数据科学家赚多少？数据全分析与可视化 ⛵

语言: CN / TW / HK
时间 2023-01-16 23:15:15

highlight: a11y-dark

💡 引言

数据科学在互联网、医疗、电信、零售、体育、航空、艺术等各个领域仍然越来越受欢迎。在 📘Glassdoor的美国最佳职位列表中，数据科学职位排名第三，2022 年有近 10,071 个职位空缺。

除了数据独特的魅力，数据科学相关岗位的薪资也备受关注，在本篇内容中，ShowMeAI会基于数据对下述问题进行分析：

  • 数据科学中薪水最高的工作是什么？
  • 哪个国家的薪水最高，机会最多？
  • 典型的薪资范围是多少？
  • 工作水平对数据科学家有多重要？
  • 数据科学，全职vs自由职业者
  • 数据科学领域薪水最高的工作是什么？
  • 数据科学领域平均薪水最高的工作是什么？
  • 数据科学专业的最低和最高工资
  • 招聘数据科学专业人员的公司规模如何？
  • 工资是不是跟公司规模有关？
  • WFH（远程办公）和 WFO 的比例是多少？
  • 数据科学工作的薪水每年如何增长？
  • 如果有人正在寻找与数据科学相关的工作，你会建议他在网上搜索什么？
  • 如果你有几年初级员工的经验，你应该考虑跳槽到什么规模的公司？

💡 数据说明

我们本次用到的数据集是 🏆数据科学工作薪水数据集，大家可以通过 ShowMeAI 的百度网盘地址下载。

🏆 实战数据集下载（百度网盘）：公众号『ShowMeAI研究中心』回复『实战』，或者点击 这里 获取本文 [37]基于pandasql和plotly的数据科学家薪资分析与可视化 『ds_salaries数据集』

⭐ ShowMeAI官方GitHub：http://github.com/ShowMeAI-Hub

数据集包含 11 列，对应的名称和含义如下：

| 参数 | 含义 | | :---- | :---- | | work_year | 支付工资的年份 | | experience_level : 发薪时的经验等级 | | employment_type | 就业类型 | | job_title | 岗位名称 | | salary | 支付的总工资总额 | | salary_currency | 支付的薪水的货币 | | salary_in_usd | 支付的标准化工资（美元） | | employee_residence | 员工的主要居住国家 | | remote_ratio | 远程完成的工作总量 | | company_location | 雇主主要办公室所在的国家/地区 | | company_size | 根据员工人数计算的公司规模 |

本篇分析使用到Pandas和SQL，欢迎大家阅读ShowMeAI的数据分析教程和对应的工具速查表文章，系统学习和动手实践：

📘图解数据分析：从入门到精通系列教程

📘编程语言速查表 | SQL 速查表

📘数据科学工具库速查表 | Pandas 速查表

📘数据科学工具库速查表 | Matplotlib 速查表

💡 导入工具库

我们先导入需要使用的工具库，我们使用pandas读取数据，使用 Plotly 和 matplotlib 进行可视化。并且我们在本篇中会使用 SQL 进行数据分析，我们这里使用到了 📘pandasql 工具库。

```python

For loading data

import pandas as pd import numpy as np

For SQL queries

import pandasql as ps

For ploting graph / Visualization

import plotly.graph_objects as go import plotly.express as px from plotly.offline import iplot import plotly.figure_factory as ff

import plotly.io as pio import seaborn as sns import matplotlib.pyplot as plt

To show graph below the code or on same notebook

from plotly.offline import init_notebook_mode init_notebook_mode(connected=True)

To convert country code to country name

import country_converter as coco

import warnings warnings.filterwarnings('ignore') ```

💡 加载数据集

我们下载的数据集是 CSV 格式的，所以我们可以使用 read_csv 方法来读取我们的数据集。

