Udemy - Mining and Analyzing LinkedIn Data

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2.3 GB
  • Uploaded Byfreecoursewb
  • Downloads71
  • Last checkedFeb. 06th '22
  • Date uploadedFeb. 04th '22
  • Seeders 2
  • Leechers16

Infohash : AFC4C0F2019C85327536686FFA5427E4C375B918

Mining and Analyzing LinkedIn Data



https://DevCourseWeb.com

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.29 GB | Duration: 6h 18m

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.

Files:

[ DevCourseWeb.com ] Udemy - Mining and Analyzing LinkedIn Data
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Course content.mp4 (49.1 MB)
    • 1. Course content.srt (7.7 KB)
    • 2. Course materials.html (0.3 KB)
    • 3. Bonus course - valid until January 31st.html (0.3 KB)
      • Course materials
      • Mining and Analyzing LinkedIn Data.pdf (3.2 MB)
      • clusters.kml (3.4 KB)
      • connections.csv (94.9 KB)
      • connections.kml (270.7 KB)
      • connections_location.csv (124.9 KB)
      • connections_locations_full.csv (147.2 KB)
      • data.json (125.5 KB)
      • invitations.csv (5.5 KB)
      • invitations_locations.csv (15.1 KB)
      • messages.csv (184.3 KB)
      __MACOSX Course materials
      • _Mining and Analyzing LinkedIn Data.pdf (0.6 KB)
      • _clusters.kml (0.6 KB)
      • _connections.csv (0.3 KB)
      • _connections.kml (0.6 KB)
      • _connections_location.csv (0.3 KB)
      • _connections_locations_full.csv (0.6 KB)
      • _data.json (0.3 KB)
      • _invitations.csv (0.6 KB)
      • _invitations_locations.csv (0.3 KB)
      • _messages.csv (0.3 KB)
      • _Course materials (0.2 KB)
        • 2. LinkedIn datasets
        • 1. Plan of attack.mp4 (8.6 MB)
        • 1. Plan of attack.srt (3.1 KB)
        • 1.1 Source code - Google Colab.html (0.1 KB)
        • 10. Messages dataset.mp4 (28.3 MB)
        • 10. Messages dataset.srt (4.9 KB)
        • 2. Creating a LinkedIn APP.mp4 (24.9 MB)
        • 2. Creating a LinkedIn APP.srt (5.8 KB)
        • 3. LinkedIn API 1.mp4 (73.8 MB)
        • 3. LinkedIn API 1.srt (13.7 KB)
        • 4. LinkedIn API 2.mp4 (104.0 MB)
        • 4. LinkedIn API 2.srt (11.8 KB)
        • 5. Getting data from LinkedIn.mp4 (14.7 MB)
        • 5. Getting data from LinkedIn.srt (2.7 KB)
        • 6. Connections dataset.mp4 (59.6 MB)
        • 6. Connections dataset.srt (11.0 KB)
        • 7. Invitations dataset 1.mp4 (28.4 MB)
        • 7. Invitations dataset 1.srt (5.0 KB)
        • 8. Invitations dataset 2.mp4 (64.1 MB)
        • 8. Invitations dataset 2.srt (10.6 KB)
        • 9. Generating fake data.mp4 (33.4 MB)
        • 9. Generating fake data.srt (5.0 KB)
        3. Connections between users and invitations
        • 1. Plan of attack.mp4 (35.7 MB)
        • 1. Plan of attack.srt (5.1 KB)
        • 1.1 Source code - Google Colab.html (0.1 KB)
        • 10. Clustering similar positions 1.mp4 (54.2 MB)
        • 10. Clustering similar positions 1.srt (8.4 KB)
        • 11. Clustering similar positions 2.mp4 (53.8 MB)
        • 11. Clustering similar positions 2.srt (8.1 KB)
        • 12. Clustering similar positions 3.mp4 (59.6 MB)
