Data analysis and data mining machine learning are not the same thing, it is not the difference between data analysis and business analysis
But the difference between data analysis and data mining
Without statistical data analysis thinking, programming tools are useless at all.
It’s like having a knife in nothing, but even the knife method (statistical analysis method) is not just swaying at all.
If you know how to use the knife, you can use another tool to control the enemy. (Excel, SAS, R are all OK)
So we must first understand data analysis, and then pursue advanced programming tools.
Suggested entry learning path:
1. Data analysis: statistical theoretical knowledge (linear regression prediction) + Excel
2. Data query and display: professional business data visualization tools such as SQL+ Tableau or PowerBI
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The most direct classification is data analysis and business analysis,
The simple difference is that data analysis is a technical position, which requires programming (Python, etc.);
Business analysis is not a technical position and does not require programming. If you have no foundation at all, and you are not sure if you like programming,
It is recommended to learn the tools and languages required for business analysis (Excel/Tableau/SQL/Statistics).
For a junior, a girl who doesn’t know anything about major and data analysis,
It is not the best choice to learn Python, Stata, Eviews, SAS, R and other professional languages directly.
It is recommended that the Oracle certification test is even more misleading, and the answer is incorrect.
The suggested learning path is:
- Learn Excel first , especially Excel's common functions, pivot tables, and visualization. Learn Excel and you are not afraid to travel all over the society . This is beneficial to your job search after graduation.
- Learn more about professional business data visualization tools like Tableau or PowerBI . The advantage of this type of tool is that it is quick to use and easy to produce results, and job interviews are a good plus. In addition, they are very important for you to build data perception (data soft skills)!
- Then learn SQL or data processing tools like Tableau Prep or Alterxy . It is a cruel fact that data analysts are dealing with data 80% of the time. To become a qualified data analyst, you must have strong data cleaning capabilities!
- Finally, learn Python or R. Putting Python and R last is because people who are proficient in Python do not necessarily understand the use of Python in data analysis, but those who are proficient in data analysis must understand the use of Python. When you first become someone who understands data analysis and then learn Python, you will get twice the result with half the effort! On the contrary, it is very likely to get twice the result with half the effort.
Finally, in the process of skill learning, we must master the training of data analysis soft skills, such as business sensitivity, business analysis models, and data analysis index systems.
The public account [DataSchool] shares such learning articles and learning courses.
Author: DataSchool
link: https: //www.zhihu.com/question/300254387/answer/537730561
Source: know almost
copyrighted by the author. For commercial reprints, please contact the author for authorization. For non-commercial reprints, please indicate the source.
The main tool is also Excel, but here the requirements for data analysis are relatively strict, some specific report development, the combination of some Excel functions, or some modeling analysis.
The analysis methods mainly use some correlation analysis, cluster analysis, linear regression, etc. For these, you need to gnaw statistics books.
To engage in data analysis, the most basic is to learn Excel and SQL languages. These two can basically meet the tool needs of junior data analysts .
As for the theoretical method, it is recommended to look at the knowledge of statistics and the analysis reports on some statistical analysis websites.
And in the process of learning to cultivate their own structured thinking, business thinking and logical thinking.
As for the later data volume, some high-level data analysis tools Python, R, tableau, SPSS, SAS, etc. may be used.
The specific tools used depend on the individual's career development plan and the popularity of the industry, and then learn new tools and mining methods!
Author: Thomas
link: https: //www.zhihu.com/question/300254387/answer/546783273
Source: know almost
copyrighted by the author. For commercial reprints, please contact the author for authorization. For non-commercial reprints, please indicate the source.