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 predictive analytics key concepts and terms, benefits, and applications
- Understand the steps to creating and selecting 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
- Understand various predictive analytics models, including: decision trees, regression, cluster analysis, Artificial Neural Networks, and other models
- Understand how predictive analytics is used and lifecycle in a workplace environment
Learning Outcomes:
- Learn about how predictive analytics works, different types of predictive analytics models, and visualizations that can accompany predictive analytics models
- Create visualizations and predictive analytics models that can be used in a portfolio to show potential employers
- Understand how to collect data for either personal projects or to be used in a workplace environment
- Examine cases of predictive analytics in the real world and ethical issues with cases where predictive analytics are poorly used in the workplace
- Learn important data communication skills for the workplace in addition to how the lifecycle of predictive analytics works in most workplace environments
Hardware/Software Requirements: Anaconda Navigator and Tableau will be used in this course. Both are open source and can be downloaded at no additional cost.
Note: a computer with an OS of either Windows 10 (released 2015) or newer, or a Mac with an OS of 10.14 (Mohave, released in 2018) or newer is required. A computer with at least 4 GBs of RAM is recommended.
Course typically offered: Online in Fall and Spring.
Suggested Prerequisites: Suggested Prerequisites: CSE-41198 Introduction to statistics using R or previous background knowledge and experience with R or Python and statistics.
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, R for Data Analytics
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10/3/2023 - 12/2/2023
$745
Online
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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.
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POLICIES:
No refunds after: 10/9/2023.
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10/3/2023 - 12/2/2023
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.
There are no sections of this course currently scheduled. Please contact the Science & Technology department at 858-534-3229 or unex-sciencetech@ucsd.edu for information about when this course will be offered again.