[数据分析实战][37] 基于pandasql和plotly的数据科学家薪资分析与可视化 @ShowMeAI
参考资料：
数据科学家赚多少？数据全分析与可视化-ShowMeAI
前言
代码实际跑过一遍，原文中有一些错误，都已经修改过来了。大多数修改的地方都用“修改”标注了。要查看相应内容建议使用搜索“修改”查看。
Table of Contents
- 1 导入工具库
- 2 加载数据集
- 3 数据预处理
- 4 数据分析&可视化
- 5 分析结论
导入工具库
# 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
# 利用pd.read_csv读取数据集
salaries = pd.read_csv("./ds_salaries数据集/ds_salaries.csv")
salaries.head()
# Function query to execute SQL queries
def query(query):
return ps.sqldf(query)
query("""
select *
from salaries
limit 5
""")
数据预处理
# 去掉无用的"Unnamed: 0"这一列
salaries = salaries.drop("Unnamed: 0", axis=1)
salaries
# 查看数据中缺失值的情况
salaries.isna().sum()
# 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
salaries["employee_residence"] = coco.convert(names = salaries["employee_residence"], to="name")
salaries["company_location"] = coco.convert(names = salaries["company_location"], to="name")
salaries
"""
将experience_level缩写变成全称
CN: Entry level(入门级)
MI: Mid Level(中级)
SE: Senior Level(高级)
EX: Expert Level(资深专家级)
"""
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""")
salaries
"""
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）
"""
salaries['employment_type'] = query("""SELECT
REPLACE(
REPLACE(
REPLACE(
REPLACE(
employment_type, 'PT', 'Part Time'),
'FT', 'Full Time'),
'FL', 'Freelance'),
'CT', 'Contract')
FROM
salaries""")
salaries
"""
数据集中公司规模字段处理
S：Small （小型）
M：Medium （中型）
L：Large （大型）
采用salaries.repalce({company_size: {}})函数来做替换
注意inplace=True进行本地修改
"""
replace_rule = {"S": "Small", "M": "Medium", "L": "Large"}
salaries.replace({"company_size": replace_rule}, inplace=True)
salaries
"""
对远程比率字段也做一些处理
采用salaries["remote_ratio"].repalce()函数来做替换
"""
replace_rule = {100: 'Fully Remote', 50: 'Partially Remote', 0: 'Non Remote Work'}
salaries["remote_ratio"].replace(replace_rule, inplace=True)
salaries
数据分析&可视化
# 数据科学领域Top10多的职位是？
top10_jobs = query("""\
select job_title, count(*) as job_count
from salaries
group by job_title
order by job_count desc
limit 10
""")
# 绘制条形图
data = go.Bar(x = top10_jobs["job_title"], y = top10_jobs["job_count"], # 横轴，纵轴数据
text = top10_jobs["job_count"], textposition = "outside", # 标记在纵轴上的文本，位置在内部
textfont = dict(size = 12, color = "black"), # 字号是12，颜色是白色
marker = dict(color = px.colors.qualitative.Alphabet, # 条形图颜色
opacity = 0.9, # 不透明度
line_color = "black", # 条形图外框线的颜色
line_width = 1) # 条形图外框线的宽度
)
layout = go.Layout(title = {'text': "Top 10 Data Science Jobs", # 粗体标题
'x':0.5}, # 居中显示
xaxis = dict(title = 'Job Title'), # x轴标题
yaxis = dict(title = 'Total'), # y轴标题
width = 900, # 宽
height = 600) # 高
fig = go.Figure(data = data, layout = layout) # 生成画图对象
fig.update_layout(plot_bgcolor = '#f1e7d2', # 图像背景
paper_bgcolor = '#f1e7d2') # 画布背景
fig.show() # 展示图像
# 饼图展示
fig = px.pie(top10_jobs, values="job_count", names="job_title", color_discrete_sequence=px.colors.qualitative.Alphabet) # 生成图片对象
fig.update_layout(title = {'text': "Distribution of job positions", # 标题
'x':0.5}, # 居中
width = 900,
height = 600)
fig.update_layout(plot_bgcolor = '#f1e7d2', # 这行其实没用，因为饼图没有背景
paper_bgcolor = '#f1e7d2')
fig.show()
# 拥有最多数据科学家的国家
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'],
text = top10_com_loc["job_count"], textposition = "outside",
textfont = dict(size = 12,
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 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()
# 统计各国支付的标准化工资(美元)的总和，并绘制地形图
temp_df = salaries.groupby('company_location')['salary_in_usd'].sum().reset_index() # reset_index将series变成了dataframe
temp_df['salary_scale'] = np.log10(temp_df['salary_in_usd']) # 对salar_in_usd取对数,用于表示颜色，修改，这里应该是temp_df
fig = px.choropleth(temp_df, locationmode = 'country names', locations = "company_location", # 数据源，指定位置的模式，位置的数据
color = "salary_scale", hover_name = "company_location", # 对应的颜色， 对应的名称
hover_data = temp_df[['salary_in_usd']], # 挂的数字，必须是dataframe
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(按照货币类型分组),选择top14
df = salaries.groupby('salary_currency', as_index = False)['salary_in_usd'].mean().sort_values('salary_in_usd', ascending = False) # 修改
# 第一个salary_in_usd最好加上两层中括号，使得到的是一个dataframe，这里只加了一层是因为as_index=False保证了它是一个dataframe
# 在分组的时候指定as_index = False再聚合 和 先分组聚合reset_index()的效果是一样的
# Selecting top 14
df = df.iloc[:14]
fig = px.bar(df, x = 'salary_currency', # px.bar也可以画柱状图
y = 'salary_in_usd',
color = 'salary_currency',
text = round(df['salary_in_usd']),
color_discrete_sequence = px.colors.qualitative.Safe,
)
fig.update_traces(textposition="outside") # 将文本放在外面
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()
# 平均工资(按照所在地分组),选择top14
df = salaries.groupby(['company_location'], as_index = False)[['salary_in_usd']].mean().sort_values('salary_in_usd', ascending = False)
#Selecting top 14
df = df.iloc[:14]
fig = px.bar(df, x = 'company_location',
y = 'salary_in_usd',
text = df['salary_in_usd'].apply(lambda x: str(round(x/1000, 2))+"k".format(x)), # 修改