        • 12. Clustering similar positions 3.srt (8.2 KB)
        • 13. Clustering similar positions 4.mp4 (49.6 MB)
        • 13. Clustering similar positions 4.srt (6.3 KB)
        • 14. Visualizing the clusters.mp4 (103.8 MB)
        • 14. Visualizing the clusters.srt (11.3 KB)
        • 15. Exporting to JSON.mp4 (41.2 MB)
        • 15. Exporting to JSON.srt (6.4 KB)
        • 16. Visualizing using dendrogram.mp4 (54.3 MB)
        • 16. Visualizing using dendrogram.srt (6.2 KB)
        • 17. Visualizing using link tree.mp4 (17.4 MB)
        • 17. Visualizing using link tree.srt (1.9 KB)
        • 18. Google location API.mp4 (48.2 MB)
        • 18. Google location API.srt (9.3 KB)
        • 19. Using the location API.mp4 (34.2 MB)
        • 19. Using the location API.srt (4.4 KB)
        • 2. Connections by day.mp4 (81.1 MB)
        • 2. Connections by day.srt (11.2 KB)
        • 20. Latitude and longitude of the contacts.mp4 (31.5 MB)
        • 20. Latitude and longitude of the contacts.srt (4.2 KB)
        • 21. Contact map using Basemap.mp4 (66.2 MB)
        • 21. Contact map using Basemap.srt (9.3 KB)
        • 22. Getting countries and cities.mp4 (52.7 MB)
        • 22. Getting countries and cities.srt (6.5 KB)
        • 23. Graph of users by countries and cities.mp4 (71.7 MB)
        • 23. Graph of users by countries and cities.srt (8.5 KB)
        • 24. Introduction to clustering.mp4 (14.0 MB)
        • 24. Introduction to clustering.srt (2.8 KB)
        • 25. Introduction to k-mean algorithm.mp4 (12.5 MB)
        • 25. Introduction to k-mean algorithm.srt (4.6 KB)
        • 26. Clustering users by location with k-means.mp4 (62.3 MB)
        • 26. Clustering users by location with k-means.srt (7.6 KB)
        • 27. Visualizing the clusters using Google Earth.mp4 (95.2 MB)
        • 27. Visualizing the clusters using Google Earth.srt (7.5 KB)
        • 28. Invitations dataset.mp4 (16.3 MB)
        • 28. Invitations dataset.srt (3.1 KB)
        • 29. HOMEWORK.html (0.3 KB)
        • 3. HOMEWORK.html (0.3 KB)
        • 30. Homework solution.mp4 (20.8 MB)
        • 30. Homework solution.srt (2.7 KB)
        • 31. Analysis of the invitations dataset.mp4 (41.5 MB)
        • 31. Analysis of the invitations dataset.srt (5.2 KB)
        • 4. Homework solution.mp4 (27.0 MB)
        • 4. Homework solution.srt (3.9 KB)
        • 5. Companies data.mp4 (74.6 MB)
        • 5. Companies data.srt (10.0 KB)
        • 6. Positions data.mp4 (85.4 MB)
        • 6. Positions data.srt (8.4 KB)
        • 7. Levenshtein distance.mp4 (47.7 MB)
        • 7. Levenshtein distance.srt (6.5 KB)
        • 8. N-gram similarity.mp4 (80.5 MB)
        • 8. N-gram similarity.srt (10.8 KB)
        • 9. Jaccard distance.mp4 (74.5 MB)
        • 9. Jaccard distance.srt (8.4 KB)
        4. Messages between users
        • 1. Plan of attack.mp4 (16.0 MB)
        • 1. Plan of attack.srt (3.2 KB)
        • 1.1 Source code - Google Colab.html (0.1 KB)
        • 2. Loading the dataset.mp4 (16.0 MB)
        • 2. Loading the dataset.srt (3.0 KB)
        • 3. Preprocessing the texts.mp4 (8

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