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所在平台: Udemy |
课程主页: https://www.udemy.com/course/snowpro-advanced-data-scientist-dsa-c02-practice-exams-2024/
课程评论：没有评论
课程总结：SnowPro高级数据科学家DSA-C02实践考试：2025 本课程旨在帮助您为SnowPro高级数据科学家考试做好准备，重点在于实践能力的提升。课程内容包涵三个完整的实践考试，每个考试都提供详细的解答，确保您对材料有深入的理解。课程主题与最新的2025年考试大纲相一致，涵盖了关键知识点。 ### 课程主题 1. **数据科学概念 (30%)** - 统计分析 (10%): 理解和应用数据分析的统计方法。 - 机器学习 (15%): 探索各种机器学习技术和算法。 - 深度学习 (5%): 深入学习高级深度学习概念。 - 自然语言处理 (5%): 应用NLP技术进行语言理解。 2. **Snowflake数据科学最佳实践 (30%)** - Snowflake数据建模 (10%): 数据建模的最佳实践。 - Snowflake数据准备与转换 (15%): 使用Snowflake高效准备和转换数据。 - Snowflake机器学习和深度学习 (5%): 利用Snowflake进行机器学习和深度学习任务。 - Snowflake数据可视化与故事讲述 (5%): 通过有效的可视化和故事讲述传达洞察。 3. **Snowflake中的数据准备和特征工程 (20%)** - Snowflake数据类型和结构 (7%): 理解Snowflake中的数据类型和结构。 - Snowflake SQL函数在数据准备和特征工程中的应用 (10%): 利用SQL函数进行有效的数据准备。 - Snowflake用户定义函数 (3%): 创建用于特定数据工程任务的自定义函数。 4. **在Snowflake中训练和使用机器学习模型 (20%)** - Snowflake机器学习算法 (7%): 探索Snowflake内的机器学习算法。 - Snowflake模型训练与评估 (10%): 在Snowflake中训练和评估机器学习模型。 - Snowflake模型部署 (3%): 在Snowflake环境中部署模型的策略。 ### 考试概述 - 总题数：55 - 时间：90分钟 ### 提醒 本课程并未与Snowflake官方合作或认可，而是作为一项非官方的准备资源，帮助参与者发展为SnowPro高级数据科学家考试所需的技能和知识。课程内容基于考试大纲，旨在提供现实的练习场景，以提高考试准备度。 ### 学习目标 - 理解并应用统计分析技术获取数据洞察。 - 探索Snowflake内多样的机器学习算法进行预测建模。 - 精通深度学习概念及其在Snowflake中的实际应用。 - 使用Snowflake的最佳实践高效建模、准备和转换数据。 ### 课程要求 - 具备基础数据科学概念和方法的知识。 - 之前有Snowflake工作的经验。
This comprehensive course is designed to prepare you for the SnowPro Advanced Data Scientist Exam. With a focus on hands-on practice, the course provides Three full-length practice exams, each accompanied by detailed explanations to ensure a thorough understanding of the material. The course covers the key topics outlined in the SnowPro Advanced Data Scientist exam blueprint, aligning with the latest 2025 updates.Course Topics Covered:Section 1: Data Science Concepts (30%)Statistical Analysis (10%): Understanding and applying statistical methods for data analysis.Machine Learning (15%): Exploring various machine learning techniques and algorithms.Deep Learning (5%): Delving into advanced deep learning concepts.Natural Language Processing (5%): Applying NLP techniques for language understanding.Section 2: Snowflake Data Science Best Practices (30%)Snowflake Data Modeling for Data Science (10%): Best practices for modeling data in Snowflake.Snowflake Data Preparation and Transformation for Data Science (15%): Efficiently preparing and transforming data using Snowflake.Snowflake Machine Learning and Deep Learning (5%): Leveraging Snowflake for ML and deep learning tasks.Snowflake Data Visualization and Storytelling (5%): Communicating insights through effective visualization and storytelling.Section 3: Data Preparation and Feature Engineering in Snowflake (20%)Snowflake Data Types and Structures for Data Science (7%): Understanding data types and structures in Snowflake.Snowflake SQL Functions for Data Preparation and Feature Engineering (10%): Utilizing SQL functions for effective data preparation.Snowflake User-Defined Functions for Data Preparation and Feature Engineering (3%): Creating custom functions for specific data engineering tasks.Section 4: Training and Using Machine Learning Models in Snowflake (20%)Snowflake Machine Learning Algorithms (7%): Exploring ML algorithms within Snowflake.Snowflake Model Training and Evaluation (10%): Training and evaluating machine learning models in Snowflake.Snowflake Model Deployment (3%): Strategies for deploying models within the Snowflake environment.Exam Overview:Total Questions: 55Time: 90 minutesDisclaimer: This course is not affiliated or endorsed by Snowflake. It is an unofficial preparation resource created to help participants develop the skills and knowledge required to succeed in the SnowPro Advanced Data Scientist Exam. The content is based on the exam blueprint and aims to provide realistic practice scenarios for better exam readiness.What you'll learnUnderstand and apply statistical analysis techniques for data insights.Explore diverse machine learning algorithms within Snowflake for predictive modeling.Master deep learning concepts and their practical implementation in Snowflake.Efficiently model, prepare, and transform data using Snowflake's best practices.Are there any course requirements or prerequisites?Basic knowledge of data science concepts and methodologies.Prior Snowflake Work Experience
