Course details

Data Science Statistics: Common Approaches to Sampling Data

Data Science Statistics: Common Approaches to Sampling Data


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

On the career path to Data Science, a fundamental understanding and the application of statistics, specifically modeling is required. The goal of all modeling is generalizing as well as possible from a sample to the population as a whole. In this Skillsoft Aspire course, you will explore the first step in this process, obtaining a representative sample from which meaningful generalizable insights can be obtained.



Expected Duration (hours)
0.8

Lesson Objectives

Data Science Statistics: Common Approaches to Sampling Data

  • Course Overview
  • describe important terms associated with the sampling process
  • define sampling bias and describe problems caused by this phenomenon
  • define simple random sampling and enumerate its properties
  • define systematic random sampling and differentiate it from simple random sampling
  • define stratified random sampling and differentiate it from simple and systematic random sampling
  • define non-probability sampling and enumerate some non-probability sampling techniques
  • define the two properties of probability sampling, enumerate three types of probability sampling, and list two types of non-probability sampling
  • Course Number:
    it_dssds1dj_02_enus

    Expertise Level
    Beginner