Clinical SAS:

• Basics of Clinical Research
• ICH GCP Guidelines
• Good programming Practices
• Use of SAS in Clinical Research
• Demo project for Clinical trial study

CDISC SDTM Overview:

• Introduction to CDISC Data Models
• Understanding CDISC - SDTM
• Understanding CDISC – ADaM
• Introduction to Protocol, SAP & CRF
• Study Populations, Study Day Calculation
• SAS Programming Guidelines
• Creating listings
• Creating Baseline Characteristic Table - 1
• Creating Baseline Characteristic Table - 2
• Creating Safety Table-1
• Creating Safety Table-2
• Creating Safety (Shift) Table-3

SAS Finance

We are providing Project exposure on below mentioned topics with respect to Customer Insight.

• Marketing and campaign management.
• Performance measurement and management.
• Scorecard building.
• Model Building concepts.

SAS BI:

Prepare Data

• Your data may be in a relational database (Oracle, Teradata, MySQL, etc.).  The data is then placed in OLAP cubes, information maps, or in some cases a SAS dataset so it can be viewed in the BI toolset.  Some users create their extract transform load (ETL) process in SAS Data Integration (DI) Studio, which is another SAS product.
• SAS OLAP Cube Studio
  • Build OLAP cubes, which are based on MDX code.  OLAP cubes allow summarization of huge datasets so users can quickly view and drill-down to desired information. This post contains an overview of building a cube in SAS OLAP Studio.
• SAS Information Map Studio
  • Build information maps from RDBMS (Oracle, Teradata, and DB2), SAS datasets and OLAP cubes. Information maps allow you to join data, rename variables, and set the data into business friendly view.

Display and Use Data:

Once your data is prepared it can be accessed in various ways.

• SAS Web Report Studio
  • Create reports from OLAP Cubes, Info Maps, or SAS datasets.  Simple, easy to use interface that allows end-users to quickly review reports and drill-down to other reports.
• SAS Add-In for Microsoft Office
  • In MS Excel – Create reports from OLAP Cubes, information maps, or SAS datasets.  Mix SAS data with the data from Excel spread sheets.
  • In MS Office/PowerPoint – create reports from cubes, information maps, or datasets.  Not as much flexibility as with MS Excel, but you can easily create report that you can share with other applications.
  • Possible to create data in MS Excel that you can save to the server and then use in other BI Tools.  This would be an option for smaller datasets that are updated infrequently and not centrally stored, for instance, an department organization structure
• SAS BI Dashboard
  • Use the summarized data to create dials, maps, and even moving indicators.
• SAS Information Delivery Portal
  • Combine all your Web Report Studio reporting, stored processes, and publication channels, and even external web sites in one location.  The portal allows a jumping off point.

Helpers:

• SAS Enterprise Guide
  • Connect to SAS datasets, RDBMS, and even Excel spread sheets. Create stored processes to use and display data.
  • Create reports and more from OLAP Cubes, Info Maps, or SAS datasets.
  • Also create reports that can be shared as a stored process for the other SAS tools.
• SAS Prompt Framework
  • Used with stored processes and with SAS Enterprise Guide.  Prompts allow you to prompt or ask questions of the users and return data based on the choices
• SAS Stored Processes
  • Write SAS programs that can move data around, create reports, or just ensure prompts are available in information maps. 

Administration:

• SAS Management Console
  • Allows you to administer the system and control data access.

SAS Advanced Analytics

Predictive Modelling:

I. Descriptive Statistics

• Introduction to Statistics
  • Measure of central Tendency
  • Measure of Dispersion
  • Measure of Shape

• Data Preparation
  • Data Handling and preparation
  • Missing value analysis and imputation
  • Outlier identification and how handle the outlier problem
• Discrete Distributions
• Binomial, Poisson, Negative Binomial, Geometric and Hyper-Geometric Distributions.
• Continuous Distributions
• Normal, Uniform, Gamma, Beta of I and II kinds, Exponential, Cauchy Distributions.
• Sampling Methods
  • Simple Random Sampling
  • Systematic Random sampling
  • Stratified Random sampling
  • Cluster Random Sampling
  • Non Random samplings: Quota, Judgment, Convince and Snow ball sampling

II. Statistical Inference

• Parametric tests
  • One sample t test
  • Independent of two sample tests ( t test & Z tests)
  • Paired t test
  • One Way ANOVA
  • Two ways ANOVA
• Non parametric Tests
  • Chi square Test   

III. Predictive Modeling technique

• Simple Linear Regression (SLR)
  • OLS method
  • MLE
  • Assumption of OLS
  • Checking Assumption of SLR
  • Problem of Homoscatasity
  • Problem of Autocorrelation
  • Problem of Multicolinarity
  •  Data Transformation   

IV. Multiple Linear regressions

V. Logistic Regression for Classification and Prediction

VI. Forecasting Technique (Time series Analysis)

•  Trend analysis
  • Smoothening technique (Moving Average and Exponential smoothing
  • Auto regression              
• ARIMA Modeling
  • Exponential Smoothing

VII. Multivariate Techniques

• Factor Analysis For Data Reduction
• Principle Component Analysis
• Cluster Analysis for Market segmentation
• Discriminate Analysis for classification and Prediction
• Conjoint analysis for Product design
• Canonical correlation