Data Reduction & Exploratory Data Analysis (EDA)


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


Overview/Description
With predictive analytics, relevant data should be stored for easy retrieval, kept up-to-date, and attributes must be selected contingent on their predictive potential. Explore data reduction and graphic tools for exploratory data analysis.

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)
0.8

Lesson Objectives

Data Reduction & Exploratory Data Analysis (EDA)

  • start the course
  • recognize key data reduction methodologies
  • use principal component analysis for feature selection
  • use the information theory approach for feature selection
  • recognize the key features of using Chi-square
  • recognize key features of the wrapper data reduction method
  • recognize the key features of factor analysis
  • recognize key features of EDA and how quantitative techniques are used to perform EDA
  • use bar charts and box-and-whisker plots to perform EDA
  • use run charts and scatter plots to perform EDA
  • use histograms and stem-and-leaf plots to perform EDA
  • recognize the direction, form, and strength of a scatter plot

    • Course Number:
    df_prma_a08_it_enus

    Expertise Level
    Intermediate