Data Analysis Method In Common Scenarios Of Finance

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

What you'll 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.

Requirements

No prior experience is required. We will start from the very basics.

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.Overall introduction to data analysisThe idea of dimension disassemblySignificance of dimension disaggregationBusiness process and three elementsCase - How to analyze the decline in the conversion rate of users' orders - the rise in the credit card cancellation rate in three monthsThree, serial substitution methodApplication scenario: quantify the influence of child indicators on parent indicatorsPrinciple and model introductionRetention analysisApplication scenario: Optimize user experience and improve retention Method application cases Funnel analysisApplication scenario: Application case of targeted optimization of key node methodLecturer: Engaged in data science and data analysis in the credit center of Tencent and large joint-stock banksMore 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

Overview

Section 1: Data analysis method in common scenarios of finance

Lecture 1 Data analysis method in common scenarios of finance①

Lecture 2 Data analysis method in common scenarios of finance②

Lecture 3 Data analysis method in common scenarios of finance③

Lecture 4 Data analysis method in common scenarios of finance④

Lecture 5 Data analysis method in common scenarios of finance⑤

Lecture 6 Data analysis method in common scenarios of finance⑥

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.