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Reachout Analytics

Setup Menus in Admin Panel

Business Analytics R

Course Curriculum

Data Visualization and Summarization

MODULE – 1
Part-1 Descriptive Statistics:
  • Introduction to Advanced Data Analytics
  • Statistical inferences Types of Variables
  • Measures of central tendency
  • Dispersion
  • Variable Distributions
  • Probability
  • Distributions
  • Normal Distribution and Properties
Part-2: Data quality outlier
  • Robust measurements
  • Outlier treatment with central tendency
  • Replacing with series means or median values
  • Z score Calculation
  • Data Normalization
  • Sampling and estimation

Part-3: Test of Hypothesis
  • Null/Alternative Hypothesis formulation
  • Type I and Type II errors
  • One Sample TTEST
  • Paired TTEST
  • Independent Sample TTEST
  • ANOVA,
  • MANOVA
  • Chi Square Test
  • Kruskal-Wallis,Mann-Whitney,
  • Wilcoxon,
  • McNemar test

Data Preparation and Quality Check

Module -2
Part-4: Data Validation & Imputation
  • Univariate procedure
  • Q-Q probability plots
  • Cumulative frequency (P P) plots
  • Explorer analysis
  • Steam and leaf analysis
  • Kolmogorov Smirnov test
  • Shapiro Wilks test
Part-5: Data Transformation
  • Log transformation (s)
  • Arcsine transformation
  • Box- Cox transformation
  • Square root transformation
  • Log transformation (s)
  • Inverse transformation
  • Min- Max Normalization

Predictive Analytics

Module – 3
Part-6: Predictive modeling & Diagnostics
  • Correlation – Pearson, Kendall
  • SLR Regression
  • MLR Regression
  • Residual analysis
  • Auto Correlation
  • VIF Analysis
  • Indexing Eigen Value interpretation
  • Homoscedasticity
  • Homogeneity
  • Stepwise regression
  • Transformation of variables
Part-7 Logistic Regression Analysis
  • Discriminant and Logit Analysis
  • Multiple Discriminant Analysis
  • Stepwise Discriminant Analysis Binary
  • Logit Regression
  • Estimation of probability using logistic regression, Wald Test
  • Hosmer Lemshow

Advanced Analysis

Module – 4
Part-8: Factor Analysis
  • Introduction to Factor Analysis – PCA
  • Reliability Test
  • KMO MSA tests, Eigen Value Interpretation
  • Rotation and Extraction
  • Varimix Models
  • Principle component analysis
  • Conformity Factor Analysis
  • Exploitary Factor Analysis
Part-9: Cluster Analysis
  • Introduction to Cluster Techniques
  • Distance Methodologies,
  • Hierarchical and Non-Hierarchical Procedures K Means clustering
  • Wards Method

Part- 10: Conjoint Analysis
  • Statistics and terms Association with Conjoint Analysis
  • Assumption and limitation of conjoint analysis
  • Hybrid Conjoint Analysis
Part –11: Time Series Forecasting
  • Smoothing and annual Time series
  • Time series forecasting for seasonal data
  • Multiplicative Models
  • Additive Models

Data Mining for Business Intelligence

Part -12: Data Mining
  • Data partition (Training, Validating Testing)
  • Data Explore
  • Data Testing
  • Data Transform
  • Linear Model
  • SVM Model
  • Tree Analysis
  • RandomForest Analysis
  • Model Evaluation
  • ROC
  • Lift Curve
  • Sensitivity
  • Error/ Confusion matrices
Part -13: Business Intelligence
  • Data Warehousing for Data Modeling
  • Data Warehousing for Report Building
  • Stars Schemes for Data Marts
  • Multi dimensional summarization (OLAP)
  • Web analytics (Concepts)

Course Curriculum

Course Reviews

  1. Linda Walker says:

    Good Course
    Nice Course Ciriculum

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