DATASCIENCE WITH S-A-S & PYTHON

introduction To Analytics

• Analytics World
  • Introduction to Analytics
  • Concept of ETL
  • S-A-S in advanced analytics

• Getting Started
• Software installation
• Introduction to GUI
• Different components of the language
• All programming windows
• Concept of Libraries and Creating Libraries
• Variable Attributes – (Name, Type, Length, Format, In format, Label)
• Importing Data and Entering data manually

• Descriptor Portion of a Dataset (Proc Contents)
• Data Portion of a Dataset
• Variable Names and Values
• Data Libraries

Understanding Data Step Processing

• Data Step and Proc Step
• Data step execution
• Compilation and execution phase
• Input buffer and concept of PDV

• Column Input and List Input and Formatted methods
• Delimiters, Reading missing and non standard values
• Reading one to many and many to one records
• Reading Hierarchical files
• Creating raw data files and put statement
• Formats / Informat

• Proc Import / Delimited text files
• Proc Export / Exporting Data
• Datalines / Cards

• Reading Multiple Records per Observation
• Reading “Mixed Record Types”
• Sub-setting from a Raw Data File
• Multiple Observations per Record
• Reading Hierarchical Files

• Concept of SAS library and SAS Catalog
• Variable Types in SAS
• Reading Data stored external to SAS
• Importing Data by using Proc Import
• Data Step SAS statements
• SAS Functions
• Appending and Merging using SAS
• SAS Procedures like proc means, proc Univariate, proc append, proc freq and proc export.
• SAS SQL
• SAS Macros

• One Sample t-test of comparing means
• Two Sample t-test of comparing means
• One Way ANOVA
• Assumptions of ANOVA Modeling
• n-way ANOVA
• ANOVA Post Hoc Studies

• Apply the principles of honest assessment to model performance measurement
• Assess classifier performance using the confusion matrix
• Model selection and validation using training and validation data
• Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
• Establish effective decision cut-off values for scoring

 Understanding and Exploration Data

• • Introduction to basic Procedures – Proc Contents, Proc Print
• Understanding and Exploration Data
  • Operators and Operands
  • Conditional Statements (Where, If, If then Else, If then Do and select when)
  • Difference between WHERE and IF statements and limitation of WHERE statements
  • Labels, Commenting
  • System Options (OBS, FSTOBS, NOOBS etc…)

• Proc Sort – with options / De-Duping
• Accumulator variable and By-Group processing
• Explicit Output Statements
• Nesting Do loops
• Do While and Do Until Statement
• Array elements and Range

• Concatenation
• Interleaving
• Proc Append
• One To One Merging
• Match Merging
• IN = Controlling merge and Indicator

Introduction to Databases

• Introduction to Proc SQL
• Basics of General SQL language
• Creating table and Inserting Values
• Retrieve & Summarize data
• Group, Sort & Filter
• Using Joins (Full, Inner, Left, Right and Outer)
• Reporting and summary analysis
• Concept of Indexes and creating Indexes (simple and composite)
• Connecting S-A-S to external Databases
• Implicit and Explicit pass through methods

• Macro Parameters and Variables
• Different types of Macro Creation
• Defining and calling a macro
• Using call Symput and Symget
• Macros options (mprint symbolgen mlogic merror serror)

• Basic Statistics – Measures of Central Tendencies and Variance
• Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
• Inferential Statistics -Sampling – Concept of Hypothesis Testing
• Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square    
• Levels of Measurement and Variable types
• Descriptive Statistics and Picturing Distributions
• Confidence Interval for the Mean

• Introduction to Predictive Modeling
• Types of Business problems – Mapping of Techniques
• Different Phases of Predictive Modeling

• Need of Data preparation
• Data Audit Report and Its importance
• Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
• Variable Reduction Techniques – Factor & PCA Analysis

• Introduction to Segmentation
• Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Behavioural Segmentation Techniques (K-Means Cluster Analysis)
• Cluster evaluation and profiling
• Interpretation of results – Implementation on new data

• Introduction – Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results – Business Validation – Implementation on new data

 Introduction – Applications

• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve,
  Probability Cut-offs, Lift charts, Model equation, Drivers, etc)
• Interpretation of Results – Business Validation -Implementation on new data

• Introduction – Applications
• Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques(Pattern based – Pattern less)
• Basic Techniques – Averages, Smoothening, etc
• Advanced Techniques – AR Models, ARIMA, etc
• Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

• Statistical learning vs. Machine learning
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)

• Recursive Partitioning(Decision Trees)
• Ensemble Models(Random Forest, Bagging & Boosting)
• K-Nearest neighbours