Who says rookie doesn't analyze data?

In this paper, we first make a general description of data analysis, then introduce the basic steps of data analysis, the tools used in each step are Excel. Introduction series, insufficient, hope gentlemen pointing.

Reference books:

"Who said rookie will not data analysis" (People door + tool article)

"Excel Chart rejects mediocrity"

General Introduction

All of the analysis is the six steps, clear analysis purposes and ideas-data collection-Data processing-analysis-data presentation-write reports. Skill tree is also around the six, data processing aspects for the cleaning and processing, data analysis for statistics and data mining, data display for data visualization, writing reports as PPT, and these steps can be used in Excel, Python, R and other tools, of course, the most important business knowledge, Related to the ability to interpret data correctly.

1. Identify analytical ideas

The data analysis methodology, which guides the development of data analysis in the future, is to guide the analysis of data from the macroscopic perspective, which is like a pre-planning of data analysis. Data analysis law refers to specific analytical methods, such as our common comparative analysis, cross-analysis, analysis, regression analysis, clustering analysis and other data analysis method. The data analysis method mainly knows how to analyze the data from the microcosmic angle.

The data analysis methodology has several functions: rationalize the analytical thinking, ensure the structural system of data analysis, decompose the problem into interrelated parts, and show their connection; conduct direction for subsequent data analysis and ensure the validity and correctness of the analysis results.

There may be some problems in the methodology that cannot be explained by data, and perhaps only a small part of the data can be described, such as user portraits.

2. Collecting data

here are just a few two examples, the work may require crawling Web page data, may also use SQL in the database query.

3. Data processing

4. Data analysis

Advanced Data Analysis Method index:

Research direction	Data analysis methods
Product Research	Analysis of correlation analysis, correspondence analysis, discriminant analysis, integration and multidimensional scale
Brand Research	Analysis of correlation, cluster analysis, discriminant analysis, factor analysis, correspondence analysis, multidimensional scale
Price study	Analysis of correlation and PSM price
Market segmentation	Cluster analysis, discriminant analysis, factor analysis, correspondence analysis, multidimensional scale analysis, logistic regression, decision tree
Research on satisfaction degree	Correlation, regression analysis, principal component analysis, factor analysis, structural equation
User Research	Analysis, cluster analysis, discriminant analysis, factor analysis, correspondence analysis, logistic regression, decision tree, association Rules
Predictive decision Making	Regression analysis, decision tree, neural network, time series, logistic regression

5. Data presentation

6. Report Writing

Who says rookie doesn't analyze data?