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IBM C1000-177 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Evaluate the Business Problem | 16% | - Define project scope and success criteria - Identify appropriate analytical tools and methodologies - Translate business objectives into data science/ML/AI solutions - Formulate testable hypotheses |
| Development Tools and Techniques | 13% | - Navigate IBM watsonx.ai, Watson Studio, and Jupyter environments - Use Python and libraries (Pandas, NumPy, Matplotlib, Scikit-learn) - Select appropriate statistical and modeling techniques - Work with structured and unstructured data formats |
| Pre-Processing and Feature Engineering | 33% | - Integrate data from multiple sources - Clean and normalize datasets - Perform feature transformation and scaling - Apply categorical and numerical encoding techniques - Handle missing values and imbalanced data - Select relevant features and reduce dimensionality |
| Model Selection, Training, Evaluation, and Presentation | 17% | - Interpret results and communicate insights to stakeholders - Split data into training, validation, and test sets - Evaluate performance using correct metrics - Train and tune model parameters - Apply responsible AI and bias mitigation principles - Choose appropriate machine learning algorithms |
| Perform Exploratory Data Analysis | 21% | - Analyze statistical distributions and correlations - Use visualization techniques to identify patterns and relationships - Detect missing values, anomalies, and outliers - Assess data quality and suitability for modeling - Apply descriptive statistics and summary metrics |
