Please submit a minimum 1 page (single spaced) . Based

 Please submit a minimum 1 page (single spaced) .  Based on your Module topics, what did you find new and interesting?  And what appeared to be a review?  Also, identify at least one discussion post you found interesting, helpful, or beneficial (and why). 

Overview & Learning Objectives

Topics covered in this Module

  • Cluster analysis
  • K-means clustering
  • Association rules
  • Text Mining
  • Word clouds
  • Statistical inference
  • Selecting a sample
  • Point estimation
  • Sampling distributions
  • Interval estimation
  • Hypothesis tests
  • Big data
  • Sampling error

Learning Objectives

 By the end of this module, students should be able to:

  • Explain concepts:
    • cluster analysis
    • centroids linkage
    • k-means clustering
  • Describe association rules and market basket applications
  • Explain word clouds
  • Discuss text mining and unstructured data
  • Apply k-means clustering
  • Compute cluster centers
  • Calculate confidence intervals using Excel
  • Calculate descriptive statistics using the Data Analysis Toolpak in Excel
  • Conduct one-tailed and two-tailed hypothesis tests in Excel
  • Calculate p-values and determine whether the null hypothesis should be rejected

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