Number of Videos: 0.5 hours – 10 lessons
Author: Mikhail Klassen
User Level: Beginner

LinkedIn is the largest professional social network in the world and is used by millions of job seekers, employers, recruiters, and other professionals. How can we mine this data to gain deeper insights into our professional networks?

Based on content from Matthew Russell’s book “Mining the Social Web” (O’Reilly), this course shows you how to access and download LinkedIn data; as well as how to perform clustering analysis and geographic analysis, and how to visualize data in new and informative ways. The course works best for learners with some basic Python experience. Understand how to access LinkedIn using the LinkedIn API and Python Learn how to download your own LinkedIn data and access your connections Explore techniques for dealing with messy data like similar titles or job descriptions Discover methods for clustering your contacts into similar jobs or grouping them by geography Lean how to produce intuitive data visualizations and output geographic data to Google Earth

After completing his PhD in astrophysics, Mikhail Klassen transitioned to data science and refined his expertise in data mining, data analysis, and machine learning. He’s now the Chief Data Scientist for Paladin: Paradigm Knowledge Solutions in Montreal, where he combines data mining and artificial intelligence to deliver personalized training for the aerospace industry.

Mac Minimum System Requirements:Mac Recommended System Requirements:Processor:   AnyRAM:   AnyHard Disk:   1GBVideo Card:   AnySupported OS:   Mac OS Sierra 10.12, Mac El Capitan 10.11, Mac Yosemite 10.10, Mac Mavericks 10.9, Mac Mountain Lion 10.8, Mac Lion 10.7, Mac Snow Leopard 10.6, Mac Leopard 10.5, Mac OS X, Macintosh

Product Features

• Learn Mining the Social Web – LinkedIn from a professional trainer on your own time at your own desk.
• This visual training method offers users increased retention and accelerated learning.
• Breaks even the most complex applications down into simplistic steps
• Comes with Extensive Working Files