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.
Este curso faz parte do Programa de cursos integrados Text Marketing Analytics
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Programa de cursos integrados Text Marketing AnalyticsUniversidade do Colorado em BoulderInformações sobre o curso
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Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
NÃvel intermediário
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Aprox. 10 horas para completar
Inglês
Legendas: Inglês
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Experimente o Coursera for BusinessO que você vai aprender
Describe the concept of network analysis and related terminology
Apply network analysis to marketing data via a peer-graded project
Visualize a network based on centrality and other statistics via homework
Extract marketing insights from a network via a peer-graded project
Habilidades que você terá
- Python Programming
- Network Analysis
- Text Datasets
- Marketing Analytics
- Social Network
Prazos flexÃveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 3 no
NÃvel intermediário
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Aprox. 10 horas para completar
Inglês
Legendas: Inglês
Sua empresa se beneficiaria do treinamento dos funcionários em habilidades sob demanda?
Experimente o Coursera for Business
oferecido por
Universidade do Colorado em Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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Este curso é parte da graduação 100% on-line Master of Science in Data Science da Universidade do Colorado em Boulder.
Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.
Programa - O que você aprenderá com este curso
1 hora para concluir
Network Analysis Introduction and Terminology
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.
1 hora para concluir
2 vÃdeos (Total 32 mÃn.), 3 leituras
1 hora para concluir
Network Analysis Data Structures and Calculations
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.
1 hora para concluir
2 vÃdeos (Total 20 mÃn.), 1 leitura, 1 teste
4 horas para concluir
Preparing and Visualizing Social Networks
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.
4 horas para concluir
1 vÃdeo (Total 17 mÃn.), 2 leituras, 2 testes
4 horas para concluir
Preparing and Visualizing Semantic Networks
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.
4 horas para concluir
1 vÃdeo (Total 12 mÃn.), 2 leituras, 2 testes
Sobre Programa de cursos integrados Text Marketing Analytics
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.
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