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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/network-analysis-for-marketing-analytics
课程评论：没有评论
课程名称：网络分析与营销分析 概述：网络分析是一种长期以来用于理解词语和行为者在更广泛网络中关系的方法。本课程着重于网络分析在营销数据中的应用，特别是文本数据集和社交网络。学习者将通过概念概述和实际数据集的Python实践操作，深入了解网络分析。课程最后将进行一个重要项目。 本课程可以作为科罗拉多大学波尔得分校（CU Boulder）数据科学硕士（MS-DS）学位的一部分进行学术学分学习。MS-DS是一个跨学科的学位，集合了应用数学、计算机科学、信息科学等多个部门的教师。该课程采用基于表现的录取方式，不需要申请流程，适合拥有广泛本科教育和/或计算机科学、信息科学、数学及统计等专业经验的人士。更多关于MS-DS项目的信息，请访问：https://www.coursera.org/degrees/master-of-science-data-science-boulder。 课程大纲： 第一部分：网络分析简介与术语 描述：在本模块中，我们将学习网络分析的关键概念和术语，包括语义网络和社交网络，并调查营销中的常见网络分析。 第二部分：网络分析的数据结构与计算 描述：在本模块中，我们将学习如何准备网络以及表示网络的常见数据格式。我们将学习不同网络计算之间的差异，以及网络的可视化呈现方式。 第三部分：社交网络的准备与可视化 描述：在本模块中，我们将学习如何解析推文的JSON格式，提取提及和文本，将连接加载到边列表中，并在Google Colab中可视化网络。 第四部分：语义网络的准备与可视化 描述：在本模块中，我们将学习如何解析推文的JSON格式，将文本处理为特征，将连接加载到边列表中，并在Google Colab中可视化网络。
Part: 1
Title:Network Analysis Introduction and Terminology
Description:In this module, we will learn the key concepts in network analysis and the key terminology, including semantic and social networks. We will also survey common network analyses in marketing.
Part: 2
Title:Network Analysis Data Structures and Calculations
Description:In this module, we will learn how networks are prepared and the common data formats that represent networks. We will learn the differences between different network calculations and how networks are presented visually.
Part: 3
Title:Preparing and Visualizing Social Networks
Description:In this module, we will learn how to parse tweet JSON, extract mentions and text, load connections into edge lists, and visualize the network in Google Colab.
Part: 4
Title:Preparing and Visualizing Semantic Networks
Description:In this module, we will learn how to parse tweet JSON, process text into features, load connections into edge lists, and visualize the network in Google Colab.
Network analysis is a long-standing methodology used to understand the relationships between words and actors in the broader networks in which they exist. This course covers network analysis as it pertains to marketing data, specifically text datasets and social networks. Learners walk through a conceptual overview of network analysis and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
