Degree Requirement
The general requirements are the same as those for the master’s degree in Statistics. The elective courses consist of three fields: statistics, information and miscellany. All students in Data Science program are required to take 3 credit hours form the field of statistics, 6 credit hours from the field of information, and 9 credit hours from the field of miscellany.
Course Information
Required Courses
M2525 Statistical Analysis Methods (3):To train graduate students with the experience of processing real data sets and the ability of using statistical methodologies for practical applications.
M2368 R Programming (3): The course is designed to introduce the R statistical software. The goal is to provide students with programming skills on data manipulations, explorations, and graphical presentations and summaries. The course coverage will also emphasize some popular techniques and methods for statistical concepts and data analysis using R. If time permitted, advanced programming in practical applications will be partly covered as well.
E0644 Database (3): The database is an organized collection of data stored and accessed electronically from a computer system. This course provides an overview of the current database management systems(DBMS) and SQL. The goals are 1) to get students familiar with how to use a relational database system to solve real problems; 2) how to use noSQL databases.
T0095 Seminar I (1/1): The aim of this course is to help graduate students to understand the recent developments and results of statistical research in different areas. This course provides opportunities for students to practice the skills of oral presentation. A few invited talks are also given by some scholars in this semester. With the process of reporting and questioning, it is possible for the students to improve their skills in briefing. The invited talks can also increase the statistical knowledge of students. Furthermore, it intends to improve the research ability and quality of students.
T0096 Seminar II (1/1): The aim of this course is to help graduate students to understand the recent developments and results of statistical and data science research in different areas. This course provides opportunities for students to practice the skills of oral presentation. A few invited talks are also given by some scholars in this semester. With the process of reporting and questioning, it is possible for the students to improve their skills in briefing. The invited talks can also increase the statistical knowledge of students. Furthermore, it intends to improve the research ability and quality of students.
M0800 Business Ethics (1): What other issues dose a business should attend to in addition to its “bottom line”? When a business operates globally, should it modify its ethical standard based on local laws and regulations? This course covers such questions and hopes to provide students with some generally accepted guidelines. Students will not only read about relevant theories in business ethics but also discuss various business ethics issues.
Elective Courses
Statistics
M0964 Applied Multivariate Analysis (3): This course introduces fundamental concepts of analyzing multivariate data, including basic exploratory and inferential statistics, principal components analysis, factor analysis, canonical correlation analysis, classification and cluster analysis, etc. In addition to basic ideas and theoretical results, practical applications are also illustrated by examples.
M2535 Data Modeling and Applications (3): This course looks to introduce the fundamentals and concepts of statistical modeling, methods, or procedures in practical data problems. Data application on how to choose, conduct, and interpret appropriate statistical analyses will be particularly emphasized. R statistical software will be used throughout the course for data analysis.
Time Series Analysis (3)
Information
E0650 Data Structures (3): This course offers a study of data structures, including stacks, recursion, queues, lists, trees, graphs, sorting and searching. Students taken this course can understand the data structure and designing logic in the developed programs or software.
M2345 Java Programming (3): Learn the basic knowledge and skills required for object-oriented programming; including the basic structure, syntax, and object-oriented concepts of the Java programming language. In addition, this course will also introduce web programming, provide web services through back-end program by Java Spring Boot.
M2431 Python Programming (3): Learn the basic knowledge and skills required for Python; including the basic structure, syntax. In addition, this course will also introduce web scraping and related libraries for data science with Python.
M2407 Parallel Computing (3)
Could Computing (3)
Miscellany
E3670 Deep Learning (3)：Introduce deep learning concepts, methods and tools. You will build your understanding through intuitive explanations and practical examples. The contents include convolutional neural networks (CNN), recurrent neural networks (RNN) and generative Adversarial Network (GAN).
M0423 Machine Learning (3):Introduce machine learning concepts, methods and tools. The contents include linear regression, classification, resampling methods, models selection, regularization, GAM models, tree-based methods and support vector machine.
M0947 Data Mining (3): This course offers a study of the techniques of data mining (DM) and knowledge discovery in databases (KDD) for Big Data. The topic includes the concept of DM, the function of DM, the step of DM, association rules, decision tree, clustering, classification, exploratory data analysis (EDA), artificial neural networks, etc. In addition, this course also introduces MLlib which is Apache Spark’s machine learning module for Big Data.
M1646 Data Analysis and Prediction Model (2):This course will introduce how to prepare data before building models and introduce array data skill. In addition, we will use data to build model. Finally, I also introduce SQL. This language is more popular in database.
M2474 Data Visualization (3): Data visualization as a problem-solving and knowledge discovery tool has become even more important as we enter the Big Data era. This course offers a study of data visualizations, including visual perception, visual cognition, data preprocessing, time series data visualization, spatial data visualization, network data visualization, etc. In addition to introducing the basic concepts of visualization, the course will also introduce visualization libraries and software such as D3.js, Data Desk, GAP, Orange3, Gephi, etc.
M2555 Quantitative Finance (3)：This course focuses on the blending of the financial and statistical theories to the financial applications using Python. The empirical application regarding the optimal assets allocation is demonstrated through ands-on project-based learning .
M2610 Applications of Data Science (3):Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. This course introduces the construction method of data analysis system, including front-end web pages, back-end programs and database design.
M2611 Machine Learning and Biostatistics Applied to Health Data Science (3):Combining pre-processing methods, biostatistical methods, and machine learning methods to build disease prediction models for health big databases.
M2612 Marketing Data Science (3):Applications of data science tools to marketing decision-making. This course covers two topics: Financial marketing and commodity marketing.
M2613 E-Commerce and Internet Marketing Practice (3):The teaching goal of this course is to incubate ecommerce talents by incorporating collaborative teaching practice and talents cultivation of B2B cross-border ecommerce global skill through uniting knowing and doing as well as competition.
M2346 Special Topics on Big Data Analytics from Social Media (3)
M2482 High-Dimensional Graphics Technioues (3)
Text Mining and Social Media Analysis (3)
Recommender Systems (3)
Special Topics in Data Science Applications (3)