Huawei HCIE-Big Data-Data Mining Exam Syllabus

HCIE-Big Data-Data Mining PDF, H13-731 Dumps, H13-731 PDF, HCIE-Big Data-Data Mining VCE, H13-731 Questions PDF, Huawei H13-731 VCE, Huawei HCIE-Big Data-Data Mining Dumps, Huawei HCIE-Big Data-Data Mining PDF Use this quick start guide to collect all the information about Huawei HCIE-Big Data-Data Mining (H13-731) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the H13-731 Huawei Certified ICT Expert-Big Data-Data Mining exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Huawei HCIE-Big Data-Data Mining certification exam.

The Huawei HCIE-Big Data-Data Mining certification is mainly targeted to those candidates who want to build their career in Cloud Platform & Cloud Services domain. The Huawei Certified ICT Expert-Big Data-Data Mining exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Huawei HCIE-Big Data-Data Mining.

Huawei HCIE-Big Data-Data Mining Exam Summary:

Exam Name	Huawei Certified ICT Expert-Big Data-Data Mining
Exam Code	H13-731
Exam Price	$300 (USD)
Duration	90 mins
Number of Questions	60
Passing Score	600 / 1000
Books / Training	HCIE-Big Data-Data Mining V3.0 training Material
Schedule Exam	Pearson VUE
Sample Questions	Huawei HCIE-Big Data-Data Mining Sample Questions
Practice Exam	Huawei H13-731 Certification Practice Exam

Huawei H13-731 Exam Syllabus Topics:

Topic	Details	Weights
Introduction to Data Mining	- Data Mining Overview What Is Data Mining Typical Data Mining Methods Data Mining Programming Languages, Frameworks, and Toolkits Path to Learn Data Mining - Data Mining Process	5%
Data Preprocessing and Feature Engineering	- Data Preprocessing Preprocessing Overview Data Cleaning Data Transformation Data Description - Feature Engineering Feature Selection and Combination Dimensionality Reduction	13%
Regression and Classification	- Classification Algorithms Concept Logistic Regression K-Nearest Neighbors Naive Bayes Decision Tree Support Vector Machine - Regression Algorithms Linear Regression Polynomial Regression Ridge Regression Lasso Regression Elastic Net Regression Support Vector Regression Decision Tree Regression - Ensemble Algorithms Basic Concepts Ensemble Methods Bagging Boosting Stacking Voting	13%
Clustering and Dimensionality Reduction	- Clustering Algorithms Concepts Prototype-based (Partitioning) Clustering Hierarchical Methods Density-based Methods Spectral Clustering - Dimensionality Reduction Algorithms Concepts Linear Dimensionality Reduction Nonlinear Dimensionality Reduction	13%
Association Analysis and Recommendation	- Association Algorithms Apriori FP-Growth PrefixSpan - Recommendation Algorithms Collaborative Filtering Recommendation Content-based Recommendation Knowledge-based Recommendation Hybrid Recommendation	13%
Model Evaluation and Optimization	- Prerequisites for Model Evaluation and Optimization - Optimization Models Overview Convex Optimization Loss Functions Types of Optimization Models Regularization Hyperparameter Tuning Tools - Model Evaluation and Selection Model Evaluation Overview Dataset Division Regression Model Evaluation Classification Model Evaluation Clustering Model Evaluation Association Model Evaluation	8%
Python Data Mining Case Analysis	- Data Reading Data Reading Metrics Collection - Feature Understanding and Analysis Single Feature Analysis Bivariate Statistical Analysis Trivariate Statistical Analysis Multivariate Statistical Analysis - Data Preprocessing Data Cleansing Feature Scaling Feature Data and Label Preparation Feature Filtering - Modeling Dataset Splitting Comparison of Modeling Algorithms Solution Improvements	0%
PySpark MLlib	- PySpark MLlib Basics - Basic Statistical Analysis of PySpark MLlib - Feature Extraction and Transformation of PySpark MLlib - Classification and Regression of PySpark MLlib - Clustering and Dimensionality Reduction of PySpark MLlib - Association Rules and Recommendation Algorithms of PySpark MLlib	10%
Huawei Big Data Platform MRS	- MRS Cloud-Native Data Lake Baseline Solution Introduction to the Lakehouse Architecture Offline Data Lake Real-Time Data Lake Logical Data Lake - Components CDL Hudi HetuEngine	15%
Huawei DataArts Studio	- Data Governance Methodology Definition Benefits Challenges Framework Strategy Organization - DataArts Studio Background, Definitions, and Functions Application Scenarios and Case Study	10%

To ensure success in Huawei HCIE-Big Data-Data Mining certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Huawei Certified ICT Expert-Big Data-Data Mining (H13-731) exam.