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Efficient collection of online user behavior information (Self-Paced Study)

Efficient collection of online user behavior information (Self-Paced Study)


  • 445.00

Why take this course:

Do you want to understand customer behavior on your website? Do you want to know the performance of your website features? Do you want to improve the features by leveraging the user behavior information?

Using the self-paced instructional approach, this course provides a unique opportunity for learners to gain knowledge of how to track user behavior, understand why it is necessary, determine what data to collect, and master the techniques for implementing the tracking system using both third-party and self-designed solutions.

Valuable skills such as Google Analytics, Hadoop, Flume, Hive, Kafka, Storm are  introduced for the purpose of the data tracking system.

One of the strengths of this course is the use of a real life case study to ensure the learner is able to apply and retain essential practical skills acquired.


Target audience:

  • Data scientists
  • Data Engineers
  • Data Analysts
  • Research Scientists in the internet space


Instructor: Sean Huang

 Shen (Sean) Huang

Big Data Architect Sean has conducted cutting-edge industry research for Microsoft Research Asia, eBay Research Lab and Search Science for over 8 years, focusing on data mining, machine learning and information retrieval. His research background includes participating in building online search and recommendation systems for two Chinese e-commerce sites over a 4 year period, achieving significant practical experience in productizing applied research.

A Ph.D in Computer Science, machine learning and big data mining expert, Sean has commercialized over 10 algorithms, published 20 academic publications with 97 independent citations around the world, and filed 13 patents with 4 issued by the United States Patent and Trademark Office. He has achieved numerous awards from industry leading companies, international conferences and other prestigious institutions, and has been invited to deliver expert reviews for Computer Engineering Journals (Chinese).

A USA EB1A (Alien of Extraordinary Ability) holder since 2015, Sean is a Microsoft Scholar and IBM Extreme Blue member. He is highly proficient in Solr/Lucene, Text Categorization/Clustering, Linear Regression, HBase, Java, Maven, Spring and knowleageable in Elasticsearch, MapReduce, Hive, Mahout, Spark, Kafka, C++, R, C#. Results-oriented and with strong communication skills, Sean plans to bring a wealth of technical intellectual capital and collaborative spirit to the ConverseLink ecosystem.


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