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Statistics Essential for Data Science

To become a successful Data Scientist, you need to have a good foundation in Math and Statistics, as these are the building blocks of Machine Learning algorithms. Key concepts such as descriptive statistics, probability theory, statistical significance, help data scientists draw better business decisions from data analysis. Key Learning Objectives...
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$799.00

Deviare

OSL

3756

To become a successful Data Scientist, you need to have a good foundation in Math and Statistics, as these are the building blocks of Machine Learning algorithms. Key concepts such as descriptive statistics, probability theory, statistical significance, help data scientists draw better business decisions from data analysis.


Key Learning Objectives

  • Understand the fundamentals of statistics
  • Work with different types of data
  • How to plot different types of data
  • Calculate the measures of central tendency, asymmetry, and variability
  • Calculate correlation and covariance
  • Distinguish and work with different types of distribution
  • Estimate confidence intervals
  • Perform hypothesis testing
  • Make data-driven decisions
  • Understand the mechanics of regression analysis
  • Carry out regression analysis
  • Use and understand dummy variables
  • Understand the concepts needed for data science even with Python and R!

Pre-requisites

There are no prerequisites for taking this  Statistics Essentials for Data Science certification course.


Modules

  • Introduction
  • Sample or population data
  • The fundamentals of descriptive statistics
  • Measures of central tendency, asymmetry, and variability
  • Practical example descriptive statistics
  • Distributions
  • Estimators and Estimates
  • Confidence intervals advanced topics
  • Practical example inferential statistics
  • Hypothesis testing Introduction
  • Hypothesis testing Let's start testing
  • Practical example hypothesis testing
  • The fundamentals of regression analysis
  • Subtleties of regression analysis
  • Assumptions for linear regression analysis
  • Dealing with categorical data
  • Practical example regression analysis

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