Collect and Analyze User Behavior Information for Large-Scale Online Systems (Self-Paced Study)

Collect and Analyze User Behavior Information for Large-Scale Online Systems (Self-Paced Study)

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Course Summary:

Nowadays, an unbelievable amount of rich customer information hidden on the pages of your website can be captured for analysis via web, mobile, IoT and other device interactions. Do you want to gain a better understanding of online customer behavior? Do you want to understand the performance of your website features? Do you want to improve the features by leveraging the user behavior information? 

Using the convenience of self-paced study, this course provides a unique opportunity to learn how to track user behavior, determine what data to collect, understand its importance for building valuable customer relationships, 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 key strengths of this course is the use of a practical case study to demonstrate how real life challenge facing online businesses today can be solved. This provides the learner with rare practical skills, using a well structured, easy to follow narrative.


Target audience:

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


Instructor: 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.