Description

Introduction

The SAS Certified Data Scientist program is designed for professionals looking to master advanced analytics techniques, including predictive modeling, machine learning, and data visualization. This training equips participants with the skills needed to extract actionable insights from complex datasets and develop data-driven solutions to business challenges.

Through hands-on exercises and real-world case studies, learners will gain proficiency in using SAS tools and techniques to perform advanced analytics and achieve certification as a SAS Data Scientist.

Prerequisites

• Basic understanding of data analytics and programming concepts
• Familiarity with SAS programming is beneficial

Table of Contents

1. Introduction to SAS Data Science
   1.1 Overview of the SAS Certified Data Scientist Credential
   1.2 Key SAS Tools for Data Science
   1.3 Understanding the Analytics Lifecycle
2. Data Preparation and Management
   2.1 Importing, Cleaning, and Transforming Data with SAS
   2.2 Advanced Data Manipulation with PROC SQL
   2.3 Creating Effective Data Sets for Analysis
   2.4 Data Quality and Governance Best Practices
3. Statistical Analysis and Hypothesis Testing
   3.1 Descriptive and Inferential Statistics
   3.2 Applying Statistical Methods in SAS(Ref: QlikSense Data Analytics and Business Intelligence)
   3.3 Understanding and Validating Assumptions
   3.4 Conducting Hypothesis Tests and ANOVA
4. Predictive Modeling with SAS
   4.1 Building Linear and Logistic Regression Models
   4.2 Decision Trees and Ensemble Methods
   4.3 Time Series Analysis and Forecasting
   4.4 Model Selection, Validation, and Performance Metrics
5. Machine Learning with SAS
   5.1 Introduction to Machine Learning Algorithms
   5.2 Implementing Clustering and Classification Models
   5.3 Feature Engineering and Dimensionality Reduction
   5.4 Hyperparameter Tuning and Model Optimization
6. Data Visualization and Reporting
   6.1 Creating Interactive Dashboards with SAS Visual Analytics
   6.2 Designing Clear and Insightful Reports
   6.3 Storytelling with Data Using Visualizations
   6.4 Best Practices for Effective Communication
7. Text Analytics and Natural Language Processing (NLP)
   7.1 Extracting Insights from Text Data
   7.2 Sentiment Analysis and Topic Modeling
   7.3 Using SAS Text Miner for NLP Tasks
   7.4 Applications of Text Analytics in Business
8. Big Data Analytics with SAS
   8.1 Leveraging SAS for Hadoop Integration
   8.2 Distributed Data Processing with SAS Grid Manager
   8.3 Handling Large Datasets with SAS LASR Server
   8.4 Performance Optimization for Big Data Workflows
9. SAS Certification Exam Preparation
   9.1 Understanding the Certification Process
   9.2 Exam Topics and Study Resources
   9.3 Practice Tests and Common Pitfalls
   9.4 Tips for Success in the SAS Certification Exam
10. Case Studies and Real-World Applications
    10.1 Retail Analytics: Customer Segmentation and Forecasting
    10.2 Financial Analytics: Risk Assessment and Fraud Detection
    10.3 Healthcare Analytics: Patient Outcomes and Resource Optimization
    10.4 Marketing Analytics: Campaign Effectiveness and ROI

Conclusion

This training provides a comprehensive foundation for becoming a SAS Certified Data Scientist, covering essential skills like data preparation, predictive modeling, and machine learning. By completing this program, participants will be prepared to tackle complex analytics challenges and contribute significantly to data-driven decision-making processes.

With certification as a SAS Data Scientist, you will be equipped to lead advanced analytics projects and drive meaningful business outcomes, ensuring your expertise remains in demand across industries.

Reference