管理学院
大数据管理与应用本科专业人才培养方案
Undergraduates culitivation scheme
前言（修订说明）
一、培养方案修订的指导思想
本次人才培养方案的修订深入贯彻习近平新时代中国特色社会主义思想和党的十九大精神、习近平总书记关于教育工作重要论述，根据《教育部关于加快建设高水平本科教育全面提高人才培养能力的意见》（教高〔2018〕2 号）、《教育部关于深化本科教育教学改革全面提高人才培养质量的意见》（教高〔2019〕6 号）、《广东工业大学本科专业建设管理办法》（广工大规字〔2020〕21 号附件 1）、《广东工业大学本科课程建设管理办法》（广工大规字〔2020〕21 号附件 2）等文件要求，以本科专业类教学质量国家标准和专业认证标准为依据，以促进学生全面发展和适应社会发展需求为根本，以培养有家国情怀、有国际视野、有坚实基础、有创新能力的高素质创新性复合型人才为目标，全面审视各专业课程设置对培养目标和毕业要求的支撑度、专业培养方案与经济社会发展和学生发展需求的契合度，深入推进人才培养模式创新，优化课程设置，改革教学内容，突出专业特色，构建起“以学生发展为中心”的多元化、个性化的高水平创新性应用型人才培养体系，实现培养理念、培养定位、培养规格、培养模式、培养成效的有机统一。
二、培养方案修订的依据
本专业主要依据以下几个方面形成2022版专业培养方案。
1.《国务院办公厅关于深化高等学校创新创业教育改革的实施意见》（国办发〔2015〕36号）
2.《教育部关于深化本科教育教学改革全面提高人才培养质量的意见》（教高〔2019〕6 号
3.广东工业大学《关于修订本科专业人才培养方案的指导意见（2022版）》
4.《广东工业大学本科专业建设管理办法》（广工大规字〔2020〕21 号附件 1）
5.大数据管理与应用专业应用型人才社会需求状况调研报告
6.“大数据管理与应用”专业专家论证意见
7. 《"中国信息系统学科课程体系2011”(CIS2011)》
三、主要内容
在2020版大数据管理与应用专业培养方案的基础上，形成2022版大数据管理与应用专业培养方案，主要修订内容如下：
1. 进一步凝练培养目标及培养特色
本次培养方案的修订，在全面考察大数据管理与应用专业应用型人才社会需求状况的基础上，按照“中国信息系统学科课程体系2011”教学质量国家标准的要求进行，具体包括：（1）专业培养目标和标准紧扣学校人才培养总目标。（2）专业培养目标对应到专业学生毕业后能够胜任金融、商业、工业、医疗与政务等领域对应岗位的内涵要求上；专业的毕业要求（培养标准）应在基本素质、专业知识和业务能力等方面具体描述，并建立系统科学的评价指标体系。（3）专业培养融合大数据应用与计算金融两个模块，强调“基于金融大数据技术的管理” 的专业特色。（4）专业的制定、修订，基于产出导向教育（OBE）理念，以社会需求为导向，理论联系实际，结合粤港澳大湾区人才需求状况，综合考虑国家社会教育发展的需要、广东省产业发展的需要、学校人才培养定位、学生发展期望等，突出实践创新能力和实践技能的培养，通过课程实验、综合设计、社会实践等形式切实提高学生的创新实践能力。
2. 优化课程体系
本专业在确定课程体系时，明确课程体系与毕业要求的支撑关系，以及对专业培养目标达成的支持情况，强调“基于金融大数据技术的管理” 的专业特色，实现不同学科方法在同一管理问题背景下的有效复合，在考虑专业知识结构、学分要求等前提下，通过目标能力矩阵分析，将专业培养目标和培养标准与课程体系对应。
课程体系按基本课程模块和扩展模块构成，基本模块132学分（含基础模块、学院平台课、专业核心课、实践环节等），扩展模块28学分（含大数据应用模块、计算金融模块）（如表1）；基本模块课程为该专业的毕业基本要求，扩展模块形成特色培养和彰显个性化教育。即保证各专业基本培养要求的条件下，形成多模块或多样化教育。课程分必修和选修两大类。对于实验、实习、实训、设计等实践性环节，本专业要求不低于30学分。
四、特色设置
大数据管理与应用专业培养方案结合我校工科办学特点和自身办学优势，培养具有良好的工科基础，突出信息技术、数据分析与经营管理结合特色，会经营、懂数据、懂金融、懂商务、懂管理的复合型人才。同时充分考虑知识要求、专业能力、人文和科学素质、诚信意识和专业操守、沟通能力、持续学习能力等方面的毕业要求，专业课程设置实现管理学、计算机科学和数理统计和金融大数据等四大类知识的融会贯通，强调“基于金融大数据技术的管理”的专业特色，实现不同学科方法在同一管理问题背景下的有效复合。培养学生成为能从大数据角度出发创造性地解决金融大数据和智能决策领域数据科学问题的高级复合型人才。
大数据管理与应用专业学生的知识要求包括人文社科及自然科学基础知识、学科基础知识、专业知识、跨专业知识等四个方面。
（1）人文社科及自然科学基础知识。学生需要具备心理学、历史学、政治学、伦理学、哲学和艺术等方面的人文知识，掌握并运用高等数学、统计学、外语和计算机等方面的知识技能，以及必要的大数据应用知识和数理统计知识。
（2）学科基础知识。大数据管理与应用专业培养的学生首先要具备宽厚的经济学、管理学基础以及丰富的财经领域专业知识，并掌握扎实的计算机软件理论知识，建立一个良好的、基础扎实的知识背景。
（3）专业知识。在具备学科基础知识后，学生还需要系统掌握云计算、金融大数据的理论技术及应用方法、专业的大数据处理能力和先进的大数据分析技术，了解国内外本学科的理论前沿和发展动态，学生自身技能能够满足政府部门、企事业单位和金融机构对金融大数据人才的需求。
Introduction
1、 Guiding ideology of training program revision
The opinions of the Ministry of education on deepening the reform of undergraduate education and teaching and comprehensively improving the quality of talent training, the management measures for the construction of undergraduate majors of Guangdong University of Technology, the management measures for the construction of undergraduate courses of Guangdong University of Technology, and other document requirements. Based on the national standards for undergraduate professional teaching quality and professional certification standards, based on promoting the all-round development of students and meeting the needs of social development, and aiming at cultivating high-quality innovative compound talents with family and country feelings, international vision, solid foundation and innovative ability, comprehensively examine the support of each professional curriculum to the training objectives and graduation requirements. The conformity of the professional training plan with the economic and social development and the development needs of students, further promote the innovation of talent training mode, optimize the curriculum, reform the teaching content, highlight the professional characteristics, build a diversified and personalized high-level innovative application-oriented talent training system with "student development as the center", and realize the training concept, training orientation, training specification. The organic unity of training mode and training effect.
