PEGAPCDS86V1

PEGAPCDS86V1 Pega Data Scientist Certification Training

The Pega Certified Data Scientist (PCDS) 86V1 exam is a certification exam that validates the skills and knowledge of individuals in data science, particularly in the context of the Pega platform. This exam is designed for individuals who have experience in data science and want to demonstrate their proficiency in using Pega’s data science tools to solve real-world problems. The PEGAPCDS86V1 exam covers a wide range of topics related to data science, including data preparation, machine learning, model deployment, and model monitoring. In this article, we will explore some of the key concepts and topics that are covered in the PEGAPCDS86V1 exam.

Data Preparation

Data preparation is a critical step in the data science process that involves cleaning, transforming, and preprocessing data to make it suitable for analysis. The PegaPCDS86V1 exam covers various topics related to data preparation, including data cleaning, data transformation, and data preprocessing. You will need to demonstrate your proficiency in using Pega’s data preparation tools to perform these tasks.

Machine Learning

Machine learning is a branch of artificial intelligence that involves building predictive models from data. The PEGAPCDS86V1 exam covers various topics related to machine learning, including supervised learning, unsupervised learning, and deep learning. You will need to demonstrate your proficiency in using Pega’s machine learning tools to build predictive models and solve real-world problems.

Model Deployment

Model deployment is the process of making predictive models available for use in production environments. The PEGAPCDS86V1 exam covers various topics related to model deployment, including model packaging, model deployment, and model serving. You will need to demonstrate your proficiency in using Pega’s model deployment tools to deploy predictive models and make them available for use.

Model Monitoring

Model monitoring is the process of tracking the performance of predictive models over time and making adjustments as needed. The PEGAPCDS86V1 exam covers various topics related to model monitoring, including model performance monitoring, model retraining, and model versioning. You will need to demonstrate your proficiency in using Pega’s model monitoring tools to monitor the performance of predictive models and make adjustments as needed.

PEGAPCDS86V1 Exam Format

The PEGAPCDS86V1 exam consists of 60 multiple-choice questions that must be completed in 90 minutes. The exam is administered through Pega’s online testing platform and can be taken from anywhere with an internet connection. The passing score for the exam is 65%, and you will receive your results immediately after completing the exam.

PEGAPCDS86V1 Exam Preparation Tips

To prepare for the PCDS 86V1 exam, it is essential to have a solid understanding of the key concepts and topics covered in the exam. Some tips to help you prepare for the exam include:

Review the exam blueprint: The exam blueprint provides an outline of the topics covered in the exam and the relative weight of each topic. Use this information to guide your study plan.

Take practice exams: Practice exams are an excellent way to familiarize yourself with the exam format and the types of questions that will be asked. Certs Grade offers practice exams for the PCDS 86V1 exam that can help you prepare at Certsgrade.com website.

Review Pega’s documentation: Pega’s documentation provides detailed information on the various tools and features that are covered in the exam. Review this documentation to gain a deeper understanding of how to use Pega’s data science tools.

Attend training courses: Certsgrade offers training courses for PEGA PCDS 86V1 exam that cover the topics and concepts covered in the exam. These courses can provide you with hands-on experience using Pega’s data science tools and help you prepare for the exam.

Course Content for PEGAPCDS86V1 Exam

This course is designed to help candidates prepare for the Pegasystems certification exam:

Introduction to Pega

• Overview of Pega and its products
• The importance of Pega in the business world
• Key concepts of Pega and its architecture

Data Science Fundamentals

• Introduction to Data Science
• Understanding Data, Types of Data and Data Sources
• Data Pre-processing and Data Wrangling
• Exploratory Data Analysis (EDA)
• Data Visualization
• Statistical Inference
• Introduction to Machine Learning
• Supervised and Unsupervised Learning
• Model Selection and Evaluation
• Ensemble Learning and Deep Learning

Pega and Data Science

• Pega’s approach to data science and machine learning
• Key concepts and terminology in Pega data science
• Understanding Pega’s data science tools and techniques
• Exploring Pega’s data science capabilities and functionalities

Pega Predictive Analytics

• Introduction to predictive analytics in Pega
• Pega’s predictive analytics tools and techniques
• Data preparation for predictive analytics
• Building and validating predictive models
• Deploying and using predictive models in Pega

Pega Text Analytics

• Introduction to text analytics in Pega
• Pega’s text analytics tools and techniques
• Pre-processing text data
• Building and validating text analytics models
• Deploying and using text analytics models in Pega

Pega Decisioning and Next-Best-Action

• Introduction to decisioning in Pega
• Pega’s decisioning tools and techniques
• Building decision strategies in Pega
• Deploying and using decision strategies in Pega
• Introduction to Next-Best-Action (NBA) in Pega
• Building and deploying NBA strategies in Pega

Pega Adaptive Analytics

• Introduction to adaptive analytics in Pega
• Pega’s adaptive analytics tools and techniques
• Building and deploying adaptive analytics models in Pega
• Evaluating and monitoring adaptive analytics models in Pega

Pega Data Integration

• Overview of Pega data integration capabilities
• Integrating Pega with external data sources
• Importing and exporting data in Pega
• Data synchronization and replication in Pega

Pega Case Management and Data Science

• Integrating data science with Pega case management
• Understanding Pega’s case management tools and techniques
• Building data science models for case management in Pega
• Deploying and using data science models in Pega case management

PEGA Certified Data Scientists Related Exams

• Pega Certified System Architect (PCSA) 87V1
• Lead System Architect (LSA) Pega Architecture Exam 86V1
• Certified Pega Decisioning Consultant (PCDC) 87V1
• Pega Certified Data Scientist (PCDS) 80V1 2020
• LSA Pega Architecture 74V1

 

In conclusion, this IT course provides a comprehensive overview of the latest technologies and industry best practices as well as increases the importance of data science in the business world and career opportunities in the field. We hope you found it useful. Keep learning and growing!

 

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