```python

Loading data

salaries = pd.read_csv('ds_salaries.csv') ```

要查看前五个记录，我们可以使用 salaries.head() 方法。

借助 pandasql完成同样的任务是这样的：

```python

Function query to execute SQL queries

def query(query): return ps.sqldf(query)

Showing Top 5 rows of data

query(""" SELECT * FROM salaries LIMIT 5 """) ```

输出：

💡 数据预处理

我们数据集中的第1列“Unnamed: 0”是没有用的，在分析之前我们把它剔除：

python salaries = salaries.drop('Unnamed: 0', axis = 1)

我们查看一下数据集中缺失值情况：

python salaries.isna().sum()

输出： work_year 0 experience_level 0 employment_type 0 job_title 0 salary 0 salary_currency 0 salary_in_usd 0 employee_residence 0 remote_ratio 0 company_location 0 company_size 0 dtype: int64

我们的数据集中没有任何缺失值，因此不用做缺失值处理，employee_residence 和 company_location 使用的是短国家代码。我们映射替换为国家的全名以便于理解：

```python

Converting countries code to country names

salaries["employee_residence"] = coco.convert(names=salaries["employee_residence"], to="name") salaries["company_location"] = coco.convert(names=salaries["company_location"], to="name") ```

这个数据集中的experience_level代表不同的经验水平，使用的是如下缩写：

  • CN: Entry Level （入门级）
  • ML：Mid level （中级）
  • SE：Senior Level （高级）
  • EX：Expert Level （资深专家级）

为了更容易理解，我们也把这些缩写替换为全称。

```python

Replacing values in column - experience_level :

salaries['experience_level'] = query("""SELECT REPLACE( REPLACE( REPLACE( REPLACE( experience_level, 'MI', 'Mid level'), 'SE', 'Senior Level'), 'EN', 'Entry Level'), 'EX', 'Expert Level') FROM salaries""") ```

同样的方法，我们对工作形式也做全称替换

  • FT: Full Time （全职）
  • PT: Part Time （兼职）
  • CT：Contract （合同制）
  • FL：Freelance （自由职业）

```python

Replacing values in column - experience_level :

salaries['employment_type'] = query("""SELECT REPLACE( REPLACE( REPLACE( REPLACE( employment_type, 'PT', 'Part Time'), 'FT', 'Full Time'), 'FL', 'Freelance'), 'CT', 'Contract') FROM salaries""") ```

数据集中公司规模字段处理如下：

  • S：Small （小型）
  • M：Medium （中型）
  • L：Large （大型）

```python

Replacing values in column - company_size :

salaries['company_size'] = query("""SELECT REPLACE( REPLACE( REPLACE( company_size, 'M', 'Medium'), 'L', 'Large'), 'S', 'Small') FROM salaries""") ```

我们对远程比率字段也做一些处理，以便更好理解

```python

Replacing values in column - remote_ratio :

salaries['remote_ratio'] = query("""SELECT REPLACE( REPLACE( REPLACE( remote_ratio, '100', 'Fully Remote'), '50', 'Partially Remote'), '0', 'Non Remote Work') FROM salaries""") ```

这是预处理后的最终输出。

💡 数据分析&可视化

💦 数据科学中薪水最高的工作是什么？

python top10_jobs = query(""" SELECT job_title, Count(*) AS job_count FROM salaries GROUP BY job_title ORDER BY job_count DESC LIMIT 10 """)

我们绘制条形图以便更直观理解：

```python data = go.Bar(x = top10_jobs['job_title'], y = top10_jobs['job_count'], text = top10_jobs['job_count'], textposition = 'inside', textfont = dict(size = 12, color = 'white'), marker = dict(color = px.colors.qualitative.Alphabet, opacity = 0.9, line_color = 'black', line_width = 1))

layout = go.Layout(title = {'text': "Top 10 Data Science Jobs", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Job Title', tickmode = 'array'), yaxis = dict(title = 'Total'), width = 900, height = 600)

fig = go.Figure(data = data, layout = layout) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 数据科学职位的市场分布

