1

Google Jobs Analysis using Python

 2 years ago
source link: https://www.journaldev.com/57952/google-jobs-analysis-using-python
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
neoserver,ios ssh client

Google Jobs Analysis using Python

Filed Under: Python Advanced

As a person who dreams big, there is this one question that always comes to mind. The question is what language, skills, or experience should I have to my skill set to grab a job at Google. 

Lucky for you, there is a dataset available right here which scrapes right from the jobs portal. The dataset has the Job Titles, Job Locations, Job Categories, Job responsibilities, minimum and preferred qualifications for each job.

In this tutorial, we will be working and analyzing the Job Dataset. We will try getting as much information as possible from the dataset.

Google provides the latest tools and technology to its workers and a friendly work culture, which makes the company a favorite among job seekers.

Importing Libraries

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

Load and Check Google Dataset

In this article, we will use the job dataset that we can load in the program using pandas read_csv method.

data = pd.read_csv('job_skills.csv')
data.head()
Google Job DatasetGoogle Job Dataset

Know and visualize how many departments are present

We will be counting the number of unique departments in the dataset with the help of the value_count() function. The function returns the counts of unique values in descending order so that the first element is the most frequently-occurring element.

all_cat = data['Category'].value_counts()
all_cat
Google Job All DepartmentsGoogle Job All Departments

We can also visualize the categories and their counts using the code below with the help of a bar chart.

sns.catplot(x="Category",data=data, kind="count",height=8.27, aspect=15/8)
plt.title('Categories Google Hire For!', fontsize = 20)
plt.xticks(rotation=90)
plt.show()
Google Job Departments Bar PlotGoogle Job Departments Bar Plot

From the two results, we can observe the following:

  1. The company hires maximum in the Sales category and,
  2. It hires least in Data and network category

Know and visualize what locations Google hire for

We can also know that company hires maximum people for which location using the visualization we obtain on executing the code below.

plt.style.use('seaborn')
sns.catplot(x="Location",data=data, kind="count",height=8.27, aspect=15/8)
plt.title('Locations Google Hire For!', fontsize = 20)
plt.xticks(rotation=90)
plt.show()
Google Job Locations Bar PlotGoogle Job Locations Bar Plot

From the visualization, we can observe that Google hires maximum in New York.

Know the Minimum Qualifications for Google

You can also understand what minimum qualifications will increase your chances of grabbing a job at your dream company!

all_min_qual = data['Minimum Qualifications'].value_counts()
pd.DataFrame(all_min_qual)
Minimum Qualifications ListGoogle Job Minimum Qualifications List

Conclusion

This was just a small tutorial on how to start analyzing data from a CSV file. You can go deeper into data analysis using more functions.

Thank you for reading the tutorial!

You can also check out the following tutorials in case you like this one:


report

Recommend

About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK