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IBM C1000-154 is an industry-recognized certification exam designed for professionals who want to demonstrate their knowledge and skills in IBM Watson Data Scientist v1. IBM Watson Data Scientist v1 certification is intended for individuals who have experience working with Watson Studio, Watson Knowledge Catalog, Watson Machine Learning, and related IBM Cloud services. C1000-154 exam tests the candidate's ability to design and develop machine learning models, work with data and analytics, and use Watson Studio to create and deploy machine learning models.
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Obtaining the IBM Watson Data Scientist certification can be beneficial for individuals who want to enhance their career prospects in the field of data science. IBM Watson Data Scientist v1 certification can help data scientists and data analysts to demonstrate their proficiency in using IBM Watson Studio and IBM Watson Knowledge Catalog, and it can also help organizations to identify individuals who have the required skills and knowledge in data science. Additionally, this certification can provide professionals with opportunities to work on exciting data science projects, collaborate with experts in the field, and build their reputation as data science professionals.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Topic 2: Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - AutoAI and automatic model building - Data asset management - Data governance and cataloging |
| Topic 3: Watson AI Services and Deployment | 10-15% | - Monitoring deployed models - Watson Assistant integration - Watson Discovery overview - Deploying models as REST APIs |
| Topic 4: Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration |
| Topic 5: Machine Learning and Model Development | 20-25% | - Supervised and unsupervised learning concepts - Feature engineering and selection - Model deployment and monitoring - Model training, evaluation, and optimization |
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