Stats and Probablility
• Descriptive statistics, random variables, and probability distribution functions
• Data distributions like uniform, binomial, exponential, poisson, etc
• Probability concepts, set theory and hypothesis testing
• Central limit theorem, t-test, chi-square, z-test
• Central limit theorem
• Anova
R Programming
• Basics of R
• Conditional and loops
• Rpackages/libraries
• Data mining gui in R
• Data structures in R
• Exceptions/debugging in R
Machine learning models in Python and R
• Linear regression model in R
• Multiple linear regressions model
• Representation of regression results
• Non-linear regression models
• Tree-based regression models
• Decision tree-based models
• Rule-based systems
Machine learning mining algorithms using Python and R
• Association analysis• Market-based analysis / rules
• Apriori algorithm
• Ensemble models - random forest model, boosting model• Segmentation analysis- types of segmentation, k-means clustering, bayesian clustering
• Feature selection/ dimension reduction, factor or component analysis.
• Axes
• Covariance