```python fig = px.pie(top10_jobs, values='job_count', names='job_title', color_discrete_sequence = px.colors.qualitative.Alphabet)

fig.update_layout(title = {'text': "Distribution of job positions", 'x':0.5, 'xanchor': 'center'}, width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 拥有最多数据科学工作的国家

```python top10_com_loc = query(""" SELECT company_location AS company, Count(*) AS job_count FROM salaries GROUP BY company ORDER BY job_count DESC LIMIT 10 """)

data = go.Bar(x = top10_com_loc['company'], y = top10_com_loc['job_count'], textfont = dict(size = 12, color = 'white'), marker = dict(color = px.colors.qualitative.Alphabet, opacity = 0.9, line_color = 'black', line_width = 1))

layout = go.Layout(title = {'text': "Top 10 Data Science Countries", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Countries', tickmode = 'array'), yaxis = dict(title = 'Total'), width = 900, height = 600)

fig = go.Figure(data = data, layout = layout) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

从上图中，我们可以看出美国在数据科学方面的工作机会最多。现在我们来看看世界各地的薪水。大家可以继续运行代码，查看可视化结果。

```python df = salaries df["company_country"] = coco.convert(names = salaries["company_location"], to = 'name_short')

temp_df = df.groupby('company_country')['salary_in_usd'].sum().reset_index() temp_df['salary_scale'] = np.log10(df['salary_in_usd'])

fig = px.choropleth(temp_df, locationmode = 'country names', locations = "company_country", color = "salary_scale", hover_name = "company_country", hover_data = temp_df[['salary_in_usd']], color_continuous_scale = 'Jet', )

fig.update_layout(title={'text':'Salaries across the World', 'xanchor': 'center','x':0.5}) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 平均工资（基于货币计算）

```python df = salaries[['salary_currency','salary_in_usd']].groupby(['salary_currency'], as_index = False).mean().set_index('salary_currency').reset_index().sort_values('salary_in_usd', ascending = False)

Selecting top 14

df = df.iloc[:14] fig = px.bar(df, x = 'salary_currency', y = 'salary_in_usd', color = 'salary_currency', color_discrete_sequence = px.colors.qualitative.Safe, )

fig.update_layout(title={'text':'Average salary as a function of currency', 'xanchor': 'center','x':0.5}, xaxis_title = 'Currency', yaxis_title = 'Mean Salary') fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

人们以美元赚取的收入最多，其次是瑞士法郎和新加坡元。

```python df = salaries[['company_country','salary_in_usd']].groupby(['company_country'], as_index = False).mean().set_index('company_country').reset_index().sort_values('salary_in_usd', ascending = False)

Selecting top 14

df = df.iloc[:14] fig = px.bar(df, x = 'company_country', y = 'salary_in_usd', color = 'company_country', color_discrete_sequence = px.colors.qualitative.Dark2, )

fig.update_layout(title = {'text': "Average salary as a function of company location", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Company Location', tickmode = 'array'), yaxis = dict(title = 'Mean Salary'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 数据科学工作经验水平分布

```python job_exp = query(""" SELECT experience_level, Count(*) AS job_count FROM salaries GROUP BY experience_level ORDER BY job_count ASC """)

data = go.Bar(x = job_exp['job_count'], y = job_exp['experience_level'], orientation = 'h', text = job_exp['job_count'], marker = dict(color = px.colors.qualitative.Alphabet, opacity = 0.9, line_color = 'white', line_width = 2))

layout = go.Layout(title = {'text': "Jobs on Experience Levels", 'x':0.5, 'xanchor':'center'}, xaxis = dict(title='Total', tickmode = 'array'), yaxis = dict(title='Experience lvl'), width = 900, height = 600)

fig = go.Figure(data = data, layout = layout) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

从上图可以看出，大多数数据科学都是 高级水平 ，专家级很少。

💦 数据科学工作就业类型分布

