OREILLY_MINING_THE_SOCIAL_WEB_TWITTER_TUTORIAL-OXBRiDGE

Section
Appz
Group
OXBRiDGE
Size
301,95 MB
Files
9
Date
2017-06-01

NFO

                                           
░ ░▒▒▒▓▓█████▌░░ University of OXford & University of camBRiDGE ░░▐████▓▓▒▒▒░ ░
   ▄▄██▓▄ ▐███▌  ████▐███████▓▄ █████████▄ ▐███▌██████▓▄▄    ▄▄▓████▓ ▐████████
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  ...is a collective term for characteristics that the two institutions share.


     ·-- PUBLISHER -----·> Infinite Skills                                    
     ·-- LECTURESHIP ---·> Mining the Social Web - Twitter                    
                                                                              
                                                                              

     ·-- LECTURE DATE --·> 06-2017           
                           05-2017 {Published}
    
     ·-- PERIOD in HRS--·> 00+ e
     ·-- SCALE ---------·> 07x50mb            
     
     ·-- WORKING FILES--·> [■] Included
                           [ ] Without 

     ·-- LECTURE LINK --·> https://goo.gl/2q8Us3                     
                                                                                              
 
     
     Interested in tapping into Twitter data so you can discover what's      
     trending, what people are talking about, and what feelings are being    
     expressed in people's tweets? This course teaches you how to use a      
     powerful set of tools that will allow you to acquire, analyze, and      
     summarize Twitter data.                                                 
                                                                             
     You'll learn the meanings within Twitter's metadata, explore the data   
     mining techniques of frequency analysis and sentiment, and gain         
     experience using Python as a data mining tool. Learners should be       
     familiar with Jupyter Notebooks and be able to install Python packages  
     on their own using the command line.                                    
                                                                             
     Learn how to interpret the metadata that accompanies every Tweet        
     Master the ability to connect to the Twitter API using Python           
     Acquire real life experience using Python for data mining               
     Understand how to perform a frequency analysis of different words,      
     users, or hashtags                                                      
     Learn to measure the emotional tone of Tweets by performing a sentiment 
     analysis                                                                
     Gain experience downloading live Twitter datastreams and analyzing them 
     for trends                                                              
     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.                                                 
     

Files

PathSize
ox_omtswtt.r0047,68 MB
ox_omtswtt.r0147,68 MB
ox_omtswtt.r0247,68 MB
ox_omtswtt.r0347,68 MB
ox_omtswtt.r0447,68 MB
ox_omtswtt.r0515,84 MB
ox_omtswtt.rar47,68 MB
ox_omtswtt.sfv175 B
oxbridge.nfo3,54 KB