Collect and Analyze User Behavior Information for Large-Scale Online Systems (Self-Paced Study)
Nowadays with the advent of the internet, there could be unbelievable amounts of rich customer information captured via web, mobile, and IoT device interactions hidden in the pages of your website that is not being collected and analyzed. Do you want to gain a better understanding of online customer behavior? 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 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 when using 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 application of a case study to ensure real life scenarios are examined, and practical skills are applied.
- 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.
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