Fundamentals of Data Mining

Fundamentals of Data Mining

An ever-increasing volume of research and industry data is being collected on a daily basis. Skilled data scientists are needed to process and filter the data, to detect new patterns or anomalies within the data, and gain deeper insight from the data.

This course provides students with a foundation in basic data mining, data analysis, and predictive modelling concepts and algorithms. Using practical exercises, students will learn data analysis and machine learning techniques for model and knowledge creation through a process of inference, model fitting, or learning from examples.

Topics include:

• Introduction to data mining and big data
• Data mining process and standards
• Classification and prediction methods
• Preparing input and output
• Decision tables
• Decision trees
• Classification rules
• Bayesian learning
• Association rules
• Numeric prediction: regression and model trees
• Clustering: k-means, hierarchical, probabilistic, EM
• Model training, testing, and evaluation

Practical experience:

• 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: Statistics for Data Analytics or equivalent working knowledge is required. Linear Algebra for Machine Learning is also recommended, but not required. You can test your level of statistical knowledge by taking the online Self-Assessment quiz.

Next Steps: Upon completion of this course, consider taking Data Preparation for Analytics to continue learning.

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

Course Number: CSE-41258
Credit: 3.00 unit(s)
Related Certificate Programs: Data Mining for Advanced Analytics

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3/28/2023 - 5/27/2023

$725

Online

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• SECTION ID:

  170437

• 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:
  

  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.

• INSTRUCTOR:

  Wolter, Nicole, Research Program Analyst, San Diego Supercomputer Center, UC San Diego

  Nicole Wolter has over 10 years of experience in high performance computing. She has spent six years doing research in performance modeling and characterization at UC San Diego. She has excellent analytical and model development skills most recently applied in the areas of medical informatics, sports analytics and large data analysis. She has conducted a number of data mining classes and lectures.

• 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.