Curriculam

• Introduction to Data Science
• Analytical Terminology, Analytical Methodology
• Introduction to SAS, R, R-studio interface
• Data Collection, Creating Datasets
• Reading Data From External Files (.Txt, .Xls, .Csv) — Tasks
• Data Exploration: Proc Print, Proc Contents —Tasks
• Data Exploration: ProcGchart, ProcGplot —- Tasks
• Data Exploration: Statistical Terminology
• Data Exploration: Understanding Probability
• Data Exploration : Analyzing Categorical Data (ProcFreq) — Tasks
• Data Exploration : Hypothesis, Types of Errors
• Data Preparation: Arranging the data: Proc Sort, Proc Format-Tasks
• Data Preparation: Keeping, Dropping, Renaming, Transposing
• Data Preparation: Using SAS Functions — Tasks
• Data Preparation: Conditional Processing, By group Processing -Tasks
• Data Preparation: Combining Data sets — Tasks
• Data Preparation: Do-Loops, Arrays
• Statistics: ProcFreq
• Statistics: ProcTtest, ProcAnova
• Proc Npar1way

• Data Mining: Introduction
• Introduction to Regression: ProcCorr, ProcReg
• Dimensionality Reduction Techniques: Proc Factor
• Dimensionality Reduction Techniques: ProcPrincomp
• Dimensionality Reduction Techniques: ProcDiscrim
• Clustering: Introduction
• Clustering case study — Task
• Association Rules — Introduction
• Association Rules — Case study: Task
• Density Estimation: Proc KDE

• ProcReg: Case study — Task
• ProcReg: Model Diagnostics — Task
• Introduction to Logistic Regression
• Proc Logistic -Case study, — Task
• Introduction to Decision Trees
• ProcDtree, Case study — Task
• Introduction to SVM, Naive Bayes, Case study
• Introduction to Neural nets,
• Neural Nets – the Case study
• Introduction to KNN, Case study — Task
• Introduction to Bagging and Boosting
• Ensemble methods Case study
• Reinforcement Learning

• Introduction to Time series
• Proc Arima — Case study — Task
• Introduction to Text Analytics
• Sentiment Analysis in R — Case study
• Introduction to Optimization
• Optimization — Case study