The popularity of SAS(R) has grown over the years as it encompasses a variety of features to deal with different types of data - structured and unstructured data - from different destinations.SAS(R) is used for different tasks such as predictive modelling;[/b] data mining; multivariate analysis, and forecasting analysis. Owing to all these facilities, it has become one of the preferred customized integrated tools.
Understanding working of SAS(R) [/b]
In order to work on data with SAS(R), the data should be in either SAS format or tabulated Excel format. Data are set into tables in rows and columns. The rows are referred as Observation while the columns are referred as Variables. This procedure enables users to fetch information from database; spreadsheet or other tabular format easily. Additionally for user’s convenience, SAS(R) allows the output to be saved in HTML, RTF, and PDF formats. Owing to all these, SAS(R) is used in banking, pharmaceuticals, education, government and other sectors.
DATA step and PROC step[/b]
SAS programs use DATA step and PROC step deal with data. To be specific, data can be changed or fetched using DATA step and data can be analysed using PROC step. DATA step comprises of two phases; namely, compilation phase and execution phase. During compilation phase, the compiler identifies syntax errors and processes declarative statement. Post this, each executable statement is processed.
Convenience with SAS(R)[/b]
Unlike other business intelligence software where programming is a must to handle large volume of data, SAS(R) offers the facilities to manage and analyse humongous data with the click of few buttons or programming. To elaborate, even non-technical users can work on it by clicking the required features with an embedded facility called of Graphical Point-And-Click User Interface while technical users can program for data analysis, data handling or for some other data related tasks.
SAS(R) Application[/b]
SAS(R) is compatible with both flat and un-formatted files and hence, it is chosen over other software in various data analysis disciplines. Given below are some of the sectors where SAS(R) is commonly utilised:
# Financial analysis
# Publication
# Psychological testing
# Sales forecasting
# Study consumer behaviour
# Financial risk analysis
# Academic research
# Website
# Data analysis
# Business Intelligence
# Banking
# Pharmaceutical analysis
SAS(R) online training - Base SAS(R) training & Advanced SAS(R) training[/b]
For as aspirants who want to understand SAS(R) features thoroughly and master them, Base SAS(R) training will be the ideal choice. Post Base SAS(R) training, the individuals can opt for Advanced SAS(R) training. Base SAS(R) training will focus on topics such as write SAS(R) programs; understand data manipulation techniques; SAS(R) array processing; navigating the SAS(R)windowing environment and many more. Advanced SAS(R) training will render knowledge on various aspects associated with SAS(R) programming[/b] such as Making use of conditional logic in the Query Builder; Macros; Advanced DATA step programming statements; Sub queries; combining large data sets, etc.
Understanding working of SAS(R) [/b]
In order to work on data with SAS(R), the data should be in either SAS format or tabulated Excel format. Data are set into tables in rows and columns. The rows are referred as Observation while the columns are referred as Variables. This procedure enables users to fetch information from database; spreadsheet or other tabular format easily. Additionally for user’s convenience, SAS(R) allows the output to be saved in HTML, RTF, and PDF formats. Owing to all these, SAS(R) is used in banking, pharmaceuticals, education, government and other sectors.
DATA step and PROC step[/b]
SAS programs use DATA step and PROC step deal with data. To be specific, data can be changed or fetched using DATA step and data can be analysed using PROC step. DATA step comprises of two phases; namely, compilation phase and execution phase. During compilation phase, the compiler identifies syntax errors and processes declarative statement. Post this, each executable statement is processed.
Convenience with SAS(R)[/b]
Unlike other business intelligence software where programming is a must to handle large volume of data, SAS(R) offers the facilities to manage and analyse humongous data with the click of few buttons or programming. To elaborate, even non-technical users can work on it by clicking the required features with an embedded facility called of Graphical Point-And-Click User Interface while technical users can program for data analysis, data handling or for some other data related tasks.
SAS(R) Application[/b]
SAS(R) is compatible with both flat and un-formatted files and hence, it is chosen over other software in various data analysis disciplines. Given below are some of the sectors where SAS(R) is commonly utilised:
# Financial analysis
# Publication
# Psychological testing
# Sales forecasting
# Study consumer behaviour
# Financial risk analysis
# Academic research
# Website
# Data analysis
# Business Intelligence
# Banking
# Pharmaceutical analysis
SAS(R) online training - Base SAS(R) training & Advanced SAS(R) training[/b]
For as aspirants who want to understand SAS(R) features thoroughly and master them, Base SAS(R) training will be the ideal choice. Post Base SAS(R) training, the individuals can opt for Advanced SAS(R) training. Base SAS(R) training will focus on topics such as write SAS(R) programs; understand data manipulation techniques; SAS(R) array processing; navigating the SAS(R)windowing environment and many more. Advanced SAS(R) training will render knowledge on various aspects associated with SAS(R) programming[/b] such as Making use of conditional logic in the Query Builder; Macros; Advanced DATA step programming statements; Sub queries; combining large data sets, etc.