Quality
7 Analysis of Cash Flows
8 Forecasting Pro Forma Financial Statements
9 Introduction to Equity Valuation
10 Quantitative and Qualitative Sensitivity Analysis to Assess Forecast Assumptions
Financial Statement Analysis: A Data Analytics Approach introduces students to financial statement analysis (FSA) topics while developing the key data analytic skills in demand by today’s employers. Using an FSA-oriented framework and the AMPS model, students learn to understand how data can be used to analyze financial statements. This is done through a balanced approach, centered on both performing data analysis and data-driven inferences. Data analytics labs ask students to think critically, bring together their knowledge of FSA and data analytics to ask and answer questions and present their findings to decision-makers. Paired with Connect this title offers the most robust platform support than any other FSA text on the market. It includes 40+ labs in Excel and Tableau with lab help videos that provide step-by-step instructions walking students through analysis tasks, lecture videos designed to reinforce learning objectives and important chapter concepts, auto-graded assessment, and SmartBook.