Master Program of Applied Statistics

I. Educational Objectives

1. Graduates should have rigorous style of study, innovation spirit of science, solid theoretical basis, and broad specialized knowledge in the field of Applied statistics, and certain ability to undertake independent scientific research.

2.They should have a deep understanding of research directions, and fully grasp the research and development trend of modern applied statistics. They should be able to effectively carry out the research of statistical theory and application, and have the stronger ability of data acquisition, analysis, processing, statistical modeling and software development, and statistical computing and inference.

3. They can skillfully use a foreign language, to read the materials in the foreign language in this field, and have certain ability to communicate and write research paper in a foreign language.

4. They should competent for scientific research, teaching, development, consulting, management etc, in the fields related to Applied statistics.

II. Main Research Areas

1. Statistical Modeling and Data Mining

2. Information Statistics

3. Economic and Financial Statistical Analysis

4. Biological and Medical Statistical Analysis

5. Social and Management Statistical Analysis

6. Statistical Computing and Software

III. The Length of Schooling and Training Mode

2 years Full-time non-directional/sponsored training program.

IV. Credit Requirement and Allocation

Total Credits

≥38 credits

Courses

≥22 credits

General Foundation Courses for Degree≥6 credits

Specialized courses for Degree≥12 credits

For those who lack Specialized basic Courses, 2-3 Specialized basic Courses are required, and are counted as Non-Degree Courses

Optional courses, Non-Degree Courses

Practice

≥4 credits

Internshipandpracticaltraining,4 credits(1 Credit per month)

Academic Activities

≥12 credits

Literature reading and thesis proposal, 1 credit

progress report, 1 credit

Academic thesis, 10 credits

V. Curriculum Provision and Credit Allocation

Classification

Courses

Course

Code

Course Name

Credit

Hours

Credits

Semester

Teaching units

Notes

Degree

Courses

General

Foundation

Courses

408.601

Theory and Practice of Scientific Socialism with Characteristics

24

1

1

Schoolof Humanities


408.602

Natural Dialectics

32

2

1

Schoolof Humanities


411.500

First Foreign

Language(English)

32

2

1

School of Foreign Languages



Humanistic curriculum


1




Required Course

011.545

Multivariate statistical analysis

64

4

1

Schoolof Mathematics

Required

011.546

Statistical method and analysis of data modeling

64

4

2

Schoolof Mathematics

Required

Electives

011.536

Random process

64

4

1

Schoolof Mathematics


011.527

Timeseriesanalysis

64

4

1

Schoolof Mathematics


011.550

Bayesian Statistics

64

4

1

Schoolof Mathematics


011.562

Financial analysis and risk management

64

4

2

Schoolof Mathematics


011.560

Statistical simulation and statistics software

64

4

2

Schoolof Mathematics



Other related courses






Non-

Degree

Courses

Supplemen-tary


Probabilitytheory







Mathematical statistics






VI. Internship and Practical Training

Establishing practice base, to strengthen practice training.

Practical Training internship, including reports: each student should submit his/her internship plan, and go to some enterprise for practice, then write a final practice report based on his/her practice activity. He/She will earn the required internship credits after he/she passes the evaluation of the head of internship unit(1 credit per month).

VII. Degree Thesis

Meet the relevant requirements of the university.

VIII. Degree Awarding

Students who earn required credits and meet the relevant requirements are allowed to graduate, and issued graduation certificate.

Students who meet graduation requirements and pass his/her thesis defense will be awarded the degree of the Master of Applied Statistics by Huazhong University of Science and Technology, after his/her thesis has been reviewed by the academic degrees committee of the university.



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