R 商業分析課程 Business Analytics
The Analytic Edge (MIT)
Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.
Strategic Analytics (ESSEC & Accenture)
This specialization is designed for students, business analysts, and data scientists who want to apply statistical knowledge and techniques to business contexts.
Build Intelligent Applications (UW)
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
社群媒體分析 Social Media Analytics
Social Media Data Analytics (Rutgers, U. NJ.)
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.
Social Media Marketing (Northwestern)
In today’s marketplace, organizations need effective, profitable social marketing strategies. In this Specialization, you’ll learn to match markets to social strategies to profitably grow your business. You’ll use social media tools and platforms to design, manage, and optimize social campaigns to promote growth and position your brand in the global digital marketplace, and you’ll develop targeted content to spark dialogue with various social communities.
Digital Marketing (UI. Urbana-Champaign)
This Specialization explores several aspects of the new digital marketing environment, including topics such as digital marketing analytics, search engine optimization, social media marketing, and 3D Printing.
Social Media Data Analytics (Rutgers, U. NJ.)
Gamification is the application of game elements and digital game design techniques to non-game problems, such as business and social impact challenges. This course will teach you the mechanisms of gamification, why it has such tremendous potential, and how to use it effectively. For additional information on the concepts described in the course, you can purchase Professor Werbach’s book For the Win: How Game Thinking Can Revolutionize Your Business in print or ebook format in several languages.
Introduction to Communication Science (U. Amsterdam)
have knowledge of the history and development of communication science
have knowledge of the dominant theoretical approaches within communication science
have knowledge and understanding of the most important models and concepts in this field.
進階資料分析 Advanced Data Analysis
Launch Your Career in Data Science (Johns Hopkins)
This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
社會網絡分析 Social Network Analytics
Model Thinking (U. Michigan)
The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology.
People and Networks (北京大學)
Learn to analyze and reason about problems in social sciences with computational thinking, appreciate interactions between computing and social sciences, as well as gain deeper understanding of some common phenomena in life and society.
大數據分析 Big Data Analysis
Data Mining (UI. Urbana-Champaign)
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Master Recommender Systems (U Minnesota)
This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
Machine Learning (Stanford)
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).