Ternary Variance - Universal Data Analysis Rule
| Universal data analysis rules, mastering the essence of analytical thinking, and being proficient in multiple types of business scenarios In the era of big data, data analysis is an increasingly valued skill. How to conduct effective analysis in different industries and business scenarios? What is examined is the practitioner's data analysis thinking, that is, the ability to use data analysis skills. Having the right data analysis mindset will not only allow you to handle various types of analysis needs with ease, but also make your work more efficient. Course Catalog ├──Document| ├──[5870] Opening words Skills determine the lower limit, thinking determines the upper limit.md 22.35kb | ├──[5871] 01 Goal-oriented: How to get out of the vicious circle of data acquisition? .md 23.82kb | ├──[5872] 02 Objective and rigorous: facts + argumentation process + opinions to create high-level analysis.md 18.74kb | ├──[5873] 03 Indicator thinking: How to understand the various types of indicators.md 25.53kb | ├──[5874] 04 Logical reasoning: What does the logical thinking ability in the recruitment requirements mean? .md 24.56kb | ├──[5875] 05 System structure: Look at the problem from a perspective beyond the problem itself and let your analysis get to the point.md 20.14kb | ├──[5876] 06 Understand the business: Make your analysis results in sync with the business side and leadership.md 16.81kb | ├──[5877] 07 Understand the user: Data analysis + user thinking = refined operation.md 20.70kb | ├──[5878] 08 Analysis process: General process of business data analysis.md 21.21kb | ├──[5879] 09 Define the problem: How to define the problem and open up the analysis ideas? .md 19.05kb | ├──[5880] 10 Disassembly question: Why is there always a bunch of useless data when disassembling? .md 16.82kb | ├──[5881] 11 Find out the cause: There are so many causes of the problem, which one is the most critical.md 15.10kb | ├──[5882] 12 Make suggestions: What are valuable suggestions? .md 13.47kb | ├──[5883] 13 Report writing: How to write a data analysis report efficiently? .md 15.15kb | ├──[5884] 14 Advanced analysis framework: other common analysis processes (mathematical modeling for predicting competitor users).md 17.42kb | ├──[5885] 15 Product analysis: How to increase the next-day retention rate of a certain function by 10%? .md 18.94kb | ├──[5886] 16 Conversion analysis: How to improve the purchase conversion rate of products? .md 16.71kb | ├──[5887] 17 Activity analysis: How to analyze the effectiveness of activities and give effective suggestions? .md 15.48kb | ├──[5888] 18 User growth: Identify the key behaviors that quickly increase the number of users.md 14.89kb | ├──[5889] 19 AB testing: Evaluation and optimization of AB testing effects.md 15.95kb | ├──[5890] Easter egg 1 Data operation: How to build a data operation system? .md 15.84kb | ├──[5891] Easter Egg 2 Interview: Interview Guide for Business Data Analysts.md 18.40kb | └──[5892] Conclusion: In-depth business and continuous learning.md 6.73kb ├──[5870] Opening words: Skills determine the lower limit, and thinking determines the upper limit.mp4 162.65M ├──[5871] 01 Goal-oriented: How to get out of the vicious circle of data acquisition? .mp4 99.59M ├──[5872] 02 Objective and rigorous: facts + argumentation process + viewpoints, to create high-level analysis.mp4 190.59M ├──[5873] 03 Indicator thinking: How to understand the various types of indicators.mp4 152.95M ├──[5874] 04 Logical reasoning: What does the logical thinking ability in the recruitment requirements mean? .mp4 165.43M ├──[5875] 05 System structure: Look at the problem from a perspective beyond the problem itself and let your analysis get to the point.mp4 191.77M ├──[5876] 06 Understand the business: Make your analysis results in sync with the business side and leadership.mp4 145.10M ├──[5877] 07 Understand the user: Data analysis + user thinking = refined operation.mp4 186.30M ├──[5878] 08 Analysis process: General process of business data analysis.mp4 176.10M ├──[5879] 09 Define the problem: How to define the problem and open up analytical thinking? .mp4 124.70M ├──[5880] 10 Disassembly question: Why is a bunch of useless data always disassembled? .mp4 187.29M ├──[5881] 11 Find out the cause: There are so many causes of the problem, which one is the most critical.mp4 150.80M ├──[5882] 12 Make suggestions: What are valuable suggestions? .mp4 141.34M ├──[5883] 13 Report writing: How to write a data analysis report efficiently? .mp4 137.81M ├──[5884] 14 Advanced analysis framework: other common analysis processes (mathematical modeling for predicting competitor users).mp4 206.81M ├──[5885] 15 Product analysis: How to increase the next-day retention rate of a certain function by 10%? .mp4 203.56M ├──[5886] 16 Conversion analysis: How to improve the purchase conversion rate of products? .mp4 161.05M ├──[5887] 17 Activity analysis: How to analyze the effectiveness of activities and give effective suggestions? .mp4 166.37M ├──[5888] 18 User growth: Identify the key behaviors that quickly increase the number of users.mp4 152.99M ├──[5889] 19 AB testing: Evaluation and optimization of AB testing effects.mp4 185.34M ├──[5890] Easter egg 1 Data operation: How to build a data operation system? .mp4 165.27M ├──[5891] Easter Egg 2 Interview: Interview Guide for Business Data Analysts.mp4 221.78M └──[5892] Conclusion: Deepen the business and continue to learn.mp4 62.32M |
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