Data Reduction & Exploratory Data Analysis (EDA)

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