Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

Time:2021-7-18

March and April of every year is the peak of recruitment, which is often called “golden three silver four golden recruitment period”. At this time, when the summary of the previous year is finished and the bonus is received, people in the workplace begin to plan for the job hunt at the beginning of the year. In the IT industry, which is one of the high paid industries, programmers also start to send their resumes to the companies they want, Will the impact of this year’s epidemic become “golden four and silver five”?

The article is continuously updated every week. Your “three links” are my greatest affirmation. WeChat can search the public for the first time to read the official account of “backend Technology School” (usually one to two updates earlier than blogs).

As it people, we should give full play to our professional expertise. How can we find satisfactory positions from various recruitment websites? I analyzed the recruitment information of C + + in Beijing, Guangzhou and Shenzhen, which is limited in space. In this paper, I only take out the data analysis of Beijing and Shenzhen. Let’s take a look at the current recruitment situation of C + + and how to scientifically improve the success rate of recruitment.

At the end of the article, share the high-definition charts of this analysis, which need to be taken by the students themselves. At the same time, I share the source code for learning and communication. If I am interested in other posts, I can run the source code analysis by myself.

requirement analysis

Through the analysis of the recruitment data released by the recruitment website, we can get the position distribution area, salary level, education requirements, job demand, key skills, and what are the characteristics of matching talents? So as to help the candidates improve their ability, make up the short board, deal with the school recruitment and social recruitment, achieve the ultimate goal and get the desired offer.

software design

Data analysis is the strength of python, and the project is implemented in Python. The software is divided into two modules: data acquisition and data analysis

Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

Detailed implementation

Data acquisition

Request library constructs request to get data

cookie = s.cookies
req = requests.post(self.baseurl, headers=self.header, data={'first': True, 'pn': i, 'kd':self.keyword}, params={'px': 'default', 'city': self.city, 'needAddtionalResult': 'false'},   cookies=cookie, timeout=3)
text = req.json()

Data storage in CSV format

With open (OS. Path. Join (self_ Key words_ City csv'.format(self.keyword, self.city)),                'w',newline='', encoding='utf-8-sig') as f:
    f_csv = csv.DictWriter(f, self.csv_header)
    f_csv.writeheader()
    f_csv.writerows(data_list)

Data analysis

Field preprocessing

df_ All. Rename ({position name ':'position'}, axis = 1, inplace = true) # axis = 1 stands for index; Axis = 0 stands for column
df_ All. Rename ({'detailed link':'url '}, axis = 1, inplace = true)
df_ All. Rename ({workplace ':'region'}, axis = 1, inplace = true)
df_ All. Rename ({salary ':'salary'}, axis = 1, inplace = true)
df_ All. Rename ({'company name':'company '}, axis = 1, inplace = true)
df_ All. Rename ({'experience requirement':'experience '}, axis = 1, inplace = true)
df_ All. Rename ({'education':'edu '}, axis = 1, inplace = true)
df_ All. Rename ({welfare ':'welfare'}, axis = 1, inplace = true)
df_ All. Rename ({'position information':'detail '}, axis = 1, inplace = true)
df_all.drop_duplicates(inplace=True)
df_all.index = range(df_all.shape[0])

Data processing presentation

from pyecharts.charts import Bar
regBar = Bar(init_opts=opts.InitOpts(width='1350px', height='750px'))
regBar.add_xaxis(region.index.tolist())
regBar.add_ Yaxis ("region", region. Values. Tolist())
regBar.set_ global_ opts(title_ Opts = opts. Titleopts (title = distribution of work area),
                     toolbox_opts=opts.ToolboxOpts(),
                     visualmap_opts=opts.VisualMapOpts())
                     
from pyecharts.commons.utils import JsCode
shBar = Bar(init_opts=opts.InitOpts(width='1350px', height='750px'))
shBar.add_xaxis(sala_high.index.tolist())
shBar.add_ Yaxis ("region", Sala_ high.values.tolist())
shBar.set_series_opts(itemstyle_opts={
            "normal": {
                "color": JsCode("""new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                    offset: 0,
                    color: 'rgba(0, 244, 255, 1)'
                }, {
                    offset: 1,
                    color: 'rgba(0, 77, 167, 1)'
                }], false)"""),
                "barBorderRadius": [30, 30, 30, 30],
                "shadowColor": 'rgb(0, 160, 221)',
            }})
shBar.set_ global_ opts(title_ Opts = opts. Titleopts (title = distribution of maximum salary range), toolbox_ opts=opts.ToolboxOpts())

word.add("", [*zip(key_words.words, key_words.num)],
         word_size_range=[20, 200], shape='diamond')
word.set_ global_ opts(title_ Opts = opts. Titleopts,
                     toolbox_opts=opts.ToolboxOpts())

Data analysis

regional distribution

Regional distribution of C + + posts, Beijing vs Shenzhen
Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

There are more C + + jobs in Beijing than in Shenzhen. The capital buff is blessed and concentrated in Haidian District and Chaoyang District. ZhongGuanCun is located in Haidian District, and Houchang village is located in the northwest of Haidian District. Tencent, Didi, Baidu, Sina, Netease and other Internet giants can naturally provide more jobs.

Shenzhen’s posts are concentrated in Nanshan District. It is speculated that goose factory C + + has made a significant contribution in Nanshan District, and the second is in Bao’an District.

Educational background distribution

C + + position education distribution, Beijing vs Shenzhen
Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

In terms of educational background, the proportion of undergraduate education in both cities is more than 80%, and the proportion of Postgraduates in Beijing is equivalent to that in junior colleges. It can be seen that most positions can be competent with bachelor’s degree, which may give you some reference when you are about to graduate.

If you have a college degree, you need to redouble your efforts, because there are not many positions left for you. At the same time, according to the chart data, the demand for post graduates in Shenzhen is 10%, while that for master’s degree is only 2%. Maybe going to Shenzhen is more friendly than going to Beijing. Emmm… For reference only.

Salary distribution

C + + position salary distribution, salary unit K.
The highest salary vs the lowest salary in Beijing
Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

The highest salary vs the lowest salary in Shenzhen
Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

There’s nothing to say about salary comparison. We just want to say that the imperial capital is really rich and powerful.

Skills reserve

Key skills of C + + position, Beijing vs Shenzhen
Big data analyzes the recruitment needs of more than 1000 jobs in first tier cities, and tells you how to find a job scientifically

First of all, the ability to solve problems by programming is the most important before stepping out of development and stepping into the management position. We can see that the ability to “program” accounts for the largest proportion in the skill cloud.

It can be seen from the word cloud of job skills that most jobs require high basic computer literacy of “algorithm, data structure, Linux, database (storage), multithreading (operating system)”. Therefore, whether you are a student preparing for school recruitment or an old man preparing for job hopping, you need to reserve these basic computer skills.

At the same time, in addition to the requirements of hard core technology, there are also requirements for candidates’ soft power. For example, candidates with the abilities of “teamwork, collaboration, learning and communication” are preferred. While improving their technical ability, we should also pay attention to the cultivation of these soft power.

It’s interesting to find that both Linux and windows have C + + development requirements. Relatively speaking, C + + development under Linux accounts for more, and the word cloud is bigger. If you have no special preference for these two platforms, learning to develop under Linux will probably increase the success rate of application.

This program is complete source code and HD analysis chart, in the official account “Back end technology school“Reply to” work “to obtain.

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