Data analysis method in common scenarios of finance

Description

The overall introduction of data analysis, the idea of dimensional disassembly, serial substitution, retention analysis；

The lecturer will introduce the data analysis as a whole, and provide the idea of dimension disassembly through business processes and actual scenarios;

Three methods of basic data analysis are provided: serial substitution method, retention analysis method and funnel analysis method.

Finally, based on their years of work experience, the lecturers will share and summarize the ideas of business data dimension disassembly, as well as the application scenarios and matters needing attention of data analysis methods.

1. Overall introduction to data analysis

2. The idea of dimension disassembly

   • Significance of dimension disaggregation

   • Business process and three elements

   • Case - How to analyze the decline in the conversion rate of users' orders - the rise in the credit card cancellation rate in three months

3. Three, serial substitution method

   • Application scenario: quantify the influence of child indicators on parent indicators

   • Principle and model introduction

4. Retention analysis

   • Application scenario: Optimize user experience and improve retention

   • Method application cases

5. Funnel analysis

   • Application scenario: Application case of targeted optimization of key node method

Lecturer: Engaged in data science and data analysis in the credit center of Tencent and large joint-stock banks

More than 5 years of experience in the development, analysis and implementation of the whole data process. Familiar with the specific application of data in financial customer acquisition, risk management and other fields

What You Will Learn!

• Acquire foundational data analysis skills for financial scenarios.
• Gain hands-on experience working with real-world financial data.
• Build a valuable skillset in high demand for data analysis in the finance industry.
• Impress potential employers with practical knowledge of data analysis techniques.
• Learn the theory behind data analysis in financial contexts.
• Explore risk assessment, trend analysis, financial modeling, and more.
• Apply data analysis techniques to real financial cases.
• Understand the detailed process of data analysis in financial settings.

Who Should Attend!

• Aspiring finance professionals looking to enhance their data analysis skills.
• Individuals interested in specializing in data analysis within financial contexts.
• Beginners who want to develop a strong foundation in data analysis for finance.
• Anyone seeking a promising career in finance.

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