2、 Basis for revision of training program
This major mainly forms the 2022 version of the professional training plan based on the following aspects.
(1) Implementation Opinions of the General Office of the State Council on Deepening the Reform of Innovation and Entrepreneurship Education in Colleges and Universities
(2) Opinions of the Ministry of education on deepening undergraduate education and teaching reform and comprehensively improving talent training quality
(3) Guidance on Revision of undergraduate professional talent training program (2022 Edition) issued by Guangdong University of Technology
(4) Management measures for undergraduate specialty construction of Guangdong University of Technology
(5) Research Report on the Social Demand of Applied Talents in Big Data Management and Application Majors
(6) Expert opinion on "big data management and application"
(7) "China Information System Curriculum System 2011" (CIS2011)
3、 Main amendments
Based on the 2020 version of the big data management and application professional training plan, the 2022 version of the big data management and application professional training plan is formed. The main revisions are as follows:
(1) Further condense training objectives and training characteristics
The revision of this training plan is based on a comprehensive investigation of the social needs of applied talents in big data management and application majors, and is carried out in accordance with the requirements of the national standards for teaching quality of "China Information Systems Curriculum System 2011", including: (1) The professional training goals and standards are closely linked to the school's overall goal of talent training. (2) The professional training objectives correspond to the connotation requirements that professional students can be competent for corresponding positions in the fields of finance, commerce, industry, medical care and government affairs after graduation; professional graduation requirements (training standards) should be in basic quality, professional knowledge and business ability. and other aspects, and establish a systematic and scientific evaluation index system. (3) Professional training integrates the two modules of big data application and computational finance, emphasizing the professional characteristics of "management based on financial big data technology". (4) The formulation and revision of majors are based on the concept of Output-Oriented Education (OBE), oriented by social needs, linking theory with practice, combined with the demand for talents in the Guangdong-Hong Kong-Macao Greater Bay Area, and comprehensively considering the needs of national social education development and Guangdong Province. The needs of industrial development, the orientation of school personnel training, and the development expectations of students, etc., highlight the cultivation of practical innovation ability and practical skills, and effectively improve students' innovative practical ability through curriculum experiments, comprehensive design, social practice and other forms.
