Business Analysis: Business Modeling and Decision Making Based on Regression Analysis and Optimization

Course 13: Business Analysis: Business Modeling and Decision Making Based on Regression Analysis and Optimization

I. Course Description

Analysis is the practice of using data insight and other quantitative approaches to enhance decision-making. From a business perspective, it is very important for mature companies to continuously optimize their business operations. Companies can use analytics to systematically evaluate the performance of assets, products, and operations teams to maintain a healthy path to increased profitability. In general, the field of business analysis provides policymakers and analysts with the tools to analyze and improve company operations.

This course will let students system learning how to use the way of data analysis to business analysis, from detailed data analysis plan, guide students to data collection as the breakthrough point, take the scientific method of data processing, data-driven oriented, evaluation enterprise research and development ability, operation ability, found and according to the results of data analysis, put forward the corresponding Suggestions, to deal with the challenges in business activities in the real world.

II. Professor Introduction

Cosimo Arnesano – Professor of the University of Southern California

Professor Cosimo Arnesano is a knowledgeable and multidisciplinary scientist and scholar in the fields of biology and biochemistry, physics, optics, statistics and data science, electronics, biomedical imaging, and business, project and operational management. He has a PhD in energy and environmental engineering, a PhD in biomedical engineering and an MBA in business administration. He has held key positions in ThermoFisher Scientific, Zeiss Microscopy and other companies, and has rich industry experience.

III. Syllabus

1. Review of statistical bases: data type and hypothesis testing
2. Correlation analysis and regression analysis
3. Advanced regression analysis: multiple regression and virtual variables
4. Linear optimization: linear planning and its diagram method
5. Advanced optimization technology
6. Decision analysis method: the application of decision tree
7. Monte Carlo simulation: Risk analysis and application
8. An Overview of Big Data and Data Science
9. Machine learning and AI foundation: classification model and neural network
10. 10 Practical application of machine learning and AI in business