Big Data Statistical Analysis Method
Course No.: SMI1133141 Credit(s):3
Course Description
This course mainly covers the basic idea of big data, basic concepts and processes of data mining, guided learning, random forest, unguided learning, Bayes classification, Causality learning, high-dimensional regression and variable selection, High-dimensional regression coefficient compression, graph model, customer relationship management, social network analysis, natural language model and text mining.
Course Learning Outcomes
The objective of the course is to give students a broad overview of the basic principles and applications of data analytics. Students will also be familiar with the various aspects of data analytics such as exploring, managing, modeling and interpreting data. Students’ learning will also be enhanced by their exposure to real life applications of data analytics in social science research, business analysis and public management.
Relationship to Other Courses
Pre-requisites: Mathematical Modeling, Data Mining, Mathematical Software
Textbook and Reading Lists
Textbook:
Xing Wang, Big Data Analysis: Methods and Applications. China Renmin University Press, 2013.
Suggested reading lists:
Jian He, Stata Comprehensive Experiment of Statistics (1st edition). Dongbei University of Finance & Economics Press, 2014.5.
Johannes Ledolter, Data Mining and Business Analytics with R. Wiley, 2013.
Gareth Jamesetal, An Introduction to Statistical Learning: with Applications in R. Springer, 2013.
Course Assessment
Item | Title | Weighting (%) |
1 | Task in home | 10% |
2 | Test and Questions in class | 10% |
3 | Final exam | 80% |
Course Schedule
Week | Topics | Text |
1-7 | Statistical Analysis Multilevel and Longitudinal Modeling | Chapters 1 |
8-12 | Machine Learning: lSupervised Learning with Regularization lResampling Methods lTree-based Methods, Support Vector Machines lUnsupervised Learning: Clustering, Dimension Reduction | Chapters 2 |
13-17 | Comprehensive Application: lText Mining and Sentiment Analysis lSocial Network Analysis lPolicy Informatics | Chapters 3 |
18 | Review | |
