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IBM C1000-154 exam is designed to test the knowledge and skills of data scientists who work with IBM Watson. IBM Watson Data Scientist v1 certification is aimed at professionals who want to demonstrate their proficiency in using Watson to analyze large sets of data and gain insights into complex business problems. C1000-154 exam covers a wide range of topics related to data science, including data exploration, data preparation, statistical analysis, machine learning, and data visualization.
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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
| Topic 2: Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations |
| Topic 3: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 4: Deploy the Solution | 10% | - Ensure scalability and reliability - Monitor model performance post-deployment - Deploy models as APIs in Watson |
| Topic 5: Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
| Topic 6: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 7: Build the Model | 20% | - Perform hyperparameter tuning - Compare and select best performing models - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Topic 8: Prepare the Data | 18% | - Feature engineering and selection - Handle missing values and outliers - Clean, transform, and normalize datasets - Use Watson tools for data preparation |
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