SAS Training Basic to advanced

SAS, which stands for Statistical Analysis System, is a powerful software suite used for advanced analytics, business intelligence, data management, and predictive modeling. Developed by SAS Institute, SAS software provides a wide range of tools and capabilities for analyzing large datasets, extracting insights, and making data-driven decisions.

What is SAS?

SAS is a comprehensive software solution designed to handle all aspects of data analysis and management. It offers a user-friendly interface, extensive documentation, and a vast array of statistical functions and procedures. Originally developed in the late 1960s at North Carolina State University, SAS has evolved into one of the most widely used statistical software packages in the world.

Key Features of SAS

1. Data Management: SAS provides robust tools for importing, cleaning, transforming, and managing large datasets. It supports various data formats, including text files, Excel spreadsheets, CSV files, and relational databases.

2. Statistical Analysis: SAS offers a wide range of statistical procedures for descriptive statistics, hypothesis testing, regression analysis, multivariate analysis, time series analysis, and more. These procedures enable users to analyze data and uncover patterns, trends, and relationships.

3. Data Visualization: SAS enables users to create high-quality graphs, charts, and visualizations to communicate insights effectively. It supports a variety of graphical techniques, including histograms, scatter plots, bar charts, and heatmaps.

4. Predictive Modeling: SAS provides advanced predictive modeling capabilities for building and evaluating statistical models. Users can develop models for forecasting, classification, clustering, and optimization, using techniques such as logistic regression, decision trees, neural networks, and support vector machines.

5. Machine Learning: SAS offers a comprehensive suite of machine learning algorithms for automated model building and predictive analytics. It includes algorithms for supervised learning, unsupervised learning, reinforcement learning, and deep learning.

6. Text Analytics: SAS provides tools for analyzing unstructured text data, such as documents, emails, social media posts, and customer feedback. Users can perform tasks such as sentiment analysis, topic modeling, text categorization, and entity extraction.

7. Business Intelligence: SAS offers business intelligence (BI) solutions for reporting, dashboarding, and data visualization. It enables organizations to create interactive dashboards, scorecards, and reports to monitor key performance indicators (KPIs) and track business metrics.

Benefits of Using SAS

• Comprehensive Solution: SAS provides a comprehensive suite of tools and capabilities for all stages of the analytics lifecycle, from data preparation to model deployment.

• Scalability: SAS can handle large volumes of data and complex analytical tasks, making it suitable for enterprise-level applications.

• Reliability: SAS is known for its reliability, accuracy, and robustness. It has been extensively tested and validated for use in critical business and research applications.

• Flexibility: SAS is highly flexible and customizable, allowing users to tailor their analyses and workflows to meet specific requirements.

• Community Support: SAS has a large and active user community, with extensive online resources, forums, and user groups available for support and collaboration.

SAS Training: Basic to Advanced

Day 1

Session-1

• Introduction to SAS PC and SAS EG
• Writing your first SAS program using Cards and Datalines
• Understanding SAS terminology compared to SQL
• Guidelines for naming datasets, variables, and values
• Exploring SAS libraries and their creation criteria
• Viewing Descriptor and Data portions of datasets and libraries
• Overview of SAS programming steps: Data Step and Proc Step
• Exercise: Practice session

Session-2

• Importing and exporting data from CSV, Excel, and Access databases
• Reading data from text files to create datasets
• Exporting SAS datasets to text files using the FILE statement
• Connecting SAS to different database servers
• Creating datasets from existing datasets
• Exercise: Hands-on exercises

Session-3

• Introduction to list reports
• Sorting datasets using the PROC SORT procedure
• Enhancing output using Output Delivery System (ODS)
• Filtering data using the WHERE statement
• Conditional data processing using the IF-THEN statement
• Comparison between user-defined and system-defined formats
• Exercise: Practical application of concepts

Days 2 and 3

Session-4

• SAS data joining techniques: Appending and Merging datasets
• Comparison between SAS Merge and SAS SQL Join
• Creating multiple datasets from a single dataset
• Updating datasets using SAS Update statement
• Comparing datasets using SAS Compare statement
• Exercise: Hands-on exercises

Session-5

• Transposing datasets from rows to columns and vice versa
• Retaining values across iterations using the RETAIN statement
• Understanding the difference between SUM and addition operations
• Utilizing FIRST. and LAST. variables
• Overview of SAS functions for character, numeric, and datetime data
• Exercise: Practical application of concepts

Session-6

• Exploring SAS loops and arrays for iterative data processing
• Generating summary reports using PROC MEANS, PROC FREQ, PROC REPORT, PROC TABULATE, PROC UNIVARIATE, PROC SURVEY, etc.
• Creating visual representations of data using SAS Graphs
• Exercise: Hands-on exercises

Day 4

Session-7

• Introduction to SAS SQL for data retrieval and manipulation
• Retrieving data using SELECT, WHERE, GROUP BY, and ORDER BY clauses
• Filtering aggregated data using the HAVING clause
• Exploring SAS SQL options and functions
• Creating new tables, altering tables, and creating views using SAS SQL
• Exercise: Practical application of SAS SQL queries

Day 5

Session-8

• Introduction to the SAS Macro Facility for automating repetitive tasks
• Creating and using macros with parameters
• Understanding macro statements, options, and functions
• Generating macro variables using various methods
• Debugging macros and handling errors
• Conditional macro statements using %IF-%THEN and %DO-%END
• Exercise: Hands-on exercises