(2) Optimize the curriculum system
When determining the curriculum system, this major should clarify the supporting relationship between the curriculum system and the graduation requirements, as well as the support for the achievement of professional training goals, emphasize the professional characteristics of "management based on financial big data technology", and realize different disciplines and methods in the same management problem. For effective compounding in the context, under the premise of considering the structure of professional knowledge and credit requirements, through the analysis of the target ability matrix, the professional training objectives and training standards are corresponding to the curriculum system.
The curriculum system is composed of basic course modules and extended modules. The basic module is 132 credits (including basic modules, college platform courses, professional core courses, practical links, etc.), and the extended module is 28 credits (including big data application modules, computational finance modules) (such as Table 1); the basic module courses are the basic requirements for graduation of the major, and the extended modules form characteristic training and individualized education. That is, under the condition of ensuring the basic training requirements of various majors, multi-module or diversified education is formed. Courses are divided into two categories: compulsory and elective. For practical links such as experiments, internships, practical training, and design, this major requires no less than 30 credits.
4、 Feature setting
The big data management and application major training program combines the characteristics of our school's engineering school and its own school-running advantages to cultivate a good engineering foundation, highlight the characteristics of information technology, data analysis and business management, and be able to operate, understand data, understand finance, understand business, Compound talents who understand management. At the same time, full consideration is given to the graduation requirements in terms of knowledge requirements, professional ability, humanistic and scientific quality, integrity awareness and professional ethics, communication ability, continuous learning ability, etc. The integration of major types of knowledge, emphasizing the professional characteristics of "management based on financial big data technology", and realizing the effective combination of different disciplines and methods under the same management problem background. Train students to become senior compound talents who can creatively solve data science problems in financial big data and intelligent decision-making from the perspective of big data.
The knowledge requirements for students majoring in big data management and application include four aspects: basic knowledge of humanities and social sciences and natural sciences, basic knowledge of disciplines, professional knowledge, and inter-professional knowledge.
(1) Basic knowledge of humanities, social sciences and natural sciences. Students need to have humanistic knowledge in psychology, history, political science, ethics, philosophy and art, master and apply knowledge and skills in advanced mathematics, statistics, foreign languages and computers, as well as necessary big data application knowledge and skills. Knowledge of mathematical statistics.
(2) Basic knowledge of subjects. The students trained in the major of big data management and application must first have a broad foundation in economics, management and rich professional knowledge in the field of finance and economics, and master a solid theoretical knowledge of computer software to establish a good and solid knowledge background.
(3) Professional knowledge. After having the basic knowledge of the subject, students also need to systematically master the theoretical techniques and application methods of cloud computing and financial big data, professional big data processing capabilities and advanced big data analysis techniques, and understand the theoretical frontiers and development trends of the subject at home and abroad. , students' own skills can meet the needs of government departments, enterprises and financial institutions for financial big data talents.
大数据管理与应用
Big data managementand applications
专业代码：120108T
Code: 120108T
学制：四年
Length of schooling: Four Years
授予学位：管理学学士
Degree: Bachelor of Mangement
制定（修订）时间： 2022年1月
Time of revision: Jan, 2022
一.培养目标
本专业以粤港澳大湾区建设、银行及证券等金融行业大数据发展、广东省智能制造等社会经济发展需要为导向，培养能够引领行业发展的金融大数据和从事管理信息系统，以及大数据分析与应用的实用型复合人才。培养具备宽厚的经济学、管理学基础以及丰富的财经领域专业知识，并掌握扎实的计算机软件理论知识，掌握云计算、金融大数据的理论技术及应用方法、专业的大数据处理能力和先进的大数据分析技术的学生，能够满足政府部门、企事业单位和金融机构对金融大数据人才的需求。同时培养出的学生能够成为从大数据角度出发创造性地解决金融大数据和智能决策领域数据科学问题的高级复合型人才。
Ⅰ. Educational objectives
Guided by the construction of the Guangdong-Hong Kong-Macao Greater Bay Area, the development of big data in banking and securities and other financial industries, and the social and economic development needs of intelligent manufacturing in Guangdong Province, this major aims to cultivate practical composite talents who can lead the development of the industry in financial big data and management information system, as well as the analysis and application of big data. To cultivate students with broad foundation of economics and management, rich professional knowledge in the field of finance and economics, solid theoretical knowledge of computer software, theoretical technology and application methods of cloud computing and financial big data, professional big data processing ability and advanced big data analysis technology. It can meet the needs of government departments, enterprises, institutions and financial institutions for financial big data talents. At the same time, the students can become senior compound talents who creatively solve data science problems in the field of financial big data and intelligent decision making from the perspective of big data.
