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Statistics for Data Science using Python

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Statistics for Data Science using Python

Syllabus – What you will learn from this course

Intro to Stats

  • Why to learn Statistics
  • Intro to Python for Statistics
  • What is Statistics
  • Use of Statistics in Data Science
  • What is Python & need of Python in Data Science
  • How Python works
  • Installational of Anaconda Navigator

Statistics module – 1

  • Random Variable, Population and Sample Statistics
  • Types of Statistics
  • What are Outliers & Measures of Central Tendancy(Mean,Median,Mode) ?
  • Mean,Mode Median implementation using Python
  • Measures of Spread (Variance,Standard Dev. ,Range,Inter-Quantile Range)
  • Outliers Detection and Removal using Python
  • Skewness in Data

Statistics Module 2

  • Frequency Tables & Histogram
  • Frequency Tables & Histogram in Python
  • Types of AnalysisTypes of Analysis in Python
  • Covariance and Co-relation
  • Co-relation using Python

Statistics Module 3

  • Intro to Probability
  • What is Prob. Density Function(PDF) & Cumalative Demnsity Function(CDF) ?
  • Bayes Theorem
  • What is a Distribution and why we use it?
  • Bionomial Distribution
  • Binomial Dist. in Python
  • Poisson’s Distribution
  • Poisson Distribution in Python
  • Normal Distribution
  • Normal Distribution in Python
  • Implementation of Z-score(Standarization) in python
  • Log-Normal Distribution and Heavy-Tailed Distribution
  • Log-Normal and Heavy Tailed Dist.. in Python
  • Q-Q Plot
  • Central Limit Theorem
  • Central Limit Theorem implemntation in Python
  • Chebyshew’s Inequality
  • Estimation Problem based on Z-stats

Statistics Module 4

  • Hypothesis testing
  • 1-tailed and 2-Tailed Test
  • Critical_Region
  • Hypothesis Table
  • Level of Significance
  • P-value
  • T-test and various types of T-test
  • T-test in Python
  • Chi-Square Test
  • Anova Test

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