Step1：Excel learning mastery

The tool used is the most commonly used Excel in the workplace!

①Key functions to learn

The focus is to understand the various functions: Vlookup, sum, count, sumif, countif, find, if, left/right, time and date functions, text functions

② pivot table learning

I have said that if Excel can only learn one function, the only one that can be selected is the pivot table.

Mastering vlookup and pivot table are the two most cost-effective skills. If you learn vlookup, join in SQL and merge in Python are easy to understand. Learn pivot table, group in SQL, pivot_table in Python is the same.

These two get it done, the basic 100,000 or less data statistics is not much difficulty, 80% of the office white-collar can be killed.



Step2: data visualization

Tools used: PowerBi, Excel

Data analysis community has a classic saying, the word is not as good as the table, the table is not as good as the chart.

Data visualization is one of the main directions of data analysis. The first step is to understand the commonly used charts.


Excel's charts can be 100% complete the above graphical requirements, but this is only the foundation. Subsequent advanced visualization is bound to use programming to draw. Why? For example, the common multivariate analysis, you can use Excel to easily complete? But in IPython only need a line of code.


Next, master BI, the following chart is Microsoft BI.


The difference between BI (Business Intelligence) and charts is that BI is good at interaction and reporting, and better at explaining the data that has happened and is happening. The data that is going to happen is where the data mining is headed.

The benefit of BI is that it largely frees up the work of data analysts, promotes department-wide data awareness, and additionally reduces the data needs of other departments (the all-important data guide).

There are many products on the BI market, which basically build dashboards Dashboard, and get visual analysis through dimensional linkage and drill-down. Finally, you need to learn visualization and infographic creation.


Step3: Database learning

Tools used: SQL

Excel has no problem to handle data within 100,000, but there is no shortage of data in the Internet industry. But where the product has a little scale, the data is a million up. This is the time to learn the database.

More and more product and operation positions will be in the recruitment conditions, will be SQL as a priority plus.

SQL is one of the core skills of data analysis, from Excel to SQL is definitely a big progress in data processing efficiency.

Learning revolves around Select. Add, delete, constraint, index, database paradigm can be skipped. Mainly understand where, group by, order by, having, like, count, sum, min, max, distinguish, if, join, left join, limit, and and the logic of or, time conversion function, etc.. If you want to follow up further, you can learn row_number, substr, convert, contact, etc. If you want to go further, you can learn about row_number, substr, convert, contact, etc. In addition, the functions of different data platforms will be different, such as Presto and phpMyAdmin, and if you are a bit more ambitious, you can learn about Explain optimization, how SQL works, data types, and IO. The main thing to learn about SQL is to practice more, look for related practice problems on the Internet, and brush it over.

Well, after these three steps, you already have the basic skills of data analysis, the rest of the training is your thinking and the actual business analysis ability.

Given that you are a traditional accounting professional, then you need to consider the current you need to develop in which direction, general data analyst career planning has a simple data analysis post, data modeling post.

Here we talk about the general data analyst commonly used tools which are?
1、Data processing tools: Excel

Data analysts, in some companies will also have data product managers, data mining engineers and so on. Some companies will also involve advanced skills like Visio, Xmind, PPT and other design icons data analysis. Data analyst is a position that requires a strong combination of skills, therefore, in some Internet companies still need data pivot exercises, Vision cross-functional flow chart exercises, Xmind project plan guide exercises, PPT advanced animation skills, etc.

In Excel, you need to focus on understanding the important skills of data processing and the application of functions, especially the application of data cleaning technology. The use of this application can de-fake the data to store the truth, master the data initiative, full control of data; Excel pivot table applications focus on mining the hidden value of data, easy to integrate a large amount of data: the production skills of various chart types and the application of Power Query, Power Pivot can show the data visualization effect, so that the data speak. Therefore, if you want to engage in data analysis positions, you need to quickly master a variety of Excel data processing and analysis skills.


2、Database: MySQL

Excel if you can play a good turn, can be competent for a part of the data volume is not very large companies. However, based on Excel's limited ability to handle data, if you want to be competent in medium-sized Internet companies in data analysis positions or more difficult. Therefore, you need to learn database technology, general Mysql. you need to understand the use of MySQL management tools and the basic operation of the database; the basic operation of data tables, MySQL data types and operators, MySQL functions, query statements, stored procedures and functions, trigger procedures and views, etc.. The more advanced ones need to learn MySQL backup and recovery; familiar with the complete MySQL data system development process.


3、Data visualization: Tableau & Echarts

If the first two are data processing techniques, then in today's "value is king" now, how to show the data better, so that others are more willing to see, this is also a technical work. It is like the company leader asked you to report on a project research results, then you can not show him the data alone, you need to make the data more intuitive, and even more beautiful!

How to understand data visualization? Like the bar graphs and pie charts we learned in school before, they are also a kind of data visualization. It's just that these days, simple bar charts are no longer sufficient for the job. Currently the more popular commercial data visualization tools are Tableau & Echarts.

Echarts is open source, the code can be changed, the variety is also very rich, not much here to introduce, you can go to create a workspace to understand the next.


4、Big data analysis: SPSS & Python & HiveSQL, etc.

If Excel is "light data processing tools", Mysql is "medium-sized data processing tools", then, big data analysis, involving a very wide range of aspects, technical points involved in more. This is why Internet companies are currently paying millions of dollars a year to find big data analysts

Big data analysis needs to deal with massive amounts of data, which requires a high level of work ability for data analysts, generally speaking, big data analysts need to be able to

(1) Use Hive's SQL method HiveQL to aggregate, query and analyze big data collections stored on Hadoop distributed file systems. Know how Hive works in the Hadoop ecosystem for data analysis.

(2) will some SPSS modeler basic application, this part of the skills corresponding to data modeling analyst

(3) He use R language to create data sets and data management and other work; will use R language data visualization operations, so that students learn how to use R language graphs, such as bar graphs, line graphs and combination graphs, etc.; is R language data mining, this part of the data mining engineer

(4) use Python to write web crawlers, a variety of ways to grab data from the page, extract data from the cache, the use of multiple threads and processes for concurrent crawling, etc

To summarize.



Write at the end

1, their own positioning is very important

All of these skills introduced above, are based on your own positioning, if you positioned only small business data analysis positions, then you may need to play Excel very 6 on it. But in the long run, this part of the job, will eventually be replaced by big data analytics. That's why it is difficult for Internet companies to find big data analysts.

2, lifelong learning is very important

Why say so, you will find that this is the pace of the times is getting faster and faster, you do not learn it is easy to be eliminated by the times, temporarily positioning themselves as small business data analysts, do not forget to learn the more popular and trendy technology. Their own field of work can be linked to the Internet as far as possible, after all, this is the general trend.

Lastly

Make good use of common tools for data analysts, and I wish you all become an excellent data analyst soon!