Undergraduate
The Data Science and Big Data Technology aims to train high-level compound talents with good political quality and moral accomplishment, all around development of moral, intellectual, physical, aesthetics and labour education. Our students have solid mathematical and physical foundation, mastery of basic theories, methods and skills related to data science, comprehensive abilities of big data analysis and processing, great skills of development and application of big data system. Our graduates are competent to the tasks of scientific research, teaching, development and management in related fields.
Ⅱ.Training requirements
The students of Data Science and Big Data Technology are required to systematically study the basic theories and basic knowledge of mathematics, computer science and data science and big data technology, to master basic skills of big data management, analysis and mining, and to have some innovative practice abilities in the field of big data. Graduates of this major should have the following qualities, knowledge and abilities:
1. Have good political quality, moral quality, legal awareness, integrity awareness and team spirit;
2. Have good physical and mental qualities and humanistic qualities, and also have a strong sense of social responsibility and national defense awareness;
3. Have the basic theories and knowledge in the fields of computer science, mathematics and statistics, software engineering etc. that are required for the works of this major;
4. Systematically master the basic theories of big data management, analysis and mining, understand the basic concepts, knowledge structure and typical methods of big data platform, and has some basic skills of big data platform construction and big data analysis;
5. Have the basic theories and methods of artificial intelligence and machine learning, and have innovative abilities of big data analysis on specific problems;
6. Have good knowledge of the design, development, testing theory and method of big data analysis software system, and have some abilities of big data software system design and implementation;
7. Have good abilities of scientific learning methods, have sustainable learning abilities for actively acquiring knowledge, and have the initial abilities of scientific research;
8. Have a good foundation of language expression ability, master a foreign language, have international vision and have the abilities of cross-cultural communication, competition and cooperation.
Ⅲ. The academic system and award degree
Years of schooling: 4
Degree awarded: bachelor of engineering
Ⅳ. The main disciplines
Computer science and technology, Statistics
Ⅴ. The core curriculums
Basis of Statistical Analysis of Big Data, Data Mining, Introduction to Artificial Intelligence, Machine Learning, Big Data Architecture and Model.
Ⅵ. Graduation credit requirements
Graduates of this major should complete 160 credits, including 139 credits for courses and 17.5 credits for practical teaching.
Ⅶ. Achieving Matrix of Training Objectives
According to the requirement of "people-oriented, moral education first, ability first and all-round development", theoretical teaching, practical teaching and second classroom teaching are used to effectively support the professional training requirements, enhance the pertinence of various training links, and realize the goal of "descriptive, measurable, differentiable and evaluable" talent training. Therefore, we specially formulate a matrix for achieving training objectives.
Training Standards (Knowledge, Ability and Quality Requirements) |
Implementing approaches |
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Curriculums setting |
Others (e.g. teaching methods, skills competitions, etc.) |
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Standard 1: Have good political quality, moral quality, legal awareness, integrity awareness and team spirit |
1.1 Ability of observing professional ethics |
Ideological and Moral Cultivation and Legal Basis, Academic Ethics Education and Graduation Practice |
Throughout Professional Course Teaching, Special Report |
1.2 Good Political Literacy |
An Introduction to the Basic Principles of Marxism, Mao Zedong Thought and the Theoretical System of Socialism with Chinese Characteristics |
News broadcasting, Current affairs forum |
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Standard 2: Have basic knowledge and accomplishments of humanities, Social Sciences and natural sciences. |
Outline of Modern Chinese History, General Education Course of Art Aesthetics, University Physics |
Listening to Lion Mountain Forum, Participating in Social and Community Activities inside and outside the School |
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Standard 3: Have good physical and mental qualities and humanistic qualities, and also have a strong sense of social responsibility and national defense awareness |
Military theory and skills, Basic Sports, Sports Club Events |
Morning Exercise, Sports Competition and Extracurricular Exercise |
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Standard 4: Have a good foundation of language expression ability, master a foreign language, have international vision and have the abilities of cross-cultural communication, competition and cooperation |
College English, Writing and communication, Graduation Thesis, International Exchange Program, All-English Course |
Putonghua Examination, English Level Examination, English Competition, Teaching of Related Specialty Courses, etc. |
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Standard 5: Have the basic theories and knowledge in the fields of computer science, mathematics and statistics, software engineering etc. that are required for the works of this major |
5.1Mathematical Basis |
Calculus (1) (2), Linear Algebra A, Probability Theory and Mathematical Statistics B, Discrete Mathematics, |
Mathematical Modeling Competition, MOOC, etc. |
5.2 Statistical Basis |
Probability Theory and Mathematical Statistics B. Basis of Statistical Analysis of Big Data |
Throughout Professional Course Teaching and Course Experiments |
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5.3 Computer Science Basis |
C/C++ Language Programming, Data Structure, Algorithmic Analysis and Design, Database Principle, Computer Composition and Structure, Operating System, Data Communication and Network, Software Engineering |
Throughout professional courses teaching, course experiments, various lectures and ACM-ICPC competitions |
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Standard 6: Systematically master the basic theories of big data management, analysis and mining, understand the basic concepts, knowledge structure and typical methods of big data platform, and has some basic skills of big data platform construction and big data analysis. |
6.1 Construction Technology of Big Data Platform |
Comprehensive Practice of Big Data Architecture and Model, High Performance Computing, Cloud Computing, Block Chain, Information Security, Big Data Projects |
Throughout professional courses teaching, course experiments, various lectures and ASC over-calculation contest, Kaggle big data contest, Ali Tianchi big data contest |
6.2 Big Data Management, Analysis and Algorithmic Implementation |
Big Data Architecture and Model, Data Mining, Machine Learning, Big Data Visualization, Information Retrieval and Search Engine, Recommendation System, Natural Language Processing and Knowledge Discovery, Big Data Acquisition and Storage, Comprehensive Practice of Big Data Projects, |
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Standard 7: Have the basic theories and methods of artificial intelligence and machine learning, and have innovative abilities of big data analysis on specific problems. |
Introduction to Artificial Intelligence, Machine Learning, Neural Network and Deep Learning, Computational Intelligence, Data Mining |
College Students' extracurricular technological innovation activities, Intel deep learning competition, ASC super count competition, Internet + innovation and entrepreneurship contest |
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Standard 8: Have good knowledge of the design, development, testing theory and method of big data analysis software system, and have some abilities of big data software system design and implementation. |
Software Engineering, Linux Programming and Application, Functional Programming, Information Security |
Running through the teaching of specialized courses and the extracurricular scientific and technological innovation activities of College Students
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Standard 9: Have good abilities of scientific learning methods, have sustainable learning abilities for actively acquiring knowledge, and have the initial abilities of scientific research. |
Recommendation System, Natural Language Processing and Knowledge Discovery, Medical Health Data Analysis and Mining*, Group Data Analysis and Mining*, Agricultural Big Data Analysis and Mining, Multimedia Data Analysis and Mining, Social Network Data Dividing and Mining |
College students' scientific and technological innovation activities, Classroom teaching, Intel deep learning competition, ASC super count competition, Internet + innovation and entrepreneurship contest |
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