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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-in-a-business-context/
课程评论：没有评论
课程名称：商业背景下的数据科学 课程概述：欢迎加入《商业背景下的数据科学》课程！成为一名成功的数据科学家不仅需要提升技术技能，更重要的是要能有效回应商业需求，创造价值。从机器学习模型中提取价值的能力通常是数据科学与其他科学的区别所在。然而，数据科学家在解决日常问题时往往过于专注于学术和机器学习方法，导致团队表现不佳，企业的价值未能及时捕获，数据科学家的职业发展缓慢。本课程将教授如何最大化数据科学模型的价值生成，介绍有效和高效的数据科学家应牢记的一些核心原则，并基于这些原则提供一个可应用于日常工作的框架。最后，通过案例研究展示如何在实践中运用该框架。 课程学习目标： - 了解数据科学领域当前的发展阶段和数据科学家的角色 - 定义商业背景下有效数据科学家的特征 - 应用框架指导以商业和价值为导向的数据科学项目开发 - 建立机器学习指标与商业指标之间的联系 - 提高作为数据科学家的生产力和价值生成能力 适合人群： - 初级和经验较少的数据科学家能够迅速学习如何在商业环境中有效工作，提升对行业的影响力，显著增加成功概率和生产力 - 有志于成为数据科学家的人士将了解在商业背景下数据科学家所需的技能，为面试做好更充分的准备 - 中高端数据科学家将学习在开发阶段采用新的视角，从而显著提升其生产力 - 数据科学经理可以找到启发和材料，帮助团队以统一的方式开展工作 课程要求： 第一、二、三部分：无特别要求！只需有提升自己作为数据科学家的愿望； 第四、五部分：需对Python、Jupyter notebook及简单的机器学习概念（如线性回归、决策树、训练/测试分割、交叉验证）有基础了解。
Welcome to the Data Science in a Business Context course!Becoming an accomplished and successful Data Scientist today not only requires one to sharpen their technical skills, but also-and more importantly-to be able to respond to a business' needs in an effective, value-generating way. Being able to extract value from a Machine Learning model is generally what differentiates Data Science from other sciences. Yet Data Scientists focus too little on this point, often adopting an academic, machine learning-oriented approach to solving problems in their daily life. This often results in underperforming Data Science teams, non-captured or belatedly-captured value for the companies they work for, and slow career progression for Data Scientists themselves. In this course I will teach you how to maximise value generation of your Data Science models. I will introduce a few core principles that an effective and productive Data Scientist should keep in mind to perform their job in a value-oriented way, and based on those principle, I will introduce a framework that you can apply in your everyday life when solving Data Science problems in a business context. I will finally show you a case study example to demonstrate how the framework works in practice.What you will learnAfter the course you will be able to:Understand the current stage of the Data Science field and Data Scientist job Define the characteristics of an effective Data Scientist in a business contextApply a framework to guide the development of a Data Science project in a business- and value-oriented wayDerive a link between a machine learning metric and a business metricIncrease your productivity and value generation as a Data ScientistWho is this course for Junior and less experienced Data Scientists will quickly learn how to perform their job in a business context, making the impact with the industry world much smoother, and dramatically increasing their probability of success and their productivityAspiring Data Scientist will understand what is needed from a Data Scientist in a business context, which will prepare them much better to the next interviewsMid-Senior and Senior Data Scientists will learn to adopt a new perspective during the development phase, which can radically improve their productivity levelData Science Mangers can find inspiration and material to have their teams work in a uniform wayRequirementsSection 1, 2, 3: no requirements! Just your desire of becoming a better, more performing Data ScientistSection 4, 5: basic familiarity with Python, Jupyter notebooks and simple Machine Learning concepts (Linear Regression, Decision Trees, train/test split, cross validation)
