C1000-177
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C1000 177 Data Science Watsonx
The certification teaches core data‑science concepts, IBM watsonx platform navigation, data preparation, machine‑learning modeling, and AI governance, enabling professionals to apply and analyze solutions responsibly.
90
Minutes
60
Questions
62/100
Passing Score
$200
Exam Cost
Who Should Take This
It is intended for data analysts, engineers, or managers with basic programming knowledge who wish to deepen their expertise in IBM watsonx and responsible AI. Learners should be comfortable with data pipelines, eager to build predictive models, and aim to ensure ethical compliance in AI projects.
What's Covered
1
Domain 1: Data Science Fundamentals
2
Domain 2: IBM watsonx Platform
3
Domain 3: Data Preparation and Engineering
4
Domain 4: Machine Learning
5
Domain 5: AI Governance and Ethics
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Domain 1: Data Science Fundamentals
3 topics
Statistical Foundations
- Identify fundamental statistical concepts including mean, median, mode, standard deviation, and probability distributions for data analysis.
- Describe data types including numerical, categorical, ordinal, and time series with their appropriate analysis methods and visualizations.
- Explain hypothesis testing procedures including null and alternative hypotheses, p-values, and confidence intervals for data-driven decisions.
- Analyze data distributions to identify patterns, outliers, and statistical relationships for exploratory data analysis investigations.
Data Science Workflow
- Identify the data science lifecycle phases including business understanding, data collection, preparation, modeling, evaluation, and deployment.
- Describe common data science roles including data scientist, data engineer, ML engineer, and data analyst with their responsibilities.
- Explain the CRISP-DM methodology for structured data science project execution with iterative refinement and stakeholder communication.
- Analyze business problems to determine appropriate data science approaches including classification, regression, and clustering solutions.
Exploratory Data Analysis
- Describe the process of exploratory data analysis including summary statistics, missing value detection, and outlier identification in IBM watsonx environments.
- Apply visualization techniques including histograms, scatter plots, and box plots to identify data distribution patterns within Watson Studio notebooks.
- Analyze correlation matrices and feature importance rankings to determine the most predictive variables for a given modeling objective.
2
Domain 2: IBM watsonx Platform
2 topics
watsonx.ai
- Identify watsonx.ai platform capabilities including foundation model access, prompt engineering, model tuning, and deployment features.
- Describe IBM Granite model family with model sizes, capabilities, and use cases for enterprise generative AI applications.
- Explain Watson Studio project creation, asset management, and collaboration features for team-based data science workflows.
- Explain AutoAI automated machine learning for pipeline generation, feature engineering, and model selection optimization.
- Analyze watsonx.ai capabilities to recommend appropriate tools and models for specific data science and AI use cases.
- Describe the integration capabilities between watsonx.ai and external data sources including cloud object storage, relational databases, and streaming platforms.
watsonx.data
- Identify watsonx.data open lakehouse architecture with Presto query engine, Spark processing, and Apache Iceberg table format.
- Explain watsonx.data data source connections including databases, object storage, and streaming sources for unified analytics.
- Explain data virtualization capabilities for querying data across multiple sources without physical movement or duplication.
- Analyze data storage and processing requirements to recommend appropriate watsonx.data configurations and query optimization.
- Analyze the trade-offs between AutoAI-generated models and manually tuned models for different business scenarios and data characteristics.
3
Domain 3: Data Preparation and Engineering
3 topics
Data Quality
- Identify common data quality issues including missing values, duplicates, inconsistencies, and format errors in datasets.
- Explain data cleaning techniques including imputation, deduplication, normalization, and outlier treatment for data preparation.
- Explain data profiling using Watson Studio tools for automated data quality assessment and column statistics generation.
- Analyze data quality assessment results to determine appropriate cleaning strategies and their impact on model accuracy.
Feature Engineering
- Identify feature engineering techniques including encoding, scaling, binning, and feature creation for model performance improvement.
- Explain feature selection methods including correlation analysis, mutual information, and recursive feature elimination for dimensionality.
- Explain data transformation pipelines using Watson Studio Data Refinery for repeatable data preparation workflow automation.
- Analyze feature importance and relationships to select optimal feature sets for specific machine learning model requirements.
