A career aspirations survey gathers information about the goals and aspirations of people in their careers. It includes questions based on long-term goals, preferred work environment, interests, and values. So, if you want to learn how to analyze the information gathered from a survey, this article is for you. This article will take you through Career aspirations survey analysis using Python.
Career Aspirations Survey Analysis
The information gathered from a career aspirations survey helps in understanding an individual’s:
- long term goals
- preferred work environment
- preferred role and responsibilities
- interests and values
I found an ideal dataset on Kaggle that contains questions based on all the information needed to understand the career aspirations of Genz. You can download the dataset from here.
In the section below, I will take you through Career aspirations survey analysis using Python, where we will understand the career aspirations of Genz.
Career Aspirations Survey Analysis using Python
Let’s start the task of career aspirations survey analysis by importing the necessary Python libraries and the dataset:
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv("Your Career Aspirations of GenZ.csv")
print(data.head())Your Current Country. Your Current Zip Code / Pin Code Your Gender \ 0 India 273005 Male 1 India 851129 Male 2 India 123106 Female 3 India 834003 Male 4 India 301019 Female Which of the below factors influence the most about your career aspirations ? \ 0 People who have changed the world for better 1 People who have changed the world for better 2 Social Media like LinkedIn 3 People from my circle, but not family members 4 Influencers who had successful careers Would you definitely pursue a Higher Education / Post Graduation outside of India ? If only you have to self sponsor it. \ 0 Yes, I will earn and do that 1 No, But if someone could bare the cost I will 2 Yes, I will earn and do that 3 No, But if someone could bare the cost I will 4 No, But if someone could bare the cost I will How likely is that you will work for one employer for 3 years or more ? \ 0 This will be hard to do, but if it is the righ... 1 This will be hard to do, but if it is the righ... 2 Will work for 3 years or more 3 This will be hard to do, but if it is the righ... 4 Will work for 3 years or more Would you work for a company whose mission is not clearly defined and publicly posted. \ 0 No 1 No 2 Yes 3 No 4 No How likely would you work for a company whose mission is misaligned with their public actions or even their product ? \ 0 Will NOT work for them 1 Will NOT work for them 2 Will work for them 3 Will NOT work for them 4 Will NOT work for them How likely would you work for a company whose mission is not bringing social impact ? \ 0 4 1 1 2 7 3 6 4 5 What is the most preferred working environment for you. \ 0 Fully Remote with No option to visit offices 1 Fully Remote with Options to travel as and whe... 2 Hybrid Working Environment with less than 15 d... 3 Hybrid Working Environment with less than 15 d... 4 Fully Remote with Options to travel as and whe... Which of the below Employers would you work with. \ 0 Employer who rewards learning and enables that... 1 Employer who pushes your limits by enabling an... 2 Employer who pushes your limits by enabling an... 3 Employer who pushes your limits by enabling an... 4 Employer who appreciates learning and enables ... Which type of learning environment that you are most likely to work in ? \ 0 Instructor or Expert Learning Programs, Trial ... 1 Self Paced Learning Portals, Instructor or Exp... 2 Self Paced Learning Portals, Trial and error b... 3 Instructor or Expert Learning Programs, Trial ... 4 Self Paced Learning Portals, Learning by obser... Which of the below careers looks close to your Aspirational job ? \ 0 Business Operations in any organization, Build... 1 Business Operations in any organization, Build... 2 Manage and drive End-to-End Projects or Produc... 3 Business Operations in any organization, Manag... 4 Teaching in any of the institutes/online or Of... What type of Manager would you work without looking into your watch ? \ 0 Manager who explains what is expected, sets a ... 1 Manager who explains what is expected, sets a ... 2 Manager who explains what is expected, sets a ... 3 Manager who explains what is expected, sets a ... 4 Manager who explains what is expected, sets a ... Which of the following setup you would like to work ? 0 Work alone, Work with 2 to 3 people in my team... 1 Work with 5 to 6 people in my team 2 Work with 2 to 3 people in my team, Work with ... 3 Work with 2 to 3 people in my team 4 Work with 2 to 3 people in my team, Work with ...