二、毕业要求
本专业学生在培养过程中，强调对学生进行基本理论、基础知识、基本能力（技能）以及健全人格、综合素质和创新精神的培养；致力于为学生全面参与教学改革，科学研究及社会服务等活动创造条件，提倡学生在参与中发现并培养自己的兴趣和能力，最大限度地发展学生的智力和潜能，鼓励学生敢于面对挑战、不断探索、努力进取、追求卓越；并提供一定的条件，促使学生养成独立工作和团队合作的能力，促使学生养成终身学习和自主学习的习惯。
经过四年的系统学习，本专业学生在毕业时应达成以下毕业要求：
1.知识要求：能够将计算机科学、管理科学和专业知识用于解决信息化、大数据分析及计算金融等领域的复杂问题。
2.问题分析：能够应用计算机科学、管理科学等知识的基本原理，识别、表达、并通过文献研究分析信息化、大数据及计算金融等领域的复杂问题，以获得有效结论。
3.设计/开发解决方案：能够针对信息化、大数据及计算金融等领域的复杂问题设计解决方案，设计满足特定需求的信息系统、信息模型或信息流程，并能够在设计环节中体现创新意识，考虑社会、健康、安全、法律、文化以及环境等因素。
4.研究：能够基于科学原理并采用科学方法对信息化、大数据及计算金融等领域的复杂问题进行研究，包括设计实验、分析与解释数据、并通过数据分析综合得到合理有效的结论。
5.使用现代工具：能够针对信息化、大数据及计算金融等领域的复杂问题，开发、选择与使用恰当的技术、资源、现代工程工具和信息技术工具，包括对复杂问题的预测与模拟，并能够理解其局限性。
6.工程与社会：能够基于计算机和管理复合知识进行合理分析，评价信息化、大数据及计算金融等领域的复杂问题解决方案对社会、健康、安全、法律以及文化的影响，并理解应承担的责任。
7.环境和可持续发展：能够理解和评价针对信息化、大数据及计算金融等领域的复杂问题的专业实践对环境、社会可持续发展的影响。
8.职业规范：具有人文社会科学素养、社会责任感，能够在工作实践中理解并遵守职业道德和规范，履行责任。
9.个人和团队：能够在多学科背景下的团队中承担个体、团队成员以及负责人的角色。
10.沟通：能够就信息化、大数据及计算金融等领域的复杂问题与业界同行及社会公众进行有效沟通和交流，包括撰写报告和设计文稿、陈述发言、清晰表达或回应指令。并具备一定的国际视野，能够在跨文化背景下进行沟通和交流。
11.项目管理：理解并掌握计算机和管理原理与经济决策方法，并能在多学科环境中应用。
12.终身学习：具有自主学习和终身学习的意识，有不断学习和适应发展的能力。
II. Graduation requirements
During the cultivation process of the students in this major, emphasize the cultivation of basic theory, basic knowledge, basic ability (skills), healthy personality, comprehensive quality and innovation spirit; create conditions for students to fully participate in the teaching reform, scientific research and social services etc. activities; advocate students to discover and develop own interest and ability, and maximize the intelligence and potential by participation, encourage students to dare to face challenges, constantly explore, strive for progress, pursue the excellence; provide the conditions to encourage students to develop the ability of work independently and team work, help students form the habit of lifelong learning and autonomous learning.
After four years of systematic study, the graduates in this major should acquire the following knowledge and abilities:
1. Specific knowledge: Use the mathematics, natural science, engineering foundation and professional knowledge to solve the complex problems in the fields of information management, big data and internet finance.