Data Pipeline Automation
- Identify the components of an automated data pipeline including extraction connectors, transformation operators, and quality gates in DataStage.
- Configure automated data transformation workflows using DataStage flow designer to handle schema mapping, type conversion, and null value imputation.
- Analyze pipeline execution logs and monitoring dashboards to diagnose data quality degradation and throughput bottlenecks.
4
Domain 4: Machine Learning
3 topics
Supervised Learning
- Identify supervised learning algorithms including linear regression, logistic regression, decision trees, and random forests with use cases.
- Describe model evaluation metrics including accuracy, precision, recall, F1 score, AUC-ROC, and RMSE for performance assessment.
- Explain model training workflows in Watson Studio including data splitting, hyperparameter tuning, and cross-validation procedures.
- Explain classification and regression model deployment using Watson Machine Learning with REST API endpoint creation.
- Analyze model evaluation results to compare algorithm performance and recommend optimal models for business requirements.
Unsupervised Learning
- Identify unsupervised learning methods including k-means clustering, hierarchical clustering, and principal component analysis techniques.
- Explain clustering analysis workflows for customer segmentation, anomaly detection, and pattern discovery in unlabeled datasets.
- Explain dimensionality reduction using PCA for visualizing high-dimensional data and reducing feature space complexity.
- Analyze clustering results to evaluate cluster quality, determine optimal cluster counts, and interpret segment characteristics.
- Implement hyperparameter tuning experiments using watsonx.ai to optimize model performance across accuracy, precision, and recall metrics.
Deep Learning Basics
- Identify neural network fundamentals including layers, activation functions, loss functions, and backpropagation for model training.
- Describe common neural network architectures including feedforward, convolutional, and recurrent networks with their application domains.
- Explain transfer learning concepts for leveraging pre-trained models to improve performance on domain-specific tasks with limited data.
- Analyze deep learning model performance and resource requirements to recommend appropriate architectures for specific AI tasks.
- Evaluate ensemble modeling strategies including bagging, boosting, and stacking to improve prediction robustness on imbalanced datasets.
5
Domain 5: AI Governance and Ethics
3 topics
Model Governance
- Identify AI governance principles including fairness, transparency, accountability, and privacy for responsible AI development practices.
- Explain watsonx.governance capabilities for model monitoring, performance tracking, and lifecycle management across deployments.
- Explain bias detection and mitigation techniques using watsonx.governance for ensuring fair model predictions across populations.
- Analyze AI governance requirements to recommend appropriate monitoring, documentation, and compliance practices for enterprise AI.
Responsible AI
- Identify model explainability methods including SHAP, LIME, and feature importance for interpreting machine learning model predictions.
- Explain model documentation practices including model cards, data sheets, and audit trails for regulatory compliance preparation.
- Explain AI risk assessment procedures for evaluating model impact, data sensitivity, and deployment context in enterprise settings.
- Analyze ethical considerations in AI deployment to recommend appropriate safeguards, monitoring, and human oversight mechanisms.
Model Lifecycle Management
- Identify the stages of the model lifecycle including development, validation, deployment, monitoring, and retirement in IBM watsonx.governance.
- Configure model monitoring dashboards in watsonx.governance to track prediction drift, data drift, and fairness metrics over time.
- Analyze model performance degradation patterns using watsonx.governance alerts to determine appropriate retraining triggers and thresholds.
- Evaluate the impact of concept drift versus data drift on deployed model accuracy and recommend appropriate remediation strategies.
Scope
Included Topics
- All domains of Foundations of Data Science Using IBM watsonx (C1000-177): data science fundamentals, watsonx platform, data preparation, model building, and AI governance.
- Data science fundamentals: statistics, probability, data types, exploratory analysis, and machine learning concepts.
- IBM watsonx platform: watsonx.ai, watsonx.data, Watson Studio, AutoAI, and Jupyter notebook integration.
- Data preparation: data cleaning, feature engineering, data transformation, and data quality assessment techniques.
- Machine learning: supervised and unsupervised learning, model training, evaluation metrics, and model deployment.
- AI governance: watsonx.governance, model monitoring, bias detection, explainability, and responsible AI practices.
Not Covered
- Advanced deep learning architectures.
- Programming language syntax details.
- Non-IBM data science platforms.
- Statistical proof derivations.
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