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Mining and Analyzing LinkedIn Data

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Mining and Analyzing LinkedIn Data, available at $69.99, has an average rating of 4.25, with 54 lectures, based on 60 reviews, and has 1064 subscribers.

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You will learn about 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 This course is ideal for individuals who are Anyone interested in data analysis using social media data or People interested in applying Artificial Intelligence and Data Science techniques to data extracted from social networks or People interested in extracting data from social networks or Undergraduate students who are studying subjects related to Artificial Intelligence, Data Science or Data Analysis or People who want to get to know their LinkedIn contacts better It is particularly useful for Anyone interested in data analysis using social media data or People interested in applying Artificial Intelligence and Data Science techniques to data extracted from social networks or People interested in extracting data from social networks or Undergraduate students who are studying subjects related to Artificial Intelligence, Data Science or Data Analysis or People who want to get to know their LinkedIn contacts better.

Enroll now: Mining and Analyzing LinkedIn Data

Summary

Title: Mining and Analyzing LinkedIn Data

Price: $69.99

Average Rating: 4.25

Number of Lectures: 54

Number of Published Lectures: 53

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 53

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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

Target Audiences

  • 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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: Course materials

Chapter 2: LinkedIn datasets

Lecture 1: Plan of attack

Lecture 2: Creating a LinkedIn APP

Lecture 3: LinkedIn API 1

Lecture 4: LinkedIn API 2

Lecture 5: Getting data from LinkedIn

Lecture 6: Connections dataset

Lecture 7: Invitations dataset 1

Lecture 8: Invitations dataset 2

Lecture 9: Generating fake data

Lecture 10: Messages dataset

Chapter 3: Connections between users and invitations

Lecture 1: Plan of attack

Lecture 2: Connections by day

Lecture 3: HOMEWORK

Lecture 4: Homework solution

Lecture 5: Companies data

Lecture 6: Positions data

Lecture 7: Levenshtein distance

Lecture 8: N-gram similarity

Lecture 9: Jaccard distance

Lecture 10: Clustering similar positions 1

Lecture 11: Clustering similar positions 2

Lecture 12: Clustering similar positions 3

Lecture 13: Clustering similar positions 4

Lecture 14: Visualizing the clusters

Lecture 15: Exporting to JSON

Lecture 16: Visualizing using dendrogram

Lecture 17: Visualizing using link tree

Lecture 18: Google location API

Lecture 19: Using the location API

Lecture 20: Latitude and longitude of the contacts

Lecture 21: Contact map using Basemap

Lecture 22: Getting countries and cities

Lecture 23: Graph of users by countries and cities

Lecture 24: Introduction to clustering

Lecture 25: Introduction to k-means algorithm

Lecture 26: Clustering users by location with k-means

Lecture 27: Visualizing the clusters using Google Earth

Lecture 28: Invitations dataset

Lecture 29: HOMEWORK

Lecture 30: Homework solution

Lecture 31: Analysis of the invitations dataset

Chapter 4: Messages between users

Lecture 1: Plan of attack

Lecture 2: Loading the dataset

Lecture 3: Preprocessing the texts

Lecture 4: Preprocessing the dataset

Lecture 5: Detecting languages

Lecture 6: Word cloud

Lecture 7: Named entity recognition

Lecture 8: Sentiment analysis

Chapter 5: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 0 votes
  • 3 stars: 6 votes
  • 4 stars: 16 votes
  • 5 stars: 37 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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