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IBM C1000-177 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Perform Exploratory Data Analysis | 21% | - Apply descriptive statistics and summary metrics - Assess data quality and suitability for modeling - Analyze statistical distributions and correlations - Use visualization techniques to identify patterns and relationships - Detect missing values, anomalies, and outliers |
| Topic 2: Pre-Processing and Feature Engineering | 33% | - Clean and normalize datasets - Select relevant features and reduce dimensionality - Integrate data from multiple sources - Apply categorical and numerical encoding techniques - Perform feature transformation and scaling - Handle missing values and imbalanced data |
| Topic 3: Development Tools and Techniques | 13% | - Select appropriate statistical and modeling techniques - Work with structured and unstructured data formats - Navigate IBM watsonx.ai, Watson Studio, and Jupyter environments - Use Python and libraries (Pandas, NumPy, Matplotlib, Scikit-learn) |
| Topic 4: Model Selection, Training, Evaluation, and Presentation | 17% | - Train and tune model parameters - Split data into training, validation, and test sets - Choose appropriate machine learning algorithms - Interpret results and communicate insights to stakeholders - Apply responsible AI and bias mitigation principles - Evaluate performance using correct metrics |
| Topic 5: Evaluate the Business Problem | 16% | - Formulate testable hypotheses - Translate business objectives into data science/ML/AI solutions - Define project scope and success criteria - Identify appropriate analytical tools and methodologies |
