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所在平台: Coursera专项课程 |
课程主页: https://www.coursera.org/specializations/text-marketing-analytics
课程评论：没有评论
课程名称：文本营销分析 课程概述： 本课程旨在帮助学习者掌握话题建模、文本分类和网络分析的相关概念。学习者将能够使用话题建模来分析大规模非结构化文本数据集，运用网络分析创建网络图、生成网络统计数据并提取定性见解。此外，课程将介绍文本分类及其相关术语（如监督机器学习）。 技能提升： 参与者将获得数据分析、机器学习、文本营销分析、评估营销问题、监督学习过程、分类模型等方面的技能。课程还包含话题模型、Python编程、无监督文本分类和数据结构等内容。 专业介绍： 市场数据复杂且具有多维性，使得分析变得困难。大型非结构化数据集往往太庞大，难以提取定性见解。此外，市场数据集常常是关联性和网络性。此专业通过三种高级方法：文本分类、话题建模和语义网络分析，解决这些问题，深入探索利用Python解决相关问题的计算机科学方法。该专业也可作为CU Boulder数据科学硕士学位的一部分，允许获取学分。 应用学习项目： 学习者将探索文本分类、话题建模和语义网络分析的概念概述，并通过教师指导的教程深入研究实际数据集，同时为每个主要方法进行重大项目的实践。 证书： 完成课程后将获得可分享的证书，课程为100%在线，学习者可以即时开始，按照个人节奏学习，设置和维护灵活的截止日期。 适合人群： 该课程适合初学者，建议具有基础Python技能，包括Python的内置函数、逻辑和数据结构。预计完成时间约为4个月，每周建议学习3小时。 课程链接： 1. [监督文本分类的营销分析](https://www.coursera.org/learn/supervised-text-classification-for-marketing-analytics) 2. [无监督文本分类的营销分析](https://www.coursera.org/learn/unsupervised-text-classification-for-marketing-analytics) 3. [营销分析的网络分析](https://www.coursera.org/learn/network-analysis-for-marketing-analytics) 这样的学习机会将帮助学生在复杂的市场数据分析中获得新技能，并为他们的职业发展提供支持。
Course Link: https://www.coursera.org/learn/supervised-text-classification-for-marketing-analytics
Name:Supervised Text Classification for Marketing Analytics
Description:Offered by University of Colorado Boulder. Marketing data often requires categorization or labeling. In today’s age, marketing data can also ... Enroll for free.
Course Link: https://www.coursera.org/learn/unsupervised-text-classification-for-marketing-analytics
Name:Unsupervised Text Classification for Marketing Analytics
Description:Offered by University of Colorado Boulder. Marketing data is often so big that humans cannot read or analyze a representative sample of it ... Enroll for free.
Course Link: https://www.coursera.org/learn/network-analysis-for-marketing-analytics
Name:Network Analysis for Marketing Analytics
Description:Offered by University of Colorado Boulder. Network analysis is a long-standing methodology used to understand the relationships between ... Enroll for free.
What you will learn
Understand the concepts of topic modeling, text classification, and network analysis
Learn to use topic modeling on large unstructured text datasets
Learn to use network analysis to create network graphs, produce network statistics, and extract qualitative insights
Describe text classification and related terminology (e.g., supervised machine learning)
Skills you will gain
Data Analysis
Machine Learning
text marketing analytics
Assess Marketing Problems
Supervised Learning Process
Supervised Learning
Classification Models
Supervised Learning Outcomes
Topic Model
Python Programming
Unsupervised Text Classification
Data Structure
About this Specialization
Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also often involve relational and connected and involve networks. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization 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.
Applied Learning Project
Learners explore conceptual overviews of text classification, text topic modeling, and semantic network analysis and dive into real-world datasets through instructor-led tutorials. Learners also conduct a major project for each of the three key methods.
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Beginner Level
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Hours to complete
Approximately 4 months to complete
Suggested pace of 3 hours/week
Available languages
English
Subtitles: English
Shareable Certificate
Shareable Certificate
Earn a Certificate upon completion
100% online courses
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Flexible Schedule
Set and maintain flexible deadlines.
Beginner Level
Beginner Level
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Hours to complete
Approximately 4 months to complete
Suggested pace of 3 hours/week
Available languages
English
Subtitles: English