```python job_emp = query(""" SELECT employment_type, COUNT(*) AS job_count FROM salaries GROUP BY employment_type ORDER BY job_count ASC """)

data = go.Bar(x = job_emp['job_count'], y = job_emp['employment_type'], orientation ='h',text = job_emp['job_count'], textposition ='outside', marker = dict(color = px.colors.qualitative.Alphabet, opacity = 0.9, line_color = 'white', line_width = 2))

layout = go.Layout(title = {'text': "Jobs on Employment Type", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title='Total', tickmode = 'array'), yaxis =dict(title='Emp Type lvl'), width = 900, height = 600)

fig = go.Figure(data = data, layout = layout) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

从上图中，我们可以看到大多数数据科学家从事 全职工作 ，而合同工和自由职业者 则较少

💦 数据科学工作数量趋势

```python job_year = query(""" SELECT work_year, COUNT(*) AS 'job count' FROM salaries GROUP BY work_year ORDER BY 'job count' DESC """)

data = go.Scatter(x = job_year['work_year'], y = job_year['job count'], marker = dict(size = 20, line_width = 1.5, line_color = 'white', color = px.colors.qualitative.Alphabet), line = dict(color = '#ED7D31', width = 4), mode = 'lines+markers')

layout = go.Layout(title = {'text' : "Data Science jobs Growth (2020 to 2022)", 'x' : 0.5, 'xanchor' : 'center'}, xaxis = dict(title = 'Year'), yaxis = dict(title = 'Jobs'), width = 900, height = 600)

fig = go.Figure(data = data, layout = layout) fig.update_xaxes(tickvals = ['2020','2021','2022']) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 数据科学工作薪水分布

```python salary_usd = query(""" SELECT salary_in_usd FROM salaries """)

import matplotlib.pyplot as plt

plt.figure(figsize = (20, 8)) sns.set(rc = {'axes.facecolor' : '#f1e7d2', 'figure.facecolor' : '#f1e7d2'})

p = sns.histplot(salary_usd["salary_in_usd"], kde = True, alpha = 1, fill = True, edgecolor = 'black', linewidth = 1) p.axes.lines[0].set_color("orange") plt.title("Data Science Salary Distribution \n", fontsize = 25) plt.xlabel("Salary", fontsize = 18) plt.ylabel("Count", fontsize = 18) plt.show() ```

💦 薪酬最高的 10 大数据科学工作

```python salary_hi10 = query(""" SELECT job_title, MAX(salary_in_usd) AS salary FROM salaries GROUP BY salary ORDER BY salary DESC LIMIT 10 """)

data = go.Bar(x = salary_hi10['salary'], y = salary_hi10['job_title'], orientation = 'h', text = salary_hi10['salary'], textposition = 'inside', insidetextanchor = 'middle', textfont = dict(size = 13, color = 'black'), marker = dict(color = px.colors.qualitative.Alphabet, opacity = 0.9, line_color = 'black', line_width = 1))

layout = go.Layout(title = {'text': "Top 10 Highest paid Data Science Jobs", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'salary', tickmode = 'array'), yaxis = dict(title = 'Job Title'), width = 900, height = 600) fig = go.Figure(data = data, layout = layout) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

首席数据工程师 是数据科学领域的高薪工作。

💦 不同岗位平均薪资与排名

```python salary_av10 = query(""" SELECT job_title, ROUND(AVG(salary_in_usd)) AS salary FROM salaries GROUP BY job_title ORDER BY salary DESC LIMIT 10 """)

data = go.Bar(x = salary_av10['salary'], y = salary_av10['job_title'], orientation = 'h', text = salary_av10['salary'], textposition = 'inside', insidetextanchor = 'middle', textfont = dict(size = 13, color = 'white'), marker = dict(color = px.colors.qualitative.Alphabet, opacity = 0.9, line_color = 'white', line_width = 2))

