Syllabus

I: Foundations of Business Analytics and R

Introduction to Business Analytics

  1. Understanding Business Analytics
  2. Types of Business Analytics

R Basics

  1. Overview of R and R Studio
  2. Data Structures
  3. Functions
  4. Statements and Looping

Statistical Test Selection

  1. Choose your Test for Data Analysis

II: Descriptive and Diagnostic Analytics

Descriptive Analytics

  1. Introduction to Descriptive Analytics
  2. Measures of Central Tendency
  3. Measures of Dispersion
  4. Measures of Skewness
  5. Measures of Kurtosis

Diagnostic Analytics

  1. Introduction to Diagnostic Analytics
  2. Parametric Vs Non-Parametric Tests

Nominal Tests

  1. Introduction to Nominal Tests
  2. Binomial Test
  3. Mc Nemar’s Test
  4. Cochran’s Q test-post-hoc test
  5. Chi-square test
  6. Phi-Coefficient of Correlation

III: Inferential Statistics: Ordinal and Scale Tests

Scale Tests (Parametric Tests)

  1. Introduction to Parametric Tests
  2. T-tests
  3. T-tests - one Sample, Two Sample, Paired Sample
  4. T-tests - one Sample, Two Sample, Paired Sample
  5. T-tests - one Sample, Two Sample, Paired Sample
  6. ANOVA
  7. One way ANOVA
  8. Two Way ANOVA
  9. Post-hoc tests
  10. Repeated Measures ANOVA
  11. Karl Pearson’s Coefficient of Correlation

Ordinal Tests (Non-parametric Tests)

  1. Introduction to Non-parametric Tests
  2. Wilcoxon Signed Rank Test
  3. Mann-Whitney U Test
  4. Kruskal-Wallis Test
  5. Friedman Tests and related Post-hoc Tests
  6. Spearman Rank Correlation

IV: Data Visualization and Python Essentials

Data Visualization Using R Graphics and R Commander / R Deducer

  1. Introduction to Data visualization
  2. Graphical Presentation - Scatter plot, Histogram
  3. Diagrammatic Presentation - Bar Charts
  4. Pie charts 2D and 3D
  5. Box plots
  6. Line plots

Python Essentials

  1. Understanding Python
  2. Data types
  3. Operators
  4. Numpy
  5. Pandas
  6. Scipy