2. Problem analysis: Apply the basic principles of mathematics, natural science and engineering science, and through the literature research to recognize, express and analysis complex problems in the fields of information management, big data and internet finance, in order to obtain valid conclusions.
3. Design/development solutions: Design solutions aiming at complex problems in the fields of information management, big data and internet finance, designed system, unit or technological process to meet the specific needs, and can reflect innovation consciousness, consider the social, health, safety, legal, cultural and environmental factors in the design process.
4. Study: Study the complex problems in the fields of information management, big data and internet finance on the base of scientific principles and the scientific method, including design experiment, analysis and interpret data, and get the reasonable and effective conclusions through the comprehensive information.
5. Use of modern tools: Develop, select and use the appropriate technology, resources and modern engineering tools and information technology tools aiming at complex problems in the fields of information management, big data and internet finance, including the prediction and simulation of complex problems and can understand its limitations.
6. Engineering and society: Reasonably analysis and evaluate the influences, based on the engineering background, and the solutions of the complex problems in the fields of information management, big data and internet finance on social, health, safety, legal and culture, and understand the responsibility.
7. Environment and sustainable development: Understand and evaluate the influences of professional practice of the complex problems in the fields of information management, big data and internet finance on the environment and social sustainable development.
8. Professional norms: Possess the humanities and social science literacy and social responsibility, understand and comply with the professional ethics and norms in the career practice and fulfill the responsibility.
9. Individual and team: Undertake the role of individual, team members and the head in the team of multidisciplinary background.
10. Communication: Effectively communicate with the industry peers and the social public communication on the complex problems in the fields of information management, big data and internet finance, including writing reports and design documents, presentation speech, clear expression or respond to commands. Have a certain international vision, can communicate under the cross-cultural background.
11. Project management: Understand and grasp the project management principle and economic decision method, can apply it in a multidisciplinary environment.
12. Lifelong learning: Possess the consciousness of independent learning and lifelong learning, and the ability to constantly learn and adapt to the development.
三、专业培养特色
1．掌握计算机和数学能力基础的上懂数据、懂金融、懂商务、懂管理的复合型人才
结合我校工科办学特点和自身办学优势，培养具有良好的工科基础，突出信息技术、数据分析与经营管理结合特色，会经营、懂数据、懂金融、懂商务、懂管理的复合型人才。为此，本专业围绕4个知识模块开展：一是经济管理类知识模块，包括经济学、管理学、会计学、财务管理、运筹学、经济法等课程；二是数理统计类知识模块，包括高等数学、线性代数、概率论与数理统计、统计学、计量经济学等课程；三是大数据应用模块，包括Java Web应用开发、数据治理与数据质量管理、数据可视化、大数据分析、数据挖掘与商务智能、文本挖掘、社交媒体分析、大数据战略与管理、计算机原理、移动应用开发等课程；四是计算金融模块，包括金融学、金融工程学、金融数量分析、证券投资学、金融随机分析、多元统计分析、金融风险管理、供应链金融、金融时间序列分析、互联网金融理财。
通过上述4类知识模块，突出以组织管理问题解决能力培养为主线，发挥本校以工为主、理工经管文法全面发展的优势，专业课程设置实现管理学、计算机科学和数理统计和金融大数据等四大类知识的融会贯通，强调“基于金融大数据技术的管理” 的专业特色，实现不同学科方法在同一管理问题背景下的有效复合。培养学生成为掌握计算机和数学能力基础的上懂数据、懂金融、懂商务、懂管理的复合型人才。
2．高度重视实践，实践教学体系化的高级应用型人才
强化学科优势特色方向，面向国家重大战略需求和经济社会实际需要，重点培养学生基于大数据解决实际问题的知识、能力和素质。强调实践教学环节，高度重视应用型高级专门人才的培养。不仅安排大量的课程实验、实训，还有专门的专业实习、社会实践，课程设计、专业综合设计、毕业设计等环节，相辅相成，形成体系，突出培养学生的工程意识、创新精神以及运用大数据架构与分析技术和金融知识解决经济、商务、管理中实际问题的能力。
3．灵活应用多种教学方法，传授不同学科知识
根据经济管理类和信息技术类知识在问题解决能力上的不同特点，综合运用多媒体远程教育等多种现代化教学手段，广泛利用教师的纵、横向科研项目成果，以及往届学生的优秀作品，灵活采用技术规范教学法、项目教学法、案例教学法等多种教学方法，对信息技术类知识传授采用类似于毕业设计指导的小班模式，确保教学质量，实现人才培养在质量和数量上的和谐发展。
III. Features of speciality cultivation
1. Compound talents who understand data,financial, business, and management