layout = go.Layout(title = {'text': "Top 10 Average paid Data Science Jobs", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'salary', tickmode = 'array'), yaxis = dict(title = 'Job Title'), width = 900, height = 600) fig = go.Figure(data = data, layout = layout) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 数据科学薪资趋势

```python salary_year = query(""" SELECT ROUND(AVG(salary_in_usd)) AS salary, work_year AS year FROM salaries GROUP BY year ORDER BY salary DESC """)

data = go.Scatter(x = salary_year['year'], y = salary_year['salary'], marker = dict(size = 20, line_width = 1.5, line_color = 'black', color = '#ED7D31'), line = dict(color = 'black', width = 4), mode = 'lines+markers')

layout = go.Layout(title = {'text' : "Data Science Salary Growth (2020 to 2022) ", 'x' : 0.5, 'xanchor' : 'center'}, xaxis = dict(title = 'Year'), yaxis = dict(title = 'Salary'), width = 900, height = 600)

fig = go.Figure(data = data, layout = layout) fig.update_xaxes(tickvals = ['2020','2021','2022']) fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 经验水平&薪资

```python salary_exp = query(""" SELECT experience_level AS 'Experience Level', salary_in_usd AS Salary FROM salaries """)

fig = px.violin(salary_exp, x = 'Experience Level', y = 'Salary', color = 'Experience Level', box = True)

fig.update_layout(title = {'text': "Salary on Experience Level", 'xanchor': 'center','x':0.5}, xaxis = dict(title = 'Experience level'), yaxis = dict(title = 'salary', ticktext = [-300000, 0, 100000, 200000, 300000, 400000, 500000, 600000, 700000]), width = 900, height = 600)

fig.update_layout(paper_bgcolor= '#f1e7d2', plot_bgcolor = '#f1e7d2', showlegend = False) fig.show() ```

💦 不同经验水平的薪资趋势

```python tmp_df = salaries.groupby(['work_year', 'experience_level']).median() tmp_df.reset_index(inplace = True)

fig = px.line(tmp_df, x='work_year', y='salary_in_usd', color='experience_level', symbol="experience_level")

fig.update_layout(title = {'text': "Median Salary Trend By Experience Level", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Working Year', tickvals = [2020, 2021, 2022], tickmode = 'array'), yaxis = dict(title = 'Salary'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

观察 1. 在COVID-19大流行期间（2020 年至 2021 年），专家级员工薪资非常高，但是呈现部分下降趋势。 2. 2021年以后专家级和高级职称人员工资有所上涨。

💦 年份&薪资分布

```python year_gp = salaries.groupby('work_year') hist_data = [year_gp.get_group(2020)['salary_in_usd'], year_gp.get_group(2021)['salary_in_usd'], year_gp.get_group(2022)['salary_in_usd']] group_labels = ['2020', '2021', '2022']

fig = ff.create_distplot(hist_data, group_labels, show_hist = False)

fig.update_layout(title = {'text': "Salary Distribution By Working Year", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Salary'), yaxis = dict(title = 'Kernel Density'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 就业类型&薪资

```python salary_emp = query(""" SELECT employment_type AS 'Employment Type', salary_in_usd AS Salary FROM salaries """)

fig = px.box(salary_emp,x='Employment Type',y='Salary', color = 'Employment Type')

fig.update_layout(title = {'text': "Salary by Employment Type", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Employment Type'), yaxis = dict(title = 'Salary'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 公司规模分布

