Business Analytics

Professor Introduction

Q. Z | Ph.D.

Home Institute：Tsinghua University

Project Description

In the modern world, business models are increasingly driven by information, data analysis, and big data. Integrating business and data is crucial in corporate decision-making, value investment, strategic planning, and more. This project aims to help students gain a foundational understanding of business analytics, covering basic concepts and skills. Students will learn relevant mathematical models and industry research frameworks, develop problem-solving approaches, and gain programming skills. The content includes the characteristics of businesses in investment, consulting, strategy, and brokerage firms. The project will teach the basic framework and experience of industry research, and various enterprise valuation methods, and enable students to quickly familiarize themselves with different industries and independently write business research reports. This project is suitable for students from business-related and interdisciplinary backgrounds.

Project Keywords

Project Outline

Part 1:  Introduction to Business Analytics
•  Importance of business analytics in modern business models
•  Overview of key concepts and tools in business analytics
•  Introduction to the role of data in corporate decision-making and strategy

Part 2: Theoretical Framework and Mathematical Models
•  Overview of relevant mathematical models used in business analytics (e.g., regression analysis, optimization models)
•  Introduction to statistical methods and their applications in business analytics
•  Case studies illustrating the use of mathematical models in real-world business scenarios

Part 3:  Industry Research and Business Characteristics
•  Analysis of business characteristics in different sectors (e.g., investment, consulting, strategy, brokerage)
•  Examination of industry research frameworks and methodologies
•  Case studies of successful businesses in key sectors

Part 4:  Data Collection and Programming Skills
•  Sources of data for business analytics (e.g., financial databases, market reports, company filings)
•  Introduction to programming languages commonly used in business analytics (e.g., Python, R)
•  Hands-on exercises in data collection, cleaning, and analysis

Part 5:  Enterprise Valuation Methods
•  Introduction to various enterprise valuation methods (e.g., discounted cash flow, comparable company analysis, precedent transactions)
•  Application of valuation techniques to real-world examples
•  Interpretation of valuation results and their implications for business strategy and investment

Part 6:  Empirical Analysis Using Data and Statistical Methods
•  Application of data analysis techniques to assess business performance
•  Interpretation of results and their implications for business strategy and decision-making
•  Comparison of findings across different sectors and business models

Part 7:  Policy Implications and Strategic Recommendations
•  Discussion of the policy implications of data-driven decision-making and business analytics
•  Recommendations for enhancing business performance through data analytics
•  Strategies for businesses to leverage data for competitive advantage

Part 8: Future Directions in Business Analytics Research
•  Identification of emerging trends and challenges in business analytics
•  Exploration of new data sources and innovative analytical methods
•  Suggestions for future research directions to further understand the impact of business analytics on corporate success

Part 9: Conclusion and Summary
•  Summarizing key findings from the research
•  Policy recommendations for supporting data-driven decision-making in businesses
•  Suggestions for future research directions

Part 10: Reporting and Presentation
•  Writing a comprehensive research report with clear structure, concise language, and accurate data presentation
•  Preparing and delivering an engaging oral presentation of the research background, methods, results, and conclusions

Suitable for

High School Students:  
•  Interested in business, data analysis, and programming
• Basic knowledge of mathematics and business principles

University Students:
• Majoring in business, finance, economics, data science, or related fields, seeking to deepen understanding and engage in research
• Familiar with basic business theories, statistical methods, and data analysis techniques

Researchers and Educators:
• Professionals with in-depth knowledge in business analytics and data-driven decision-making
• Educators aiming to incorporate current research trends into their teaching and academic work