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Data Science Statistics: Inferential Statistics

Data Science Statistics: Inferential Statistics


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

On the career path to Data Science, a fundamental understanding of statistics, specifically inferential statistics is required. Inferential statistics go beyond merely describing a dataset and seek to posit and prove or disprove the existence of relationships within the data. In this Skillsoft Aspire course, you will explore hypothesis testing, which finds wide applications in data science.



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