Data Analysis Training Using SAS
All 3 days only R11,500.00 (Excl. VAT) per delegate
About This Course
Statistical Analysis System, SAS, is the analytical leader offering an easy to use statistical software used to solve a variety of research problems and for business intelligence. It provides a wide range of capabilities such information retrieval and data management, simple and complex data analysis. SAS enterprise guide makes it easy to conduct data cleaning and management, graphics, descriptive analysis and advanced analysis. Like Stata and SPSS, SAS is considered one of the most powerful statistical packages for data management and analysis.
SAS is used by many research institutions, business organisations, governments and universities for a number of reasons. Some are provided below.
- It is a fast, powerful statistical package designed for researchers and data analysts of all disciplines.
- It is powerful for statistical analysis, econometric and data mining.
- It is also useful for data management and data warehousing. It can also interact with the other databases.
- SAS is a comprehensive set of statistical tools, integrated to run descriptive statistics, regression, advanced statistics and more.
- SAS is a versatile statistical analysis software and makes use of both point-and-click and programming interface.
- It enables users to produce automated reports in different formats including HTML and PDF.
- Many organisations such as government departments, NGOs and private companies SAS making SAS a universal skill.
- One of the main advantages of SAS is its versatility in analyzing different types of data across different fields from social research, health research to business intelligence. As a result, it is considered by many to be one of the top three statistical packages.
The course is suitable for individuals who are new to SAS or who feel they would benefit from a refresher course. No prior knowledge of SAS is assumed; however, knowledge of basic statistics (e.g., p-values, significance tests) is advantageous. The course objectives include introducing participants to SAS and SAS files. They will learn to use SAS for data cleaning, management and statistical analysis including importing other types of data files like Excel, into SAS. The theory behind the statistics will be explained and participants will be able to interpret statistical outputs. They will also achieve the understanding of descriptive statistics, confidence intervals, p-values and bi-variate inferential statistics (hypothesis testing). They will also improve their skills to critically analyse research reports. The three-day training will teach participants the following:
Day 1 - Learn to Use SAS
- Study designs and data collection methods
- From variables to datasets
- Introduction to SAS
- Explore the capabilities of SAS
- Functionality of SAS components
- Importing data from Microsoft Excel to SAS
Day 2 - Data Analysis: Descriptive statistics and Subgroup analysis
- Introduction to descriptive statistics
- Making sense of data: more descriptive statistics
- Subgroup analysis
- Plotting and manipulating SAS graphs
Day 3 - Hypothesis testing and inferential statistics
- Cross-tabulation and chi-square test
- Risk factor analysis
- Different types of statistical tests
- Measures of effect
- Is the association confounded: understanding confounding?
- Multivariate regression: theory and introduction to practice
- Class exercise and presentations
The course will teach participants the following:
After the training, participants will be able to take raw data collected in their settings, clean them, summarise them, analyse them and take appropriate action. They will also achieve the understanding of descriptive statistics and bi-variate inferential statistics. They will also be able to critically review research reports and papers. In addition to this, participants will be able to use SAS in their practical professional work to produce neat and reproducible analysis outputs.
- Research analysts
- Data analysts
- Programme managers
- Market researchers
- Clinical and medical researchers
- Government practitioners