Combined with the characteristics of engineering running in our school and its own advantages, we aim to cultivate interdisciplinary talents with good engineering foundation, outstanding characteristics of combining information technology, data analysis and operation management, and knowledge of operation, data, finance, business and management. Therefore, this major is carried out around four knowledge modules: one is economic management knowledge module, including Economics, Management, Accounting, Financial Management, Operations Research, Economic Law and other courses; Second, mathematical statistics knowledge modules, including Advanced Mathematics, Linear Algebra, Probability Theory and Mathematical Statistics, Statistics, Econometrics and other courses; The third is the big data application module, including Java Web Application Development, Data Management and Data Quality Management, Data Visualization, Data Analysis, Data Mining and Business Intelligence, Text Mining, Social Media Analysis, Strategic and Big Data Management, Computer Principles, Mobile Application Development, etc. Fourth, Finance, Financial Engineering, Financial Quantitative Analysis, Securities Investment, Finance Stochastic Analysis, Multivariate Statistical Analysis, Financial Risk Management, Supply Chain Finance, Gold Time Series Analysis, Internet Financial Management.
Through the above four kinds of knowledge module, highlight to management problem solving ability training as the main line, the school is given priority to with work, management of technology, the grammar of comprehensive development advantages, professional curriculum implementation management, computer science and mathematical statistics and financial data such as four major categories of knowledge mastery, emphasis on "based on the financial data management technology" professional characteristics, Realize the effective combination of different discipline methods in the same management problem background. To train students to become inter-disciplinary talents who understand data, finance, business and management on the basis of computer and mathematical ability.
2. Strong emphasis for practice, course architecture
Emphasize the practical education session, pay effort for the cultivation of senior application-oriented specialists. The course system not only includes plentiful experimental courses and exercitation, but also professional training, social training, course project, major comprehensive design, graduation design, which supplement each other and form a complete system. It focuses on the cultivation of the understanding in engineering and the spirit of innovation, and the capability of solving practical problems in economics, business, and management by using information technology and financial knowledge.
3. Utilizing Flexible cultivation method, imparting wide knowledge in various subjects
Based on the different capacities of knowledge between economic management and information technology in solving problems, comprehensively utilize remote multi-media teaching etc modern teaching methods, extensively employ teachers’ academic and application project achievement, and good works of former graduates, flexibly use technical specifications teaching method, project teaching method, case study etc various methods. For teaching the knowledge on information technology, utilize small class mode to guarantee the teaching quality, achieve the harmonious development of personnel training in quality and quantity.
四、专业主干学科
经济学、管理学、计算机科学与技术、数理统计。
IV. Key discipline for the specialty
Economics, Management, Computer Science and Technology, and Mathematical statistics.
五、专业核心课程
管理学、经济学 、统计学、计量经济学 、运筹学、会计学原理、Java程序设计语言、数据结构 、数据库原理、Python程序设计、大数据平台基础(Hadoop)、大数据采集与管理、NoSQL数据库、人工智能应用
V. Core courses
Management, Economics, Statistics, Econometrics, Operations Research, Accounting, Java Programming, Data Structure, Database Principles, Python Programming, Big Data Platform Foundation (Hadoop), Big Data Collection and Management, NoSQL Database, Artificial Intelligence Applications
六、特色课程（全英课程、双语课程及其他特色教学改革课程）
Java Web应用开发、数据治理与数据质量管理、数据可视化、大数据分析、数据挖掘与商务智能、文本挖掘、社交媒体分析、大数据战略与管理、计算机原理、移动应用开发、金融学、金融工程学、金融数量分析、证券投资学、金融随机分析、多元统计分析、金融风险管理、供应链金融、金融时间序列分析、互联网金融理财。
Ⅵ. Feature Courses (English courses, bilingual courses and other featured reforming courses)
Java Web Application Development, Data Management and Data Quality Management, Data Visualization, Data Analysis, Data Mining and Business Intelligence, Text Mining, Social Media Analysis, Strategic and Big Data Management, Computer Principles, Mobile Application Development,Finance, Financial Engineering, Financial Quantitative Analysis, Securities Investment, Finance Stochastic Analysis, Multivariate Statistical Analysis, Financial Risk Management, Supply Chain Finance, Gold Time Series Analysis, Internet Financial Management.
