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Statistics is a fundamental and integral part of data science, serving as a powerful toolkit for understanding, analyzing, and interpreting data. It provides the necessary tools to extract meaningful insights from raw data, make informed decisions, and build predictive models. In the realm of data science, statistics plays a crucial role in various stages of the data lifecycle, from data collection and exploration to hypothesis testing and model evaluation.

What you will learn?

  • Understand what a Normal Distribution is.
  • Explain the difference between continuous and discrete variables
  • Understand the Central Limit Theorem
  • Use the Z-Score and Z-Tables
  • Understand the difference between a normal distribution and a t-distribution
  • Create confidence intervals
  • Understand standard deviations
  • Understand what a sampling distribution is
  • Apply Hypothesis Testing for Proportions
  • Use the t-Score and t-Tables

Course Curriculum

  • Statistics
  • Inferential Statistics
  • Descriptive Statistics
  • Mean, Median and Mode
  • Population vs Sample
  • Gaussian or Normal Distribution
  • Log Normal Distribution
  • Covariance
  • Central Limit Theorem
  • Chebyshev’s inequality
  • Pearson Correlation Coefficient
  • Spearman’s Rank Correlation Coefficient
  • Standardization vs Normalization
  • Use of Python in Statistics
  • Data as a table
  • Pandas Data Frame
  • Student’s T-test
  • Paired test
  • Python formulas for specifying statistical models
  • Multiple Regression
  • Analysis of variance(ANOVA)
  • PairPlot: scatter matrices
  • ImPlot: plotting a univariate regression