Basic SAS Programming

Meet a dedicated full-time trainer with a background in engineering and computer science, sharing expertise in SAS and Python, EDL tools, and big data technologies like Hadoop and Spark.

Learn how databases serve as centralized repositories that multiple users can access simultaneously. See how tables organize structured data in rows and columns, and distinguish structured from unstructured data.

Examine structured, semi-structured, and unstructured data, using Twitter comments as examples, and learn core database terms such as variables, headers, fields, and observations, plus the EPL process for data warehousing.

Set expectations for the course and introduce SAS concepts. Cover tables, variables, rows and observations, creating data sets, and basic data manipulation using SAS.

SAS, the statistical analysis system, enables data mining, data entry, and SQL database connections; it supports logistic regression, random forest, time-series forecasting, and automated data-processing applications.

Register for SAS on demand by creating a user profile on the fast website, obtain a username and password, and use the administration page to access the cloud-based service.

Learn the SAS environment and SAS Studio basics: create and run programs, view logs and results, and manage data via cloud-based libraries and datasets.

Create a SAS data step to build a tiny table using a numeric variable; learn dataset naming rules, libraries, semicolons, and the run statement to execute the program.

Learn how to create a SAS table with character variables, including customer name and card counts, and to handle character literals using single or double quotes.

Create a SAS table with multiple observations using lines and an input statement, declare a character variable with a dollar sign, and define two variables: name and years of experience.

Learn to create and manage SAS libraries, differentiate temporary and permanent libraries, and store datasets with proper libref naming and physical paths.

Learn how to import a CSP data file into a SAS library via a cloud interface, producing 200 observations and 11 variables, with most variables imported as numeric.

Create a new variable by summing subject marks to obtain total marks, then compute average marks by dividing by five, using set index to copy the table.

Split the data into male and female groups with if or where clauses, and create separate tables with gender flags set to 0 or 1.

Concatenate two SAS tables to create a new data set that combines all records from both sources, illustrating appending male and female student tables into a single data set.

Create a new variable using if-else in SAS to classify math scores into high, medium, and low with thresholds: high if score > 65, medium 50-65, and low below 50.