Course details

Data Mining, Data Distributions, & Hypothesis Testing

Data Mining, Data Distributions, & Hypothesis Testing


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Purposeful information can be extracted from large data sets to determine what has, could, or should happen. Explore descriptive, predictive, and prescriptive analytics, including data mining, distribution models, and hypothesis testing.

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives

Data Mining, Data Distributions, & Hypothesis Testing

  • start the course
  • recognize key features of descriptive analytics
  • recognize key features of prescriptive analytics
  • recognize key features of data mining
  • identify important data mining concepts and techniques
  • identify data mining methods used for predictive analysis
  • identify features of a standard normal distribution
  • list features of the Binomial and Poisson distributions
  • recognize key features of hypothesis testing and its application
  • recognize key features of one and two-tailed hypothesis tests
  • match analytics problems to appropriate data mining methods
  • Course Number:
    df_prma_a06_it_enus

    Expertise Level
    Intermediate