Mining and Analyzing LinkedIn Data

Apply Data Science and Artificial Intelligence techniques to extract and analyze your LinkedIn network

What you'll learn
Extract data from your LinkedIn profile using the LinkedIn API and .csv files
Extract and analyze the connections between users, invitations, and text messages
Generate fake usernames to mask real information
Explore and view data related to your contacts' companies and job titles
Use edit Levenshtein distance, n-gram similarity and Jaccard distance to measure similarity between strings
Cluster contacts based on similarity between positions, as well as generate HTML views to improve data presentation
Use location APIs to extract latitude and longitude of contacts, in order to capture the city and country of lives
View the location of contacts dynamically with Google Earth and the Basemap library
Cluster contacts using the k-means algorithm
Apply natural language processing techniques to analyze your LinkedIn text messages
Generate word cloud to view the most frequent terms
Extract name entities from text messages
Create a sentiment classifier to extract the polarity of the LinkedIn text messages

Requirements
Programming logic
Basic Python programming
No LinkedIn knowledge is necessary
Description
LinkedIn is a social network focused on professional experience in order to generate connections and relationships between professionals from different areas. Professionals can provide profissional skills and search for jobs by connecting with people around the world. For example, if you would like to work with Data Science you can connect with companies and people who work in this field, increasing your chances of getting a job. On the other hand, companies are able to search for candidates according to the curriculum and skills provided by users. In 2017, LinkedIn established itself as the largest business platform and an important strategic tool for both professionals and companies.

It is important that professionals know how to use the data of this social network in their favor. LinkedIn provides some datasets related to your profile, in which it is possible to apply Data Science and Analysis techniques to extract important and interesting insights about our network of connections. We can answer questions like this: What are the main positions of the people who are connected to us? Which companies are sending invitations to our profile? What is the location of our contacts? Is our LinkedIn network made up of people and companies related to our job? Are the companies I want to work for sending invitations to my profile? These and other questions can be answered during this course, so you can analyze if your network is in line with what you want professionally. Below you can see the main topics that will be implemented step by step

Extract data from your LinkedIn profile using the LinkedIn API and .csv files. If you do not have LinkedIn, you will be able to follow the course using the data about my profile

Extract and analyze connections between users, invitations and text messages

Generate fake data to mask real information

Explore and visualize data related to your contacts' companies and job titles

Use Levenshtein distance, n-gram similarity and Jaccard distance to measure similarity between strings

Cluster contacts based on similarity between positions, as well as generate HTML views to improve data presentation

Use location APIs to extract latitude and longitude of contacts to capture the city and country they live

View the location of contacts dynamically with Google Earth and the Basemap library

Cluster contacts using k-means algorithm

Apply natural language processing techniques to analyze your LinkedIn text messages

Generate word cloud to view the most frequent terms

Extract named entities from your text messages

Create a sentiment classifier to extract the polarity from LinkedIn messages

During the course, we will use the Python programming language and Google Colab, so you do not need to spend time installing the stuff on your own machine. You will be able to follow the course with a browser and an Internet connection! This is the best course if this is your first contact with social media data analysis!

Who this course is for
Anyone interested in data analysis using social media data
People interested in applying Artificial Intelligence and Data Science techniques to data extracted from social networks
People interested in extracting data from social networks
Undergraduate students who are studying subjects related to Artificial Intelligence, Data Science or Data Analysis
People who want to get to know their LinkedIn contacts better