color = 'company_location',
color_discrete_sequence = px.colors.qualitative.Dark2,
)
fig.update_traces(textposition="outside") # 将文本放在外面
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
job_exp = query("""
SELECT experience_level, Count(*) AS job_count
FROM salaries
GROUP BY experience_level
ORDER BY job_count ASC
""")
# 绘制水平柱状图
data = go.Bar(x = job_exp['job_count'], y = job_exp['experience_level'],
orientation = 'h', text = job_exp['job_count'], # orientation表示朝向,'h'表示水平
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'),
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()
# 数据科学工作就业类型分布
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()
# 数据科学工作数量趋势(2020-2022)
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'], # go模块绘制散点图
marker = dict(size = 20, # 散点大小
line_width = 1.5, # 散点的外框线宽
line_color = 'white', # 散点的外框线颜色
color = px.colors.qualitative.Alphabet), # 散点颜色
line = dict(color = '#ED7D31', width = 4),
mode = 'lines+markers') # 散点和线都画
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
salary_usd = query("""
SELECT salary_in_usd
FROM salaries
""")
# 绘制直方图和核密度曲线
plt.figure(figsize = (20, 8))
sns.set(rc = {'axes.facecolor' : '#f1e7d2', # 背景颜色
'figure.facecolor' : '#f1e7d2'}) # 图形颜色
p = sns.histplot(salary_usd["salary_in_usd"],
kde = True, # 计算核密度估计,用一条或多条线来近似分布
alpha = 1, # 透明度,如果是distplot则没有这个参数
fill = True, # 填充直方图下面的空间,默认为True
edgecolor = 'black',# 直方图的外框颜色为黑色
linewidth = 1 # 直方图的外框线宽
)
p.axes.lines[0].set_color("orange") # 核密度曲线的颜色标为橙色
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大数据分析工作
salary_hi10 = query("""
SELECT job_title,
salary
FROM salaries
ORDER BY salary_in_usd DESC
LIMIT 10
""") # 修改
data = go.Bar(x = salary_hi10['salary'],
y = salary_hi10['job_title'],
orientation = 'h',
text = salary_hi10['salary'],
textposition = 'inside',
insidetextanchor = 'middle', # 文本锚点在中间
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()
# 不同岗位平均薪资与排名
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 = 'black'),
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
salary_year = query("""
SELECT ROUND(AVG(salary_in_usd)) AS salary,
work_year AS year
FROM salaries
GROUP BY work_year
ORDER BY salary DESC
""") # 修改
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
salary_exp = query("""
SELECT experience_level AS 'Experience Level',
salary_in_usd AS Salary
FROM salaries
""")
# 绘制小提琴图
fig = px.violin(salary_exp, x = 'Experience Level', y = 'Salary', color = 'Experience Level', box = True)
fig.update_layout(title = {'text': "Salary on Experience Level
经验水平&薪资",
'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
tmp_df = salaries.groupby(['work_year', 'experience_level']).median() # 按照工作年份和经验水平分组,只有数字类型会被求中位数
tmp_df.reset_index(inplace = True)
display(tmp_df.head()) # 修改:打印dataframe的开头五行
fig = px.line(tmp_df, x='work_year', y='salary_in_usd', color='experience_level', symbol="experience_level") # 绘制多条折线图
fig.update_layout(title = {'text': "Median Salary Trend By Experience Level
不同经验水平的薪资趋势",
'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()
# 年份&薪资分布
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) # 绘制多条核密度曲线
fig.update_layout(title = {'text': "Salary Distribution By Working Year
年份&薪资分布",
'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
salary_emp = query("""
SELECT employment_type AS 'Employment Type',
salary_in_usd AS Salary
FROM salaries
""")
# 绘制箱线图
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
comp_size = query("""
SELECT company_size,
COUNT(*) AS count
FROM salaries
GROUP BY company_size
""")
# 绘制环形图(饼图中间镂空)
data = go.Pie(labels = comp_size['company_size'],
values = comp_size['count'].values, # .values可写可不写
hoverinfo = 'label', # 移动到饼图上时会显示的信息
hole = 0.5, # 中间镂空
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
df = salaries.groupby(['company_size', 'experience_level']).size() # size()聚合方法是考虑有多少行,一定返回series,count()会考虑每列,如果有空值就不计入
comp_s = np.round(df['Small'].values / df['Small'].values.sum(),2) # values得到的是ndarray,这里的计算用到广播
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']
# 绘制极坐标图
fig.add_trace(go.Scatterpolar( # add_trace方法增加一条轨迹
r = comp_s,
theta = categories, # 设置角坐标
fill = 'toself', # 起点和终点连线,形成闭环的图形
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])), # 极坐标幅度为[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
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
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
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
exp_remote = salaries.groupby(['experience_level', 'remote_ratio']).size() # 修改:得到series
display(exp_remote.head())
exp_remote = exp_remote.reset_index().rename(columns={0: 'cnt'})
display(exp_remote.head())
print(exp_remote.index)
fig = px.histogram(exp_remote, x = 'experience_level',
y = 'cnt', color = 'remote_ratio',
barmode = 'group', # 同一组的条形图不堆叠
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