SAS is used for a variety of purposes including data analysis, statistical modeling and business intelligence.
First we import the data into SAS and then perform data cleaning, followed by analyzing the data, performing statistical tests, creating models or generating reports. We generate insights and make informed, data-driven decisions based on the analysis conducted using SAS.
Following is a list of areas wherein we use the SAS software.
Multivariate Analysis
Multivariate Analysis means analyzing more than one variable for each unit or individual in a dataset. Instead of just looking at one variable in isolation, it considers relationships between multiple variables simultaneously.
It includes techniques like multiple regression analysis, Multivariate Analysis of Variance (MANOVA), Principal Component Analysis (PCA), Factor Analysis, Cluster Analysis etc. SAS has a built-in wide range of procedures specifically designed for different types of multivariate analyses and return the results in a readable format including tables and graphs.
Business Intelligence
SAS helps to integrate data from various sources including databases, MS Excel, CSV files and other data sources. It has various functions for data cleansing, transformation and loading processes. It helps automate daily tasks and generating reports by extracting and analyzing data from various sources using SAS Macros.
Time Series Forecasting
SAS has a module called SAS/ETS which include various procedures for time series techniques such as Exponential Smoothing, ARIMA, Seasonal Decomposition etc. It helps in forecasting sales, estimating customer growth etc.
Data Visualization
By using SAS, you can create visually appealing graphs. Some commonly used procedures for data visualization include PROC SGPLOT, PROC SGPANEL, PROC SGSCATTER. It allows you to export graphs and charts in various formats such as PDF, PNG and SVG.
Machine Learning
SAS supports various machine learning algorithms including random forest, decision tree, gradient boosting etc. It also includes various procedures for model validation and scoring new data.
Clinical Trials and Research
In the field of biostatistics, SAS is used for analyzing clinical trial data, monitoring adverse events and drug safety and identifying potential medication risks. It is also used for survival analysis which examines the time it takes for an event of interest to occur such as time to death.
Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.
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