Data Mining Methods

This course is part of Data Mining Foundations and Practice Specialization

Instructor: Qin (Christine) Lv

What you'll learn

  •   Identify the core functionalities of data modeling in the data mining pipeline
  •   Apply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work.
  •   Evaluate data modeling techniques, determine which is most suitable for a particular task, and identify potential improvements.
  • Skills you'll gain

  •   Supervised Learning
  •   Big Data
  •   Text Mining
  •   Statistical Analysis
  •   Anomaly Detection
  •   Machine Learning Methods
  •   Classification And Regression Tree (CART)
  •   Artificial Neural Networks
  •   Data Mining
  •   Machine Learning Algorithms
  •   Advanced Analytics
  •   Data Science
  •   Algorithms
  •   Exploratory Data Analysis
  •   Predictive Modeling
  •   Statistical Machine Learning
  •   Unsupervised Learning
  •   Data Analysis
  •   Unstructured Data
  • There are 4 modules in this course

    This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE

    Classification

    Clustering

    Outlier Analysis

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