七、毕业学分要求
课内总学分不低于161学分，实践教学环节学分不少于30学分。
VII. Credits required for graduation
A total course credit is not below 161, practical session credits not below 30.
八、主要实践教学环节
专业导论、专业实习、专业综合设计、毕业实习、毕业设计（论文）等。
VIII. Main components of practical teaching
Cognitive internship, Professional internship, Professional comprehensive design, Graduation internship, Graduation design (thesis) etc.
九.课程体系的构成及课程学分分配比例
IX. Course system structure and course credit proportion
1、课内部分 Intra-curricular sector
|
课程类别 Course Category |
内容说明 Description |
总学分 Total Credits |
总学时 Total Teaching Hours |
占总学分 比例 Percentage |
小计 Subtotal |
必修 Compulsory Courses |
公共基础课 Basic Public Courses |
含“思想政治理论课”、体育、大学英语、高等数学、大学物理、计算机文化基础等。 Courses such asIdeological & Political Theories, University Physical Education, College English, AdvancedMathematics, Basic Computer Literary. |
54 |
980 |
33.12% |
56.25% |
专业基础课 Basic Specialty Courses |
构筑专业基础平台的基本概念、理论和基础知识的课程。 Courses for constructing the basic concepts, theories and knowledge underlying the specialty. |
19 |
304 |
11.88% |
专业课 Specialty Courses |
构筑专业方向的概念、理论和知识的课程。 Courses for constructing concepts, theories and knowledge of the specialty emphasis. |
18 |
288 |
11.25% |
实验实习实训 Experimental and Practical Courses |
|
13 |
|
7.50% |
18.75% |
设计（论文） Graduation Design (Thesis) |
|
17 |
|
11.25% |
课程类别 Course Category |
内容说明 Description |
总学分 Total Credits |
总学时 Total Teaching Hours |
占总学分 比例 Percentage |
小计 Subtotal |
选修 Elective Courses |
全校性公共课（至少选12.0学分） University Wide Public Courses (A minimum of 12.0 credits required) |
指人文社科类、自然科学与工程技术类全校性公选课。 University wide public elective courses in humanities and social sciences, natural sciences, and engineering. |
12.0 |
192 |
7.50% |
25.00% |
专业基础课 （至少选 学分） Basic Specialty Courses (A minimum of 11credits required) |
指相关学科和跨学科的基础理论和知识的课程。 Courses for basic theories and knowledge in the main discipline and related disciplines. |
14 |
224 |
8.75% |
专业课 （至少选 学分）Specialty Courses (A minimum of credits required) |
指学科方向和跨学科方向的基础理论和知识的课程。 Courses for basic theories and knowledge in the disciplinary emphasis and interdisciplinary emphasis. |
14 |
224 |
8.75% |
实验实习实训 （至少选 学分） Experimental and Practical Courses (A minimum of credits required) |
|
0 |
0 |
0% |
0% |
设计（论文） （至少选 学分） Graduation Design (Thesis) (A minimum of credits required) |
|
0 |
0 |
0% |
合计 Total |
161.0 |
|
100% |
100% |
2、课外部分 Extra-curricular sector
课程类别 Category |
课程名称 Course Name |
学分 Credits |
总学时 Total Teaching Hours |
实验 学时 Teaching Hours for Experiments |
实习实训学时 Teaching Hours for Practice |
上机 学时 Teaching Hours with Computers |
必修 Compulsory Part |
公共教育类 Public Education |
入学教育 Entrance Education |
0.5 |
0.5周 |
|
|
|
公益活动 Social Work |
1.0 |
16 |
|
|
|
社会实践 Social Practice |
2.0 |
32 |
|
|
|
“毛泽东思想和中国特色社会主义理论体系概论”课外导读 Extra-curricular guided reading of An Introduction to Mao Zedong Thought and Theoretical System of Socialism with Chinese Characteristics |
1.0 |
16 |
|
|
|
毕业教育 Graduation Education |
0.5 |
0.5周 |
|
|
|
小计 Subtotal |
5.0 |
80 |
|
|
|
