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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/intro-analyticthinking-datascience-datamining
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课程名称：分析思维、数据科学与数据挖掘入门 课程概述：欢迎参加《分析思维、数据科学与数据挖掘入门》课程。在本课程中，我们将开始探索数据科学的领域和职业，重点关注在处理数据时所需的技能和伦理考虑。我们将回顾数据科学可以解决的商业问题类型，并讨论CRISP-DM过程在数据挖掘中的应用。还会简要介绍描述性、预测性和规范性分析。课程最后，我们将进行一个探索性活动，以了解数据科学工具包中可能包含的工具和资源。 课程大纲： 1. **数据科学：领域与职业** - 本模块将回顾数据科学的领域，探索小数据和大数据的概念。我们还将调查成功数据科学家的技能，并讨论他们未来可能面临的商业问题。 2. **数据科学在商业中的应用** - 本模块将深入分析数据科学在商业环境中的应用，并讨论在处理数据时需要考虑的伦理问题。 3. **数据挖掘与数据分析概述** - 本模块将首先解释CRISP-DM，这是一种跨行业的数据挖掘标准过程。我们还将介绍描述性、预测性和规范性分析的基本概念。 4. **使用数据科学解决问题** - 在本课程的最后一个模块中，我们将探讨数据科学解决方案的现实应用，并仔细查看您可能在数据科学工具包中遇到的各种工具和程序。
Name:Data Science: The Field and Profession
Description:Welcome to Module 1, Data Science: The Field and Profession. In this module, we will review data science as a field and explore the concepts of small and big data. We will also survey the skills of successful data scientists and discuss the types of business problems data scientists might be asked to solve in the near future.
Name:Data Science in Business
Description:Welcome to Module 2, Data Science in Business. In this module, we will take a closer look at the applications of data science in a business environment and discuss ethical considerations to keep in mind when working with data.
Name:Data Mining and an Overview of Data Analytics
Description:Welcome to Module 3, Data Mining and an Overview of Data Analytics. In this module we will begin with an explanation of CRISP-DM, a cross-industry standard process for data mining. We will also provide an introduction to descriptive, predictive and prescriptive analytics.
Name:Solving Problems with Data Science
Description:Welcome to Module 4, Solving Problems with Data Science. In this last module of the course we will explore some real-world applications of data science solutions and take a closer look at the types of tools and programs you might expect to see in a data science toolkit.
Welcome to Introduction to Analytic Thinking, Data Science, and Data Mining. In this course, we will begin with an exploration of the field and profession of data science with a focus on the skills and ethical considerations required when working with data. We will review the types of business problems data science can solve and discuss the application of the CRISP-DM process to data mining efforts. A brief overview of Descriptive, Predictive, and Prescriptive Analytics will be provided, and we will conclude the course with an exploratory activity to learn more about the tools and resources you might find in a data science toolkit.
