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The Pega Certified Data Scientist (PCDS) 80V1 2019 Certification Exam is an essential certification for professionals who want to demonstrate their knowledge and skills in building and deploying AI and machine learning models using the Pega Platform. Pega Certified Data Scientist (PCDS) 80V1 2019 certification is recognized globally, and it demonstrates to employers that the certified data scientist has the skills and knowledge required to effectively use Pega tools for data science. With the demand for data scientists increasing worldwide, obtaining the Pega Certified Data Scientist (PCDS) 80V1 2019 Certification can help data scientists advance their careers and increase their earning potential.
The PEGAPCDS80V1_2019 Exam is aimed at individuals who want to work as a data scientist or wish to enhance their skills in the field of data science. Pega Certified Data Scientist (PCDS) 80V1 2019 certification demonstrates the ability to use data analytics tools to support business decisions, automate processes, and identify patterns and trends in data. Passing the exam shows the candidate's proficiency in using the Pega platform to work with data and analyze it effectively. With this certification, you can show prospective employers that you are an expert in using Pega software to address real-world business challenges.
Pegasystems PEGAPCDS80V1_2019 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Pega Customer Decision Hub Implementation | 20% | - Integration with predictive models - Outcome analysis and reporting - Real-time decisioning |
| Text and Predictive Analytics Tools | 10% | - Text analytics configuration - Predictive analyzer usage |
| Predictive Analytics Fundamentals | 20% | - Predictive model types - Statistical modeling concepts - Data preparation and feature engineering |
| Decision Strategy Design | 25% | - Eligibility and arbitration logic - Decision strategy canvas - Simulation and validation |
| Adaptive Model Management | 25% | - Model refresh and retraining - Adaptive model configuration - Model performance monitoring |
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