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Advanced Web Analytics: Harnessing the Predictive Power

The success of today’s organizations depends on the ability of workers to make better and faster fact-based decisions to solve complicated business problems. Statistics-based predictive analytics has become the key enabler for this evidence-based management. Digital analytics uses sophisticated traffic information about a web service to deliver a comprehensive array of vital business intelligence and visitor behavior insights.

Using Microsoft Excel, R, and KNIME software tools as a work engine for data analysis, this course will teach the applications of predictive analytics using web analytics data. Popular methods and algorithms, including regression, naive Bayes, and decision trees, will be covered and assessed for their value and validity.

Topics include:

  • Linear regression
  • Logistic regression
  • Naive Bayes
  • Decision trees
  • Visitor segmentation
  • Landing page experiments
  • Predicting visits

Practical experience:

  • Analyze data using Microsoft Excel, R, and/or KNIME

Software: Students will use the R statistical package, Google Analytics, Excel, and/or KNIME during this course.

Course typically offered: Online in Winter and Summer

Prerequisites: Familiarity with Google Analytics or another web analytics tool required. Basic math, including statistics, functions, and matrices, required. Basic programming skills or Introduction to Programming required.

Next Steps: Upon completion of this course, consider taking Fundamentals of Data Mining to continue learning.

More Information: For more information about this course, please contact infotech@ucsd.edu.

Course Number: CSE-41195
Credit: 2.00 unit(s)