SAS is one of the most powerful statistical software used in the world of Data Science. It not only helps in Machine Learning algorithms, but also it has very robust 450 plus inbuilt functions that makes it very efficient in data management and data cleaning. SAS is a language which is easiest to learn among all the software being used for analytics. This course will be helpful for novice to professionals, who would be learning not only the power functionalities of SAS coding, but also the Machine Learning algorithms to add value to clients of different domain.
Introduction to SAS
5 main windows of SAS
Importing data into SAS
Data step Vs Proc Step
Conditional processing: If, else if, and else statement
Boolean in if else statement
Where statement
All types of merging using data step
Proc Print
Proc means and all the options
Proc univariate
Proc Freq
Proc sort
Removal of duplicates
Difference between functions and Proc
Inbuilt Numeric functions of SAS
Inbuilt Character functions of SAS
Inbuilt Date functions of SAS
SQL queries
Merging with SQL
Macros in SAS
Output Delivery System in SAS
Everything you want to know about statistics….Well sort of!!
Mean, Median, Mode
Standard Deviation, Variance,
Normal Distribution
Hypothesis testing
T-test, Anova, Normality test
Predictive Analytics – Linear Regression
Concepts of Linear Regression
Simple and Multiple Linear Regression
Automatic Dummy Variables creation technique
Model Validation parameters
Model Assumption testing
Splitting of data for Validation and testing
Business Case Study with real data to model in SAS software
Participants will be asked to develop a Linear Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.
Predictive Analytics – Logistic Regression
Concepts of Logistic Regression
Difference between Linear Regression and Logistic Regression
Automatic Dummy Variables creation technique
Model Validation parameters
Model Assumption testing
Splitting of data for Validation and testing
Business Case Study with real data to model in SAS software
Participants will be asked to develop a Logistic Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client.
Time series forecasting: ARIMA
Difference between forecasting and prediction
Concepts of time series data
Concepts of ARIMA
Descriptive analytics for ARIMA
Development of model
Best model selection
Forecasting with the best model
Business Case Study with real data to model in SAS software