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所在平台: Udemy |
课程主页: https://www.udemy.com/course/idsb-introduction-to-data-science-for-beginners/
课程评论：没有评论
课程名称：IDSB：初学者的数据科学入门 课程概述： 这个课程旨在帮助职场人士，无论是初学者、经验丰富的高管还是数据科学爱好者，提升他们在数据科学领域的能力，为职业生涯或组织做出显著贡献。通过本课程，学员将深入理解核心的数据科学概念，包括数据分析、统计学和机器学习，从而有效解决现实问题。 学习目标： - 理解数据科学的基本概念，学习如何应对数据处理、可视化和预测方面的实际挑战。 - 提高应用理论知识的能力，掌握数据挖掘、回归分析和聚类等关键工具和技术，以进行深刻的分析。 - 通过实际案例研究、框架和互动练习，打下职业发展的坚实基础。 课程框架： - 互动视频讲座、案例研究、评估、可下载资源和互动练习，涵盖数据收集、准备和分析的关键概念。 - 了解数据科学家在解决现实问题中的角色和责任，掌握数据准确性、可靠性及核心统计概念的重要性。 - 学习数据挖掘技术，有效沟通发现，并了解机器学习基础，用于预测建模。 沟通技能： - 学习清晰有效的沟通在数据科学中的重要性，包括口头沟通、积极倾听和如何向非技术利益相关者有效传达发现。 - 理解如何专业地编写报告、邮件及文档，并探索与数据科学团队的同事建立良好关系的非语言沟通技巧。 课程内容： 第一部分：数据科学基础 - 数据科学概述、主要应用及跨学科领域简介。 - 统计学：采样、描述性统计、假设检验、回归分析、预测及方差分析。 - 概率与分布：概率的基本概念及其数学规则。 第二部分：数据挖掘与机器学习 - 数据挖掘的基本概念、数据结构及其主要应用。 - 机器学习技术及其他方法。 - 数据科学工具及其功能，包括商业智能工具。 在课程的最后一部分，将学习如何组织和管理数据工作流程，设定数据分析目标，并有效管理项目。学员将获得对数据驱动项目的规划和组织的深入见解，包括数据管道的结构和团队协作。 此外，课程提供案例研究、模板、工作表、阅读材料、测验、自我评估以及动手作业，以增强学员对数据科学概念的理解，并提供实时数据分析项目的机会，以将理论知识应用于实际问题，确保学员能够有效解决现实数据问题。
Description· Take the next step in your career! Whether you're an aspiring professional, experienced executive, or budding Data Science enthusiast, this course is your gateway to sharpening your Data Science capabilities and making a significant impact in your career or organization.· With this course as your guide, you learn how to: Understand core Data Science concepts, including data analysis, statistics, and machine learning, to solve real-world problems effectively.· Enhance your ability to apply theoretical knowledge to practical challenges in data handling, visualization, and prediction.· Gain proficiency in key tools and techniques like data mining, regression, and clustering for insightful analysis.· Develop a solid foundation for career advancement, with practical case studies, frameworks, and interactive exercises to hone your expertise.The Frameworks of the Course• Engaging video lectures, case studies, assessments, downloadable resources, and interactive exercises. This course is created to introduce you to Data Science, covering key concepts such as data collection, preparation, and analysis. You will learn about the role and responsibilities of a Data Scientist, and the importance of Data Science in solving real-world problems. Key topics will include understanding data accuracy, reliability, and core statistical concepts to interpret data and make informed decisions. You will also explore data mining techniques to effectively communicate findings, and dive into the basics of machine learning for predictive modeling.• Communication Skills: You will learn the importance of clear and effective communication in Data Science. Topics will cover verbal communication for presenting data insights, active listening techniques for collaborative problem-solving, and how to communicate findings effectively to non-technical stakeholders. You will also understand how to structure written reports, emails, and documentation in a professional way. Additionally, the course will cover non-verbal communication such as body language and how to build rapport with colleagues in Data Science teams. You will also explore office technology and tools relevant to Data Science, including software used for data visualization and analysis, as well as basic troubleshooting for Data Science-related tools.The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on assignments to nurture and enhance your understanding of Data Science concepts. You will also have access to real-world data analysis projects where you can apply theoretical knowledge to practical issues, learning how to work with real datasets and make data-driven decisions.In the first part of the course, you'll learn the fundamentals of Data Science, including an introduction to its key concepts such as data collection, data preparation, and the importance of data accuracy and reliability. You'll explore the role and responsibilities of a Data Scientist and understand the critical skills required to work effectively in this field. This section will also cover core statistical concepts that form the foundation of data analysis and help in making informed decisions based on data.In the middle part of the course, you'll develop your understanding of data analysis tools and techniques. You'll gain hands-on experience with data visualization to present insights clearly, and learn the basics of machine learning for building predictive models. This section will also explore how to communicate data insights effectively, covering skills such as verbal communication, active listening, and presenting data findings to non-technical audiences. The course will also dive into office technologies relevant to Data Science, including essential software tools and advanced features of data analysis platforms.In the final part of the course, you'll develop your skills in organizing and managing data workflows. You will learn how to prioritize data-related tasks, set data analysis goals, and use tools to track progress and manage projects effectively. You'll also gain insights into how to plan and organize data-driven projects, including understanding how to structure data pipelines and coordinate team efforts. Additionally, you'll receive continuous support with guaranteed responses to all your queries within 48 hours, ensuring that you can apply what you've learned to real-world data problems effectively.Course Content:Part 1Introduction and Study PlanIntroduction, Study Plan and Structure of the CourseModule 1: About to Data ScienceLesson 1: Overview of Data ScienceLesson 2: Major Application of Data ScienceLesson 3: Brief about Interdisciplinary FieldModule 2: StatisticsLesson 1: SamplingLesson 2: Descriptive StatisticsLesson 3: Hypothesis TestingLesson 4: RegressionLesson 5: ForecastingLesson 6: ANOVAModule 3: Probability and DistributionLesson 1: ProbabilityLesson 2: Mathematical Rules in ProbabilityLesson 3: Probability DistributionPart 2Module 4: Data MiningLesson 1: About Data MiningLesson 2: Data StructureLesson 3: Major ApplicationModule 5: Machine LearningLesson 1: Machine Learning TechniquesLesson 2: Other MethodsModule 6: Tools and FunctionLesson 1: Business Intelligent ToolsAssignment: Data Science
