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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-science-for-business-innovation
课程评论：没有评论
课程名称：商业创新中的数据科学 课程概述： 《商业创新中的数据科学》纳米课程概述了高管和中层管理人员所需的数据科学核心技能，以推动数据驱动的创新。该课程解释了数据科学的定义及其被广泛关注的原因。 学习内容： * 数据科学能够创造的价值 * 数据科学可以解决的主要问题类别 * 描述性、预测性和规范性分析之间的区别 * 机器学习与人工智能的角色 从更技术的角度，课程涵盖了监督学习、无监督学习和半监督学习方法，并解释了通过分类、聚类和回归技术可以获得的成果。讨论了 NoSQL 数据模型和技术的作用，以及可扩展的云计算平台的影响与作用。所有主题均通过基于实例的讲座覆盖，讨论用例、成功故事和现实示例。 完成该纳米课程后，如果希望进一步深化数据科学知识，可以参加《商业创新中的数据科学》现场课程 [链接](https://professionalschool.eitdigital.eu/data-science-for-business-innovation)。 课程大纲： 1. **数据驱动商业概述** 该模块介绍课程，提供主题的基本概述。呈现与数据科学及大数据相关的重要概念，并展望如何在实际环境中利用它们来提升商业价值。 2. **术语及基础概念** 在此模块中，您将学习机器学习和数据科学的基础概念。通过讨论Netflix这一完全数据驱动的公司的成功故事，理解这些技术如何为组织带来商业价值，并了解机器学习与编程之间的不同。 3. **商业数据科学方法** 本模块将帮助您理解数据分析的基本方法（如线性回归、朴素贝叶斯、决策树、聚类与逻辑回归）的概念与直觉。所有方法均以典型商业应用为起点，通过简单示例的指导解释进行直观讲解。 4. **挑战与总结** 该模块总结了迄今为止学习的概念，并介绍了一系列数据驱动策略中数据精明的管理者必须考虑的挑战与风险。
Name:Introduction to Data-driven Business
Description:This module introduces the course and offers some basic overview of the topics. It presents the crucial concepts related to data science and big data and provides an outlook on how to use them in real world settings for increasing business value.
Name:Terminology and Foundational Concepts
Description:In this module, you will learn the foundational concepts of machine learning and data science. You will understand how these techniques can be useful in terms of increased business value for organizations, thanks to the discussion of a very well known success story, namely Netflix, which can be deemed as a completely data-driven business. You will also understand how machine learning is different from programming.
Name:Data Science Methods for Business
Description:In this module, you will learn the concepts and intuitions about the basic approaches for data analysis, including linear regression, naive Bayes, decision trees, clustering, and logistic regression. All the methods are presented starting from typical business uses and are covered in an intuitive way through a guided explanation of how the approach works on simple examples.
Name:Challenges and Conclusions
Description:This module summarizes the concepts learned so far and introduces a set of challenges and risks that data-savvy managers must take into account when deciding for a data-driven strategy.
The Data Science for Business Innovation nano-course is a compendium of the must-have expertise in data science for executives and middle-management to foster data-driven innovation. The course explains what Data Science is and why it is so hyped. You will learn: * the value that Data Science can create * the main classes of problems that Data Science can solve * the difference is between descriptive, predictive, and prescriptive analytics * the roles of machine learning and artificial intelligence. From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories, and realistic examples. Following this nano-course, if you wish to further deepen your data science knowledge, you can attend the Data Science for Business Innovation live course https://professionalschool.eitdigital.eu/data-science-for-business-innovation
