Quantitative Data Management and Analysis with SAS

The Quantitative Data Management and Analysis with SAS course provides participants with the skills to manage and analyze quantitative data effectively using SAS software. Recognized as a leading tool in data analysis and statistics, SAS empowers users with powerful techniques and best practices for data handling, manipulation, and advanced analytics.

Objective

The main goal of this training is to enable participants to proficiently manage and analyze data using SAS. Upon completion, participants will be able to:

Understand the concepts of quantitative data management and statistical analysis.
Import, clean, and transform datasets in SAS.
Conduct exploratory data analysis and descriptive statistics.
Perform hypothesis testing and draw statistical inferences.
Create visually appealing and interpretable data visualizations.
Develop and assess predictive models using SAS.
Present analytical findings in comprehensive reports.

Organizational Benefits

Organizations attending this training will gain the following advantages:

Enhanced Efficiency: Learn how to handle and manage large datasets accurately.
Advanced Analysis Capabilities: Benefit from SAS’s robust statistical and modeling tools.
Time and Resource Savings: Streamline data processing with SAS, boosting productivity.
Improved Communication: Create impactful data visualizations for stakeholders.
Informed Decision-Making: Make evidence-based decisions using quantitative insights.
Alignment with Standards: Adopt industry-standard methods for analytics.

Target Audience

This course is designed for professionals and organizations engaged in data analytics and decision-making processes, including:

Data Analysts and Researchers
Statisticians and Data Scientists
Business Analysts and Market Researchers
Data Managers and Administrators
Policy Planners and Decision-Makers
Graduates in Quantitative Disciplines

Duration

This intensive 5-day course provides participants with a deep understanding of SAS and its application in quantitative analytics.

Course Outline

This program includes over 20 comprehensive modules, providing a holistic approach to quantitative data analysis using SAS. Below is a sample course outline:

Module 1: Introduction to Quantitative Analytics

Overview of quantitative data analysis workflows.
Introduction to SAS software and its features.
Understanding data quality essentials.

Module 2: Importing and Exporting Data in SAS

Importing datasets from diverse sources (Excel, CSV, etc.).
Exporting analysis results into external formats.
Handling missing values effectively.

Module 3: Data Manipulation and Transformation

Cleaning and validating datasets.
Combining, merging, and reshaping data structures.
Optimizing data for analysis.

Module 4: Descriptive Statistics and Exploratory Analysis

Computing summary statistics with SAS.
Visualizing distributions and key metrics.
Identifying trends and data patterns.

Module 5: Hypothesis Testing and Statistical Inference

Conducting various statistical tests (t-tests, ANOVA, chi-square).
Understanding p-values and confidence intervals.
Drawing meaningful conclusions from data.

Module 6: Regression Analysis

Implementing simple and multiple regressions.
Diagnosing regression model assumptions.
Interpreting regression outputs.

Module 7: Data Visualization in SAS

Designing effective graphs and charts.
Customizing and enhancing visual outputs.
Leveraging interactive visualization techniques.

Module 8: Predictive Modeling

Building predictive models with regression and decision trees.
Evaluating and optimizing predictive algorithms.
Applying predictive insights to solve business problems.

Module 9: Survival Analysis

Overview of survival analysis methods.
Kaplan-Meier and Cox regression applications.
Interpreting survival probabilities and hazard ratios.

Module 10: Advanced SAS Programming Techniques

Efficient data processing through SAS programming.
Automating tasks with SAS macros.
Debugging and error handling best practices.

Module 11: Longitudinal Data Analysis

Handling repeated measures and time-dependent data.
Employing mixed models for analysis.
Addressing missing data challenges.

Module 12: Clustering and Segmentation

Fundamentals of clustering analysis.
Identifying actionable segments.
Applying insights for targeted strategies.

Module 13: Factor Analysis and Dimensionality Reduction

Conducting factor analysis and PCA.
Reducing dimensionality for simpler interpretations.
Applying results to streamline datasets.

Module 14: Survey Data Analytics

Designing and analyzing survey datasets.
Using weights and complex design corrections.
Generating actionable survey insights.

Module 15: Time Series Analysis

Forecasting trends and patterns in data.
Identifying seasonal and cyclic behaviors.
Applying ARIMA and other modeling techniques.

Module 16: Advanced Analytics and Machine Learning

Logistic regression and classification methods.
Decision-making with machine learning techniques.
Interpreting complex results for stakeholders.

Module 17: Case Studies and Real-World Applications

Applying course concepts to industry scenarios.
Solving analytical challenges through hands-on projects.

General Notes

Training can be customized to suit specific participant needs.
Proficiency in English is required.
Sessions include guided presentations, practical exercises, and group activities led by experienced facilitators.
Participants receive a Stepsure Training and Research Institute Certificate upon completion.
Training is conducted onsite, online, or in-house based on client preferences.
Flexible course durations and content adjustments available.
Fees cover training materials, meals (onsite), and certificates. Participants handle personal expenses like travel and accommodation.
Optional services like visa processing and equipment rental available upon request.
Group discounts of 10%–50% are offered for two or more participants.
Payment is required before the training start date.