Business Analytics Online/Offline/Corporate Training

Business Analytics

(With SAS, R, WEKA, SPSS & Excel)*



Module 1: Data Visualization and Summarization

Part-1: Descriptive Statistics:

. Introduction to Advanced Data Analytics
. Statistical descriptive and inferences for various Business problems
. Types of Variables
. Measures of central tendency
. Dispersion
. Variable Distributions
. Probability Distributions
. Normal Distribution and Properties
. Skewness and Kurtosis
. Five number Summary Analysis

Part-2: Data quality and outlier treatment

. Outlier treatment with robust measurements
. Outlier treatment with central tendency Mean
. Outlier with Min Max methods
. Imputation 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 T-TEST
. Paired T-TEST
. Independent Sample T-TEST
. Analysis of Variance ( ANOVA),
. MANOVA
. Chi Square Test (Non Parametric Tests)
. Kruskal-Wallis,
. Mann-Whitney,
. Wilcoxon, McNemar test

Module 2: Data Preparation and Quality Check

Part-4: Data Validation and Data Imputation

. Proc Univariate techniques & analysis (SAS)
. Q-Q probability plots
. Cumulative frequency ( P P plots)
. Explorer analysis ( SPSS)
. Steam and leaf analysis
. Kolmogorov Smirnov test
. Shapiro Wilks test
. Anderson darling test






Part-5: Data Transformation

. Log transformation
. Arcsine transformation
. Box- Cox transformation
. Square root transformation
. Inverse transformation

Module 3: Predictive Analytics (Supervised Learning)

Part-6: Predictive modeling & Diagnostics

. Correlation - Pearson, Kendall, Wilcox
. SLR Regression
. MLR Regression
. Residual analysis
. Auto Correlation
. VIF Analysis
. CP Indexing
. Eigen Value for PCA Analysis
. Homoscedasticity
. Heteroskedasticity
. Stepwise regression
. Forward Regression
. Backward Regression
. Quadraint Regression
. Transformed Regression
. Dummy Variables Regression




Part-7 Logistic Regression Analysis

. Logistic Regression
. Discriminant Regression Analysis
. Multiple Discriminant Analysis
. Stepwise Discriminant Analysis
. Binary Regression Analysis
. Profit and Logit Models
. Estimation of probability using logistic regression,
. Wald Test statistics for Model
. Hosmer Lemshow
. Nagurkake R square
. Maximum likelihood estimation
. AIC
. BIC (Bayesian information criterion)

Module 4: Advanced Analytics (Unsupervised Learning)

Part-8: Dimension Reduction Analysis

. Introduction to Factor Analysis
. Principle component analysis
. Reliability Test
. KMO MSA tests, Eigen Value Interpretation,
. Rotation and Extraction steps
. Varmix Models
. Conformity Factor Analysis
. Exploitary Factor Analysis
. Factor Score for Regression




Part-9: Cluster Analysis

. Introduction to Cluster Techniques
. Hierarchical clustering
. K Means clustering
. Wards Methods
. Variation Methods
. Linkage Methods
. Centroid Methods

Module 5: Data Mining and Machine Learning

Part -10: Data Mining Machine Learning

. Data partition (Training, Validating, Testing)
. Data Explore Analysis
. Data Testing Analysis
. Data Transform Analysis
. Linear Model
. Non Linear Model
. Random Forest Analysis
. Tree analysis (CHAID )
. J48 proned & unproned
. SVM(Supporting Vector Machine)
. ANN (Artificial Neural Network)










. Model Evaluation Testing
. Error/ Confusion matrices
. ROC
. MAPE
. Lift Curve
. Sensitivity
. Misclassification Rating

























Note: * Business Analytics course can be implemented in the tool subject to the packages chosen