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Huawei H13-731 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Introduction to Data Mining | 5% | - Data Mining concepts and process - CRISP-DM framework - Big Data and Data Mining integration |
| Regression & Predictive Modeling | 10% | - Model tuning and regularization - Linear and Logistic Regression |
| Data Preprocessing | 13% | - Data cleaning, integration, transformation - Feature selection and dimensionality reduction - Handling missing values and outliers |
| Association Rule Mining | 8% | - Rule evaluation and application - Apriori, FP-Growth algorithms |
| Machine Learning & Deep Learning | 15% | - Neural Networks basics - Distributed machine learning - Huawei ModelArts and MindSpore |
| Classification Algorithms | 15% | - Decision Tree, Random Forest - SVM, Naive Bayes, KNN - Model evaluation and metrics |
| Big Data Architecture & Governance | 12% | - Spark framework and components - Data governance, security and quality - Huawei FusionInsight platform - Hadoop ecosystem architecture |
| Big Data Analysis & Optimization | 10% | - Performance tuning and optimization - End-to-end mining project design - Spark SQL, Spark MLlib |
| Clustering Algorithms | 12% | - K-Means, DBSCAN, Hierarchical clustering - Clustering validation techniques |
