Data Mining: Advanced Concepts and Algorithms
As the amount of research and industry data being collected daily continues to grow, intelligent software tools are increasingly needed to process and filter the data, detect new patterns and similarities within it, and extract meaningful information from it. Data mining and predictive modeling offer a means of effective classification and analysis of large, complex, multi-dimensional data, leading to discovery of functional models, trends and patterns.
Building upon the skills learned in previous courses, this course covers advanced data mining, data analysis, and pattern recognition concepts and algorithms, as well as models and machine learning algorithms.
- Data mining with big data
- Artificial neural networks
- Feed-forward networks
- Radial-basis functions
- Recurrent neural networks
- Probability graph models and Bayesian learning
- Hidden Markov models
- Support vector machines
- Ensemble learning: bagging, boosting, stacking
- Random forests
- Data mining tools
- Text mining
- Hands-on data mining projects
Software: WEKA is used for class assignments. There is no additional cost for this product.
Course typically offered: Online in Fall and Spring
Prerequisites: Data Preparation for Analytics and Fundamentals of Data Mining or equivalent experience required.
Next Steps: Upon completion of this course, consider taking the Data Mining Practicum to continue learning.
More Information: For more information about this course, please contact email@example.com.
Course Number: CSE-41262
Credit: 3.00 unit(s)
Related Certificate Programs: Data Mining for Advanced Analytics
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3/28/2023 - 5/27/2023
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.
Sipes, Tamara, Data Mining Specialist
Tamara Sipes is a data mining specialist. She uses her data mining expertise to analyze data, select meaningful attributes and build predictive models that discover significant trends and relationships. Her work has led to patent awards for clients in biotechnology and other industries, and she has published research in the areas of data mining and learning technologies.
Balac, Natasha, President and CEO, Data Insight Discovery, Inc.
Natasha Balac received her master's and Ph.D. in computer science from Vanderbilt University with an emphasis in data mining from large data sets. Her dissertation focused on creating and applying novel data mining techniques to mobile robots and real time sensor data. Balac has held several positions within UC San Diego since 2002 and is currently the director of the Interdisciplinary Center for Data Science. She has also led multiple collaborations across a wide range of organizations in industry, government and academia. She founded the Predictive Analytics Center of Excellence at the Supercomputer Center, lead the data science program at Calit2/Qualcomm institute and lectures in the computer science department at UC San Diego Extension. Dr. B...Read More
No information available at this time.
No refunds after: 4/3/2023.
3/28/2023 - 5/27/2023
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 firstname.lastname@example.org for information about when this course will be offered again.