Purpose
SAS (Statistical Analysis System) is a software suite that provides tools for quantitative data management and analysis. It includes a wide range of modules for tasks such as data preparation, statistical analysis, data visualization, and reporting. Some of the key features of SAS include its ability to handle large datasets, perform advanced statistical analysis, and generate high-quality graphics and reports. SAS is widely used in a variety of industries, including finance, healthcare, and retail, for tasks such as data mining, predictive modeling, and financial analysis.
Course Objectives
- Data preparation: Cleaning, transforming, and consolidating large and complex datasets to make them ready for analysis.
- Statistical analysis: Conducting a wide range of statistical analyses, such as descriptive statistics, inferential statistics, and multivariate analysis.
- Predictive modeling: Building and evaluating predictive models using techniques such as linear and logistic regression, decision trees, and neural networks.
- Data visualization: Creating high-quality graphics and visualizations to help communicate the results of the analysis.
- Report generation: Generating detailed reports and summaries of the analysis for stakeholders and decision-makers.
- Automation: Automating repetitive tasks, such as data preparation and statistical analysis, to improve efficiency and reduce errors.
- Data Governance: Enforcing data governance rules, such as data lineage and data quality, to ensure data integrity and compliance.
Overall, the main objective of using SAS for quantitative data management and analysis is to gain insights and make data-driven decisions from large and complex datasets.
Target Audience
- Data analysts and scientists: Professionals who are responsible for collecting, cleaning, and analyzing data to extract insights and make data-driven decisions.
- Business analysts: Professionals who use data and analysis to support strategic decision making and business operations in various industries such as finance, healthcare, and retail.
- Researchers: Professionals in academic and research institutions who use SAS to conduct statistical analysis and generate reports for publications and grant applications.
- Data engineers: Professionals who are responsible for designing, building, and maintaining the data infrastructure and pipelines that are used to store and process large datasets.
- IT professionals: Professionals in IT departments who use SAS to automate data management tasks, enforce data governance rules, and provide data access to different stakeholders.
Overall, the target audience for SAS for quantitative data management and analysis includes anyone who needs to work with large and complex datasets to gain insights and make data-driven decisions.
Program Outline
Topic 1. Data preparation
This step involves cleaning, transforming, and consolidating large and complex datasets to make them ready for analysis. Tasks in this step might include removing missing or duplicate data, handling outliers, and transforming variables to make them suitable for analysis.
Topic 2. Data exploration
This step involves exploring the data to get a sense of its distribution, patterns, and relationships. Tasks in this step might include generating descriptive statistics, creating visualizations, and identifying outliers and anomalies.
Topic 3. Statistical analysis
This step involves conducting a wide range of statistical analyses to extract insights and test hypotheses. Tasks in this step might include conducting inferential statistics, building predictive models, and testing for relationships between variables.
Topic 4. Data visualization
This step involves creating high-quality graphics and visualizations to help communicate the results of the analysis. Tasks in this step might include generating plots, charts, and maps to highlight key findings.
Topic 5. Report generation
This step involves generating detailed reports and summaries of the analysis for stakeholders and decision-makers. Tasks in this step might include creating tables, figures, and summaries to present the key findings and conclusions.
Topic 6. Automation
This step involves automating repetitive tasks, such as data preparation and statistical analysis, to improve efficiency and reduce errors. Tasks in this step might include creating macros, scripts, or functions to automate processes.
Topic 7. Data Governance
This step involves enforcing data governance rules, such as data lineage and data quality, to ensure data integrity and compliance. Tasks in this step might include creating and maintaining data catalog, data lineage, and data quality checks.