Certificate Course in Advance Analytics & Statistical Modeling
Coverage
Basic Concept of Statistics, Introduction of Analytics, Data & Variable Definition, Concept of Supervised and Unsupervised Learning, Descriptive Statistics - Mean, Median, Mode, Standard Deviation and Variance, Coefficient of Variation, Covariance Analysis, Cor- relation Analysis, Concept of Graphs & Charts - Histogram, Boxplot, Scatter Plot, Line Chart, Bar Diagram, Pie Chart, Statistical Distributions - Normal, Binomial, Poisson Distribution, Concept of Hypothesis Testing, Types of Statistical Testing - T-Test, ANOVA, Chi-Square Test, Regression Analysis, Multiple Linear Regression Analysis, Model Accuracy Checking, Logistic Regression, Confusion Matrix, Overall Accuracy, Recall (Sensitivity), Precision, F-Score, Decision Tree, Ensemble Models, Bagging Technique, Random Forest,Gradient Boosting Algorithm (GBM), Unsupervised Techniques - Principal Component Analysis (PCA), How PCA Works in Real Dataset, Cluster Analysis - K-Means Clustering.
Who Can Attend
- Science
- Statistics Aspirants
- Mathematics Graduates
- Commerce
- Faculty members
- data Engineers
- IT Professionals
- Administrators
- Job seekers