The column names in the dataset contain questions asked in the survey, and the rows contain the answers submitted by the people of generation z. Let’s look at all the questions asked in the survey:
print(data.columns)
Index(['Your Current Country.', 'Your Current Zip Code / Pin Code',
'Your Gender',
'Which of the below factors influence the most about your career aspirations ?',
'Would you definitely pursue a Higher Education / Post Graduation outside of India ? If only you have to self sponsor it.',
'How likely is that you will work for one employer for 3 years or more ?',
'Would you work for a company whose mission is not clearly defined and publicly posted.',
'How likely would you work for a company whose mission is misaligned with their public actions or even their product ?',
'How likely would you work for a company whose mission is not bringing social impact ?',
'What is the most preferred working environment for you.',
'Which of the below Employers would you work with.',
'Which type of learning environment that you are most likely to work in ?',
'Which of the below careers looks close to your Aspirational job ?',
'What type of Manager would you work without looking into your watch ?',
'Which of the following setup you would like to work ?'],
dtype='object')
Now let’s analyze all the questions in the survey one by one. Let’s start by looking at the current country of the people who submitted answers to this career aspirations survey:
country = data["Your Current Country."].value_counts()
label = country.index
counts = country.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Current Country')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
So 98.3% of the people who submitted answers to the survey live in India. Now let’s have a look at the factors influencing the career aspirations of Genz:
question1 = data["Which of the below factors influence the most about your career aspirations ?"].value_counts()
label = question1.index
counts = question1.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Factors influencing career aspirations')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 33.6% of the Genz are influenced by parents
- 24.3% are influenced by the people who have changed the world for the better
- 16.6% are influenced by people from their circle but not family
- 15.7% are influenced by influencers having successful careers
Now let’s have a look at how many want to pursue higher education outside India with their investment:
question2 = "Would you definitely pursue a Higher Education / Post Graduation outside of India ? If only you have to self sponsor it."
question2 = data[question2].value_counts()
label = question2.index
counts = question2.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Will you pursue a Higher Education outside India with your investment?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 46.8% believe in pursuing higher education outside India with their self-earned income
- 27.7% don’t want to pursue higher education outside of India
- 25.5% can only pursue higher education outside India if someone can bare that cost
Now let’s have a look at how likely Genz is to work for one employer for three years or more:
question3 = "How likely is that you will work for one employer for 3 years or more ?"
question3 = data[question3].value_counts()
label = question3.index
counts = question3.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='How likely is that you will work for one employer for 3 years or more?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 59.1% find it hard, but they can only if the company is good
- 33.6% don’t have any problem working for three years or more
- and only 7.23% say that they will don’t work for so long by any chance
Now let’s have a look at whether they will work for a company whose mission is not clearly defined:
question4 = "Would you work for a company whose mission is not clearly defined and publicly posted."
question4 = data[question4].value_counts()
label = question4.index
counts = question4.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Would you work for a company whose mission is not clearly defined and publicly posted?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 66.8% say no
- and 33.2% don’t have any problem with it
Now let’s have a look at whether Genz will work for a company whose mission misaligns with its actions:
question5 = "How likely would you work for a company whose mission is misaligned with their public actions or even their product ?"
question5 = data[question5].value_counts()
label = question5.index
counts = question5.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='How likely would you work for a company whose mission is misaligned with their actions?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 67.2% say no
- and 32.8% don’t have any problem with it
Now let’s see how likely the Genz will work for a company whose mission is not bringing any social impact (on a scale of 1 – 10):
question6 = "How likely would you work for a company whose mission is not bringing social impact ?"
question6 = data[question6].value_counts()
label = question6.index
counts = question6.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='How likely would you work for a company whose mission is not bringing social impact?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Most people are unhappy working with a company whose mission is not to bring any social impact. Now let’s have a look at the preferred working environment of Genz:
question7 = "What is the most preferred working environment for you."
question7 = data[question7].value_counts()
label = question7.index
counts = question7.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='What is the most preferred working environment for you?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 25.5% of Genz want a fully remote working environment with options to travel
- 24.3% of Genz want a hybrid working environment with less than 15 days a month at the office
- 21.3% of Genz are happy with every day at the office
Now let’s have a look at what kind of employers Genz wants to work with:
question8 = "Which of the below Employers would you work with."
question8 = data[question8].value_counts()
label = question8.index
counts = question8.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Which of the below Employers would you work with?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 48.1% of Genz wants employers who push their limits by enabling a learning environment and reward them at the end
- 31.9% want employers who appreciate a learning environment
- 15.3% want an employer who enables a rewarding environment
Now let’s have a look at which type of learning environment the Genz are most likely to work in:
question9 = "Which type of learning environment that you are most likely to work in ?"
question9 = data[question9].value_counts()
label = question9.index
counts = question9.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Which type of learning environment that you are most likely to work in?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Observations:
- 25.1% want a self-paced learning portal and an instructor learning program
- 19.1% want an instructor or expert learning program and trial-and-error side projects within the company
- 17.4% want an instructor or expert learning program and want to learn by observing others
- 16.2% want a self-paced learning portal and want to learn by observing others
Now let’s have a look at what type of managers Genz wants to work under:
question10 = "What type of Manager would you work without looking into your watch ?"
question10 = data[question10].value_counts()
label = question10.index
counts = question10.values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='What type of Manager would you work without looking into your watch?')
fig.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
Most Genz wants managers who can explain expectations clearly, and who set goals and help achieve them.
So this is how you can analyze the data collected from a survey using the Python programming language.
Summary
So this is how we can analyze a career aspirations survey using Python. A Career Aspirations Survey gathers information about the goals and aspirations of people in their careers. It includes questions based on long-term goals, preferred work environment, interests, and values. I hope you liked this article on Career Aspirations Survey analysis using Python. Feel free to ask valuable questions in the comments section below.
