Course details

Data Science Statistics: Inferential Statistics

Data Science Statistics: Inferential Statistics


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

In this Skillsoft Aspire course on data science, learners can explore hypothesis testing, which finds wide applications in data science. This beginner-level, 10-video course builds upon previous coursework by introducing simple inferential statistics, called the backbone of data science, because they seek to posit and prove or disprove relationships within data. You will start by learning steps in simple hypothesis testing: the null and alternative hypotheses, s-statistic, and p-value, as ach term is introduced and explained. Next, listen to an informative discussion of a specific family of hypothesis tests, the t-test. Then learn to describe their applications, and become familiar with how to use cases including linear regression. Learn about Gaussian distribution and the related concepts of correlation, which measures relationships between any two variables, and autocorrelation, a special form used in the concept of time-series analysis. In the closing exercise, review your knowledge by differentiating between the null and the alternative hypotheses in a hypothesis testing procedure, then enumerating four distinct uses for different types of t-tests.



Expected Duration (hours)
1.0

Lesson Objectives

Data Science Statistics: Inferential Statistics

  • Course Overview
  • draw the shape of a Gaussian distribution and enumerate its defining properties
  • enumerate the steps involved in hypothesis testing and define the null and alternative hypotheses
  • describe the role of test statistic and p-value in accepting or rejecting a null hypothesis
  • enumerate types and uses of t-tests in hypothesis testing
  • outline the significance of skewness and kurtosis and define the skewness and kurtosis of a normally distributed random variable
  • calculate the autocorrelation of a time series
  • define linear regression
  • interpret the R-squared of a regression and identify overfitting
  • differentiate between null and alternative hypotheses, enumerate four use cases for t-tests, and calculate the correlation of time series with itself
  • Course Number:
    it_dssds1dj_03_enus

    Expertise Level
    Beginner