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Competition - Employee Network Analysis

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How can the company improve collaboration?

📖 Background

You work in the analytics department of a multinational company, and the head of HR wants your help mapping out the company's employee network using message data.

They plan to use the network map to understand interdepartmental dynamics better and explore how the company shares information. The ultimate goal of this project is to think of ways to improve collaboration throughout the company.

💾 The data

The company has six months of information on inter-employee communication. For privacy reasons, only sender, receiver, and message length information are available (source).

Messages has information on the sender, receiver, and time.
  • "sender" - represents the employee id of the employee sending the message.
  • "receiver" - represents the employee id of the employee receiving the message.
  • "timestamp" - the date of the message.
  • "message_length" - the length in words of the message.
Employees has information on each employee;
  • "id" - represents the employee id of the employee.
  • "department" - is the department within the company.
  • "location" - is the country where the employee lives.
  • "age" - is the age of the employee.

Acknowledgments: Pietro Panzarasa, Tore Opsahl, and Kathleen M. Carley. "Patterns and dynamics of users' behavior and interaction: Network analysis of an online community." Journal of the American Society for Information Science and Technology 60.5 (2009): 911-932.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import timedelta
messages = pd.read_csv('data/messages.csv', parse_dates= ['timestamp'])
employees = pd.read_csv('data/employees.csv')
employees[employees.id.isin([605,598])]

Executive Summary

  • The departments with the most activity are Sales, Operations and Admin
  • The employee with the most connections is 598 from Operations department. And the one with more messages is 605 from Admin department
  • The most influent employee 605 and the department is Admin

Before start doing any analysis it's always good to check for inconsistencies

# checking the data for inconsistencies
employees.info()
messages.info()

There's no null data in both dataset

employees.describe()
employees.department.value_counts()
employees.location.value_counts()
messages.describe()



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