```python comp_size = query(""" SELECT company_size, COUNT(*) AS count FROM salaries GROUP BY company_size """)

import plotly.graph_objects as go data = go.Pie(labels = comp_size['company_size'], values = comp_size['count'].values, hoverinfo = 'label', hole = 0.5, textfont_size = 16, textposition = 'auto') fig = go.Figure(data = data)

fig.update_layout(title = {'text': "Company Size", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = ''), yaxis = dict(title = ''), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 不同公司规模的经验水平比例

```python df = salaries.groupby(['company_size', 'experience_level']).size() comp_s = np.round(df['Small'].values / df['Small'].values.sum(),2) comp_m = np.round(df['Medium'].values / df['Medium'].values.sum(),2) comp_l = np.round(df['Large'].values / df['Large'].values.sum(),2)

fig = go.Figure() categories = ['Entry Level', 'Expert Level','Mid level','Senior Level']

fig.add_trace(go.Scatterpolar( r = comp_s, theta = categories, fill = 'toself', name = 'Company Size S'))

fig.add_trace(go.Scatterpolar( r = comp_m, theta = categories, fill = 'toself', name = 'Company Size M'))

fig.add_trace(go.Scatterpolar( r = comp_l, theta = categories, fill = 'toself', name = 'Company Size L'))

fig.update_layout( polar = dict( radialaxis = dict(range = [0, 0.6])), showlegend = True, )

fig.update_layout(title = {'text': "Proportion of Experience Level In Different Company Sizes", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = ''), yaxis = dict(title = ''), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 不同公司规模&工作薪资

```python salary_size = query(""" SELECT company_size AS 'Company size', salary_in_usd AS Salary FROM salaries """)

fig = px.box(salary_size, x='Company size', y = 'Salary', color = 'Company size')

fig.update_layout(title = {'text': "Salary by Company size", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Company size'), yaxis = dict(title = 'Salary'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 WFH（远程办公）和 WFO 的比例

```python rem_type = query(""" SELECT remote_ratio, COUNT(*) AS total FROM salaries GROUP BY remote_ratio """)

data = go.Pie(labels = rem_type['remote_ratio'], values = rem_type['total'].values, hoverinfo = 'label', hole = 0.4, textfont_size = 18, textposition = 'auto')

fig = go.Figure(data = data)

fig.update_layout(title = {'text': "Remote Ratio", 'x':0.5, 'xanchor': 'center'}, width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 薪水受Remote Type影响程度

```python salary_remote = query(""" SELECT remote_ratio AS 'Remote type', salary_in_usd AS Salary From salaries """)

fig = px.box(salary_remote, x = 'Remote type', y = 'Salary', color = 'Remote type')

fig.update_layout(title = {'text': "Salary by Remote Type", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Remote type'), yaxis = dict(title = 'Salary'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💦 不同经验水平&远程比率

```python exp_remote = salaries.groupby(['experience_level', 'remote_ratio']).count() exp_remote.reset_index(inplace = True)

fig = px.histogram(exp_remote, x = 'experience_level', y = 'work_year', color = 'remote_ratio', barmode = 'group', text_auto = True)

fig.update_layout(title = {'text': "Respondent Count In Different Experience Level Based on Remote Ratio", 'x':0.5, 'xanchor': 'center'}, xaxis = dict(title = 'Experience Level'), yaxis = dict(title = 'Number of Respondents'), width = 900, height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2') fig.show() ```

💡 分析结论

  • 数据科学领域Top3多的职位是数据科学家、数据工程师和数据分析师。

  • 数据科学工作越来越受欢迎。员工比例从2020年的11.9%增加到2022年的52.4%。

  • 美国是数据科学公司最多的国家。

  • 工资分布的IQR在62.7k和150k之间。

  • 在数据科学员工中，大多数是高级水平，而专家级则更少。

  • 大多数数据科学员工都是全职工作，很少有合同工和自由职业者。

  • 首席数据工程师是薪酬最高的数据科学工作。

  • 数据科学的最低工资（入门级经验）为4000美元，具有专家级经验的数据科学的最高工资为60万美元。

  • 公司构成：53.7%中型公司，32.6%大型公司，13.7%小型数据科学公司。

  • 工资也受公司规模影响，规模大的公司支付更高的薪水。

  • 62.8%的数据科学是完全远程工作，20.9%是非远程工作，16.3%是部分远程工作。

  • 数据科学薪水随时间和经验积累而增长。

参考资料

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