Lecture: Business Network Analytics and Applications

Course Content

In this lecture, we will introduce the basic concepts of networks, network modeling and analytics techniques, as well network analysis and visualization tools. This course will cover topics including network theory, social network analysis, network visualizations, business intelligence, relational data mining, recommendation systems, financial and marketing networks. This course will help students to have a better understanding of network theory and analytical techniques, and enable them to conduct network data collection, processing and analysis through empirical exercises and homework assignments.

Teaching Language: the lectures and tutorial will all be conducted in English. All homework assignments and project reports are also required to be finished in English.

The students are expected to:

• Understand the basics about network science and its applications in Economics, Finance and Marketing;
• Collect and process network (relational) data from various sources (e.g., online communities) through programming and database management software such as MySQL;
• Conduct network modeling and analysis using tools like R and NetDraw.

1. For each lecture and reading session, students must read the reading materials before class. During class, we will go over the lecutre slides and. Instead, lectures will be interactive, illustrating the concepts from the lecture notes (via experiments, demos, etc.), and students are expected to participate during class discussions.
2. The lecture will be given in 4  days: 12/09/17-15/09/17. We will have 4 tutorial sessions, one final term project. In these sessions, the instructors will teach the students the required skills to finish the assignments.
3. There will be a term project that require a considerable amount of efforts and time to collect, process, and analyze large-scale real-world social networks.
4. For the assignments and the term project. We use the following software: i) MySQL, ii) Java programming, iii) R, and iv) NetDraw..

Tutorial sessions and homework assignments are critical to provide students hands on experiences and skills. Each tutorial and assignment is designed to help you eventually finish the term projects independently. The attendance of each lecture is mandatory.

Each tutorial session is supervised by the teaching assistant (TA). He will help you to get familiar with the necessary software, explain the protocal for conducting the assignments, and answer your questions.

1. This course will introduce the theoritical concepts of network sciences and network-based modeling and analytical techniques in the lectures.
2. In the tutorial session, students will also learn to use a set of network visualization and analysis tools to model and analyze real-world network/relational dataset. 
3. There is a term project of this course. The project will consist of modeling anad analysis of given real-world dataset. The outcome of the project is a comprehensive report which consists of 1) processed network datasets, 2) network visualizations, and 3) analysis results and interpretations.The details of the projects will be annouced during the semester.
4. In this couse, we will be using the following programs: 1) R (Windows/Mac), 2) NetDraw (Windows), 3) MySQL Database Management Software (Windows/Mac). 

No prior knowledge is required. Students need to be able to program in Java to solve the practical problems such as using API to retrieve data. We will provide source code that students can extend, most likely in Java. Knowedge in SQL language will be helpful too.

Recommended for all BSc and MSc students with an interest in topics at business network analysis and application.

The grading breakdown will be:

1. One final project. (90%)
2. Active participation and interaction during the lectures and tutorials. (10%)

The projects include hands-on network data collection, visualization and anlaysis tasks. The detail requirements will be announced later.