Course Overview

• This program is designed for learners who want to build a solid foundation in data analytics and progress quickly to advanced topics. It starts from the basics of SAS and SQL, then moves into data management, data manipulation, complex queries, statistical analysis, and macro programming.
• The course covers how SQL is used in database management and how SAS is applied in data mining and statistical analysis. Through hands-on projects from industries such as finance, retail, and healthcare, students learn how to apply these tools in real-world business environments.
• The curriculum includes workplace-oriented simulations, 1-on-1 mock interviews, and résumé coaching to help learners prepare for real recruitment scenarios and qualify for advanced data analyst roles.

Software Overview: What Is SAS?

SAS (Statistical Analysis System) is one of the leading commercial analytics platforms worldwide. It is widely used in banking, finance, insurance, pharmaceuticals, and market research. SAS supports statistical analysis, data mining, predictive modeling, data management, and business intelligence, and is highly trusted in enterprises for its robust data-processing capabilities and flexible programming language.

Core Capabilities of SAS

• Data Management & Processing:
  • Data Import & Export: Read and process a variety of data sources (CSV, Excel, databases, etc.) using SAS programs and export analysis outputs.
  • Data Cleaning: Improve data quality through transformations, missing-value handling, and outlier detection.
• Statistical Analysis:
  • Descriptive Statistics: Compute means, medians, standard deviations, and other summary measures to understand data distributions.
  • Inferential Statistics: Apply hypothesis testing, ANOVA, and regression analysis to draw conclusions about populations from sample data.
• Data Modeling:
  • Regression Modeling: Build linear and logistic regression models for prediction, risk assessment, and business interpretation.
  • Time Series Analysis: Use ARIMA, exponential smoothing, and related techniques for forecasting and trend analysis.
  • Classification & Clustering: Apply decision trees and clustering methods for customer segmentation, risk stratification, and pattern discovery.
• Reporting & Visualization:
  • Data Visualization: Create bar charts, scatter plots, boxplots, and other visuals to communicate insights clearly.
  • Professional Reporting: Use ODS (Output Delivery System) to generate standardized analytical reports for business stakeholders.
• Advanced Analytics:
  • Macro Programming: Write reusable, parameter-driven SAS macros to automate repetitive tasks and streamline workflows.
  • Data Mining: Use decision trees, association rules, and other techniques to uncover hidden patterns in large datasets.
• Enterprise Applications:
  • Decision Support: Support strategic and operational decisions such as sales forecasting, customer behavior analysis, and portfolio monitoring.
  • Operational Optimization: Use data-driven insights to optimize business processes and improve efficiency and service quality.