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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/social-media-data-analytics
课程评论：没有评论
课程名称：社交媒体数据分析 课程概述： 本课程旨在帮助学习者掌握社交媒体数据分析的基本技能。完成课程后，您将能够： - 利用各种应用程序接口（API）服务，从YouTube、Twitter、Flickr等不同社交媒体来源收集数据。 - 处理收集到的主要是结构化的数据，使用相关性、回归和分类等方法，以获取关于数据源及其生成者的洞察。 - 分析主要为文本评论的非结构化数据，以理解其中表达的情感。 - 使用不同的工具收集、分析和探索社交媒体数据，以支持研究和开发。 学习者故事示例： 以莎比拉为例，她是一名数据分析师，为一家跨国公司提供咨询。她具有网络分析工具和市场数据的工作经验，希望拓展社交媒体领域，利用通过社交媒体渠道获取的大量数据，了解客户、合作伙伴和竞争者对其产品/服务的看法。莎比拉希望构建一种新的数据分析工作流程，将传统的数据处理方法与社交媒体数据的新方法相结合。 需要这些技能的职位示例： - 社交媒体分析师 - 网络分析师 - 数据分析师 - 市场营销与公关专员 最终项目交付物： 本课程将包含一系列小型作业或迷你项目，涉及从各个社交媒体来源收集、分析和呈现数据，运用课堂上学习的技巧。 课程开发者：Dr. Chirag Shah，目前在华盛顿大学任教，曾为罗格斯大学的教职员工。 课程大纲： - **数据分析简介**：介绍社交媒体数据和数据分析的若干概念，探索结构化与非结构化数据的分析方法，并简要讨论数据可视化。 - **收集与提取社交媒体数据**：学习如何使用Python脚本从Twitter和YouTube收集数据，包括开发者账户的创建和API的使用。 - **数据分析、可视化与探索**：专注于对YouTube收集的数据进行统计分析，学习使用R进行更大数据集的分析。 - **案例研究**：通过两个案例研究（均使用Twitter的非结构化数据），进行情感分析和文本挖掘，并总结课程内容和后续学习方向。
Name:Introduction to Data Analytics
Description:In this first unit of the course, several concepts related to social media data and data analytics are introduced. We start by first discussing two kinds of data - structured and unstructured. Then look at how structured data, the primary focus of this course, is analyzed and what one could gain by doing such analysis. Finally, we briefly cover some of the visualizations for exploring and presenting data.Make sure to go through the material for this unit in the sequence it's provided. First, watch the four short videos, then take the practice test, followed by the two quizzes. Finally, read the documents about installation and configuration of Python and R. This is very important - before proceeding to the next units, make sure you have installed necessary tools, and also learned how to install new packages/libraries for them. The course expects students to have programming experience in Python and R.
Name:Collecting and Extracting Social Media Data
Description:In this unit we will see how to collect data from Twitter and YouTube. The unit will start with an introduction to Python programming. Then we will use a Python script, with a little editing, to extract data from Twitter. A similar exercise will then be done with YouTube. In both the cases, we will also see how to create developer accounts and what information to obtain to use the data collection APIs. Once again, make sure to go item-by-item in the order provided. Before beginning this unit, ensure that you have all the right tools (Python, R, Anaconda) ready and configured. The lessons depend on them and also your ability to install required packages.
Name:Data Analysis, Visualization, and Exploration
Description:In this unit, we will focus on analyzing and visualizing the data from various social media services. We will first use the data collected before from YouTube to do various statistics analyses such as correlation and regression. We will then introduce R - a platform for doing statistical analysis. Using R, then we will analyze a much larger dataset obtained from Yelp. Make sure you have covered the material in the previous units before proceeding with this. That means, having all the tools (Anaconda, Python, and R) as well as various packages installed. We will also need new packages this time, so make sure you know how to install them to your Python or R. If needed, please review some basic concepts in statistics - specifically, correlation and regression - before or during working on this unit.
Name:Case Studies
Description:In the final unit of this course, we will work on two case studies - both using Twitter and focusing on unstructured data (in this case, text). The first case study will involve doing sentiment analysis with Python. The second case study will take us through basic text mining application using R. We wrap up the unit with a conclusion of what we did in this course and where to go next for further learning and exploration.
Learner Outcomes: After taking this course, you will be able to: - Utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr. - Process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive insights about the sources and people who generated that data. - Analyze unstructured data - primarily textual comments - for sentiments expressed in them. - Use different tools for collecting, analyzing, and exploring social media data for research and development purposes. Sample Learner Story: Data analyst wanting to leverage social media data. Isabella is a Data Analyst working as a consultant for a multinational corporation. She has experience working with Web analysis tools as well as marketing data. She wants to now expand into social media arena, trying to leverage the vast amounts of data available through various social media channels. Specifically, she wants to see how their clients, partners, and competitors view their products/services and talk about them. She hopes to build a new workflow of data analytics that incorporates traditional data processing using Web and marketing tools, as well as newer methods of using social media data. Sample Job Roles requiring these skills: - Social Media Analyst - Web Analyst - Data Analyst - Marketing and Public Relations Final Project Deliverable/ Artifact: The course will have a series of small assignments or mini-projects that involve data collection, analysis, and presentation involving various social media sources using the techniques learned in the class. The course was developed by Dr. Chirag Shah while he was a faculty member at Rutgers University. He is currently a faculty member at University of Washington.
