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