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IBM C1000-177 Exam Syllabus Topics:

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