文科生如何学习统计

Contents

• Academic statistics: 都有哪些内容
• Academic statistics: 数学基础
• 工作中的统计：哪些最重要？
• 我是文科生, 我能学会吗？
• 工作中的统计：如何学？

Academic statistics: 都有哪些内容

• In University: Statistics is a basic course for several departments:

Academic statistics: 都有哪些内容

• Experimental design
• Survey data analysis
• Descriptive analysis
• Inferential analysis
• Predictive analysis
• Forecast analysis

Academic statistics: 都有哪些内容

• Investment（对冲基金）analysis: Statistics in investing include average trading volume, 52-week low, 52-week high, beta, and correlation between asset classes or securities.
• 计量经济: Statistics in economics include GDP, unemployment, consumer pricing, inflation, and other economic growth metrics.
• Marketing: Statistics in marketing include conversion rates, click-through rates, search quantities, and social media metrics.
• Human resource: include employee turnover, employee satisfaction, and average compensation relative to the market.

Academic statistics:数学基础

• Differential and integral calculus: 微积分
• Linear algebra: 线性代数
• Probability theory: 概率理论

Academic statistics vs 工作中统计

• Academic statistics: 推理的严谨，数学理论的学习与应用；Write program , from the ground. Academic statistics是为了degree
• 工作中统计: 在数据分析software的帮助下为company提供solution，为公司创造价值

工作中的统计

• Marketing research:
  • Survey data analysis:
  • Descriptive analysis
  • Predictive modelling
  • AI-deep learning
  • Forecast

工作中的统计

• Marketing research: product design, select functionality and price; customer satisfactory
• Survey data analysis: social analysis; opinion about a special event
• Descriptive analysis: dashboard design
• Predictive modelling: machine learning- predict probabilities
• AI-deep learning: image processing, 智能text、语音生成，spam email detect
• Forecast: forecast sale, revenue, planning and budget, etc

工作中的统计

• Understanding basic statistics is necessary
• Know well what software can help you complete these statistics
• Understand how to use procedures, options,
• Business cases are important.

工作中的统计– Descriptive analysis

• Ad hoc analysis;
• Dashboard design.
• Processing software: SAS, SQL, Python, R.
• Calculating KPI: based on business definition, calculating KPI by SAS, SQL, Python, R
• Display results: SAS/python/R programming, tableau, power bi, excel

工作中的统计– Survey/marketing research

• Business scenario, product features,
• Design questions: Microsoft word, tools for special fields
• Analyzing data: SAS/STAT, SPSS
• Display results: SAS/python/R programming, tableau, power bi, excel

工作中的统计– Predictive modelling

• Business scenario,
• Analyzing data: SAS/STAT, SPSS/IBM statistics, Python
• Display results: SAS/python/R programming, tableau, power bi, excel

工作中的统计– AI-deep learning

• Business scenario,
• Analyzing data: SAS/Viya, IBM Watson studio, Python, Tensorflow, Keras
• Display results: SAS/python/R programming, tableau, power bi, excel

工作中的统计– forecast—planning, budget

• Business scenario,
• Preparing data: SAS/Base, SAS/SQL, python, R
• Analyzing data: SAS/ETS, Tableau, power bi, excel
• Display results: tableau, power bi, excel

Teaching yourself statistics?

• Elemental statistics: high school level, You can teach yourself
• Academic statistics: university/graduate difficult to learning by yourself,
• if you have (a)微积分 ; (b)线性代数; (c)概率理论

工作中的统计:

• Elemental statistics/basic concept—you may teach yourself
• Inferential/predictive/forecast/deep learning- difficult to 自学
• Application of Software: working experience
• Business scenario: working experience

Advanced data analysis

• Business problem
• Business idea
• Business software
• Business solution
• Business insight