Forecasting and Forensic Analytics, FallĀ 2020

Forecasting and Forensic Analytics, Fall 2020

This course explores how data can be used to solve accounting problems across financial accounting, managerial accounting, and audit contexts. Students gain exposure to techniques for exploring how financial and non-financial data is used to forecast events, detect financial discrepancies and frauds, predict corporate default, optimize operations, and determine business strategy. The emphasis of this class is on problem solving, theory, and application, with additional emphasis on interpretation and communication. Some programming is required, but some programming help is provided via online tutorials and instructor-provided code. Some advanced analytics methods such as text analytics, neural networks and deep learning are discussed in the later weeks. This course has been designed to equip students with an analytics mind-set to develop analytics strategies and make better business decisions.

This class is partnered with Datacamp. For a list of required and recommended Datacamp classes, see this link.

Resources
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Class files
i1 Session 1: Review Interactive slides Print & mobile slides (pdf) R Practice
    In class: AI will Enhance us[…]    
i1 Session 1: R Supplement Interactive slides Print & mobile slides (pdf) R Practice
i3 Session 2: Linear regression Interactive slides Print & mobile slides (pdf) R Practice
    Student’s favorite cafes map    
i4 Session 3: Advanced Linear regression Interactive slides Print & mobile slides (pdf) R Practice
    In class reading Interactive slides on Walmart Print & mobile Walmart slides (pdf)
i5 Session 4: Logistic regression Interactive slides Print & mobile slides (pdf) R Practice
i6 Session 5: Logistic regression for bankruptcy Interactive slides Print & mobile slides (pdf) R Practice
    In class: Carillion’s liquidation In class: Altman 2000 In class: McKinsey supply-chain analytics
i7 Session 6: Logistic regression for fraud detection Interactive slides Print & mobile slides (pdf) In class: Sanofi’s 2018 AAER
i8 Session 7: Textual analysis Interactive slides Print & mobile slides (pdf) R Practice
    In class: NLP in Call Centers    
i9 Session 8: Topic modeling and anomaly detection Interactive slides Print & mobile slides (pdf) LDAvis visualization for STM
    STM Browser visualization with SIC codes    
i10 Session 9: Machine Learning and AI (Ensembles and Ethics) Interactive slides Print & mobile slides (pdf) In class: Four keys to avoiding bias in AI
    In class: Proactive policing    
i10 Session 10: ML and AI for numeric and text data Interactive slides Print & mobile slides (pdf) Shakespeare with TPU and Keras
    Semantic Similarity with USE    
i10 Session 11: ML and AI for images and video Interactive slides Print & mobile slides (pdf) Fashion MNIST
    Style transfer with your own images Music VAE R Code for CNN and AlexNet