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The PCDS 80V1 2020 certification is designed to test your understanding of Pega's data science features, as well as your ability to create data science solutions that can help businesses make data-driven decisions. You'll learn about data preparation, model building, model evaluation, and deployment. During the exam, you'll be required to apply your knowledge of these topics to a series of real-world scenarios, in order to demonstrate your mastery of Pega's data science capabilities.
The Pega Certified Data Scientist (PCDS) 80V1 2020 exam is a vendor-neutral certification, which means that it is not limited to any particular industry or technology. It is ideal for professionals who are interested in working with large datasets, developing predictive models, and leveraging machine learning techniques to gain insights into customer behavior and business operations.
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The PEGAPCDS80V1_2020 exam blueprint includes data science ethics, data quality management, data governance, and data privacy. Pega Certified Data Scientist (PCDS) 80V1 2020 certification demonstrates the candidate's compliance with standard data science principles and their ability to work with sensitive data, protect data privacy, and maintain data integrity. This exemplifies the candidate's ethical and responsible practices in data science, promoting trust and transparency among clients and stakeholders. Furthermore, the Pega Certified Data Scientist credential confirms the candidate's knowledge of best practices in implementing machine learning models in a production environment, promoting efficient and effective use of resources.
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Reference: https://academy.pega.com/exam/pega-certified-data-scientist
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Pegasystems PEGAPCDS80V1_2020 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Next-Best-Action | 4% | - Introduction to Pega for data scientists |
| Topic 2: Predictive Analytics | 32% | - Using third-party predictive models - Building predictive models |
| Topic 3: Decision Strategies | 16% | - Decisioning logic and hierarchy
|
| Topic 4: Text Analytics | 12% | - Sentiment and text classification use cases |
| Topic 5: Adaptive Analytics | 36% | - Monitoring model performance - Adaptive model creation and learning - Quantifying ML impact |
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