Advanced Business Intelligence: Introduction to Predictive Analytics

Advanced Business Intelligence: Introduction to Predictive Analytics

This course introduces the predictive modeling process and basics of predictive analytics for business applications, including hands-on introduction to data preparation, model identification and validation, model documentation, and interpretation of model results.

Topics include:

• Explain predicative analytics key concepts and terms, benefits, and applications
• Identify and set up the business problem for predictive analytics
• Understand the steps to creating a predictive model
• Comprehend the data mining process, including data collection or selection, data cleansing, evaluation of results, best practices and common mistakes
• Explore visualizing and sharing model results
• Perform various data mining techniques, including decision trees, regression, cluster analysis, Artificial Neural Networks, and various ensemble methods

Practical experience:

• Examine case studies of successful data mining applications in business, industry, and science

Software: R Programming and Weka 3 will be used in this course. Both are open source and can be downloaded at no additional cost.

Course typically offered: Online in Fall and Spring.

Prerequisites: CSE-41198: Introduction to Statistics Using R or previous background knowledge and experience with a programming language and statistics.

Next step: Upon completion of this course, consider taking Fundamentals of Data Mining for further learning.

More information: For more information about this course, please contact unex-techdata@ucsd.edu.

Course Number: CSE-41288
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence Analysis, Machine Learning Methods, R for Data Analytics

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3/28/2023 - 5/27/2023

$725

Online

Cancelled

• SECTION ID:

  170432

• CLASS TYPE:

  Online Asynchronous.
  
  This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
  You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.

• INSTRUCTOR:
  

  Hallett, Melodie

  Melodie Hallett is a Data Mining Specialist, Statistical Consultant, and Educator. Melodie received her M.S.in Statistics from Miami University and her Ph.D. in Computational Science with an emphasis in Statistics from Claremont University jointly with SDSU. Her dissertation focused on creating and applying novel data mining techniques, particularly random forest models, to correlated survival data. Melodie has taught at SDSU since 2001, focusing on courses in Statistics, Data Mining, SAS, SAS for Econometrics, and SPSS. Melodie has written a course reader for Statistics using SPSS. She has also been an expert witness for statistics and data mining on a class action lawsuit. She has over 20 years experience as a statistical consultant working with companies including Proctor &...Read More

• TEXTBOOKS:

  No information available at this time.

• POLICIES:

  No refunds after: 4/3/2023.
  

• NOTE:

• DATE & LOCATION:

  3/28/2023 - 5/27/2023
  extensioncanvas.ucsd.edu
  You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.