AWS Cloud Computing

AWS Cloud Computing


Module 1: Basic

  • R Installation
  • R Studio
  • Understanding Data Structures in R – Lists
  • Matrices, Vectors
  • R Studio the IDE
  • Basic Building Blocks in R
  • Understanding Vectors in R
  • Basic Operations Operators and Types
  • Handling Missing Values in R
  • Matrices and Data Frames in R Logical Statements in R

Module 2: Data Visualization

  • Grammar of Graphics
  • Bar Charts
  • Histograms
  • Pie Charts
  • Scatter Plots
  • Line Plots and Regression
  • Word Clouds
  • Box Plots
  • GGPLOT2

Module 3: Statistical Learning and ANOVA

  • Measures of Central Tendency in Data
  • Measures of Dispersion
  • Understanding Skewness in Data
  • Probability Theory
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing
  • One-Way Analysis of Variance
  • Assumption of ANOVA
  • Statistics Associated with One-Way Analysis of Variance
  • Interpreting the ANOVA Results
  • TwoWay Analysis of Variance
  • Interpreting the ANOVA Results
  • Analysis of Covariance

Module 4: Regression

  • What is Regression Analysis
  • Limitations of Regression
  • Covariance and Correlation
  • Multivariate Analysis
  • Assumptions of Linearity Hypothesis Testing
  • Limitations of Regression
  • Implementing Simple & Multiple Linear Regression
  • Making Sense of Result Parameters
  • Model Validation
  • Handling Other Issues/Assumptions in Linear Regression
  • Handling Outliers
  • Categorical Variables
  • Autocorrelation
  • Multicollinearity
  • Heteroskedasticity Prediction and Confidence Intervals

Module 5: Regression

  • Implementing Logistic Regression
  • Making Sense of Result Parameters: Wald Test
  • Likelihood Ratio Test Statistic
  • Chi-Square Test Goodness of Fit Measures
  • Model Validation: Cross Validation
  • ROC Curve
  • Confusion Matrix

Module 6: Decision Trees and Random Forest

  • Introduction to Predictive Modelling with Decision Trees
  • Entropy & Information Gain
  • Standard Deviation Reduction (SDR)
  • Overfitting Problem
  • Cross Validation for Overfitting Problem
  • Running as a Solution for Overfitting

Real-time case studies as per market standards.