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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unlocking-business-value-with-ai-driven-data-analytics/
课程评论：没有评论
**课程名称**: 通过 AI 驱动的数据分析释放商业价值 **课程概述**: 本课程是一门面向初学者的入门课程，旨在为有志于成为数据科学家的学员介绍数据分析和机器学习的基础知识。这是一个小型的速成课程，为深入学习数据科学和机器学习概念奠定基础。本课程全面介绍了数据分析和机器学习，涵盖了基本概念、技术和现实世界场景。学员将学习如何收集、清洗、分析和可视化数据，以及如何构建和评估基础的机器学习模型。 **课程目标**: * 理解数据分析生命周期的所有 6 个阶段的基本概念以及每个阶段的步骤或行动要点。 * 理解数据发现及其在此阶段的行动要点。 * 理解数据准备的步骤和关键方面。 * 理解模型规划和模型构建的关键方面以及遵循的步骤。 * 区分不同类型的业务问题以及适合解决这些问题的模型。 * 了解不同行业的客户流失预测模型选择。 * 发现现实世界场景以及基于问题类型进行模型选择。 * 了解数据分析各阶段使用的工具和技术。 **适合人群**: * 没有数据分析或机器学习经验的初学者。 * 希望提升数据驱动决策能力的在校学生和职场人士。 * 对人工智能和机器学习应用感兴趣的业务分析师、IT 专业人士和爱好者。 **先修知识**: * 具备计算机和数学基础知识（无需编程背景）。 **学习大纲**: 无
This beginner-friendly course is designed to introduce aspiring data scientists to the fundamentals of data analytics and machine learning. This small crash course will set the background if you want to learn the data science and machine learning concepts in detail. This course provides a comprehensive introduction to data analytics and machine learning, covering fundamental concepts, techniques, and real-world scenarios. Participants will learn how to collect, clean, analyze, and visualize data, as well as build and evaluate basic machine learning models.By the end of the course, students will be able to: 1. Understand the fundamentals of all 6 stages in Data Analytics Lifecycle and the steps or action points in each stage. 2. Understand Data Discovery and action items in this phase. 3.Understand Data preparation steps and key aspects. 4. Understand Model Planning and Model building key aspects and steps to follow 5. Categorize between business problem types and the suitable model for the problem. 6. Churn Prediction Model Selection For different Industries 7. Discover real-world scenarios and Model selection based on problem type 8. Tools and Techniques used in each phase of data analyticsWho Should Attend? 1. Beginners with no prior experience in data analytics or machine learning. 2. Students and professionals looking to upskill in data-driven decision-making.3. Business analysts, IT professionals, and enthusiasts interested in AI and ML applications.Prerequisites: 1. Basic understanding of computers and mathematics (No programming background required).
