Data Science and Big Data Technology

专业代码：080910T             学  制：4年

Program Code: 080910TDuration：4 years

培养目标（Educational Objectives）

培养热爱祖国，坚持社会主义道路，德、智、体、美、劳全面发展，具有扎实的数据科学与大数据技术基。睹婀，实践动手能力强的研究型或工程型数据科学领军人才：具备大数据领域基础知识、基本技能和科学研究的基本素质；具有应用大数据技术理论和方法进行数据建：透咝Х治鲇氪硇幸倒丶约际跷侍獾淖酆夏芰Γ痪哂星苛业纳缁嵩鹑胃，具有良好的职业道德、敬业精神、英语能力和国际视野；具有源头创新和引领行业技术发展的潜质，具有终身学习并适应大数据领域新发展的能力；适应独立和团队工作环境，成为推动大数据技术在互联网、金融、教育、医疗、物流等相关行业应用创新的数据科学人才。

To cultivate researchers and engineers in big data science who are patriotic, adhere to the undertaking of socialist development with Chinese characteristics and are well-developed morally, intelligently, physically, aesthetically and with a hard-working spirit. The one who has a solid theoretical knowledge in data science and big data technology and a good command of English; the one who is both well-read and have strong practical skills; the one that has a basic knowledge, technical skills and research abilities in the field of big data; the one that has comprehensive abilities to build data models and analyze key industrial problems using big date technology theories and methodologies; the one that has a strong sense of society, of professionalism and work well in a team; the one that has a global outlook and strong technical expertise; the one that has the potential to create, to innovate and to become a leader in technological advancement; the one that is able to pursue lifelong learning and to adapt to the innovation and development of big data science; the one that could work independently and as a team member to promote big data technology innovation in internet, finance, education, health, logistics sectors and so on.

毕业要求（Student Outcomes）

№1.工程知识：能够将数学、自然科学、工程基础和专业知识用于解决大数据领域复杂工程问题。

№1.1 能够应用数学、自然科学、工程基础和专业知识表述大数据领域工程问题，并建立具体对象的数学模型；

№1.2 能够应用数学、自然科学、工程基础和专业知识解释模型的物理含义，对模型进行正确的推理和解答；

№1.3 能够将数学、自然科学、工程基础和专业知识用于大数据领域工程问题的分析、计算和设计。

№1.4能够将数学、自然科学、工程基础和专业知识用于大数据领域工程问题的解决方案的比较与综合。

№2.问题分析：能够应用数学、自然科学和工程科学的基本原理，识别、表达、并通过文献研究分析大数据领域相关复杂工程问题，以获得有效结论。

№2.1对大数据相关工程问题，能分析其需求，给出任务目标的需求描述，并识别其面临的各种制约条件。

№2.2对大数据相关工程问题，能根据需求描述，建立解决问题的抽象模型。

№2.3对大数据相关工程问题，能根据所建立的抽象模型，通过文献检索与资料查询等方式获取知识和方法，对问题进行分析，并得出有效结论。

№3.解决方案：能够设计针对大数据领域相关复杂工程问题的解决方案，设计满足特定需求的系统、单元或流程，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。

№3.1针对特定需求，能对大数据领域中的相关工程问题进行分解和细化，能够进行模块的设计与开发。

№3.2了解大数据领域技术发展的现状与趋势，能够在方案设计中体现创新意识。

№3.3结合社会、健康、安全、法律、文化及环境等因素，综合考虑复杂工程问题的应用背景、系统特性、设计流程等因素，分析对比候选方案的可行性和性能，确定解决方案。

№4.研究能力：能够基于科学原理并采用科学方法对大数据领域相关复杂工程问题进行研究，包括设计实验、分析与解释数据、并通过信息综合得到合理有效的结论。

№4.1能够基于科学原理并采用科学方法进行大数据领域的相关复杂工程问题的系统分析和建模。

№4.2能够针对复杂工程系统进行实验方案设计、实验平台搭建、实验数据获取。

№4.3能够对实验数据进行信息综合分析，并得到合理有效的结论，反馈到工程设计实践中。

№5.使用现代工具：能够针对大数据领域相关复杂工程问题，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，包括对复杂工程问题的预测与模拟，并能够理解其局限性。

№5.1能恰当使用计算机软件技术及算法仿真工具，完成大数据系统中的复杂工程问题的模拟与仿真分析，能理解其局限性。

№5.2能熟练使用仪器工具观察分析大数据系统性能，能运用图表、公式等手段表达和解决大数据的设计问题，能理解其局限性。

№6.工程与社会：能够基于大数据相关背景知识进行合理分析，评价专业工程实践和复杂工程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。

№6.1具备社会、健康、法律、安全以及文化的基本知识和素养。

№6.2能够合理评价大数据领域相关工程实践和复杂工程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复杂工程问题的大数据专业工程实践对环境、社会可持续发展的影响。

№7.1了解大数据相关产业、大数据服务业相关的方针、政策与法律法规。

№7.2理解大数据产业与环境的关系，理解和评价针对复杂工程问题的工程实践对环境、社会可持续发展的影响，理解用技术手段降低其负面影响的作用与其局限性。

№8.职业规范：具有人文社会科学素养、社会责任感，能够在工程实践中理解并遵守工程职业道德和规范，履行责任。

№8.1具有人文知识、思辨能力、处事能力和科学精神，理解应担负的社会责任。

№8.2能够在大数据项目实践中理解并遵守工程职业道德和规范，具有法律意识，做到责任担当、贡献国家、服务社会。

№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。

№9.1能够在大数据领域相关研究、开发和生产的团队中承担个体和成员角色，具有团队合作精神或意识；

№9.2能够在多学科背景下充分理解和消化其他学科的知识和方法，掌握团队合作的组织管理方式，具有团队负责人意识。

№10.沟通能力：能够就大数据领域复杂工程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。

№10.1具有良好的表达能力，能够就复杂工程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。

№10.2具备运用外语的能力和一定的国际视野，能够在跨文化背景下进行沟通和交流。

№11.项目管理：理解并掌握工程管理原理与经济决策方法，并能在多学科环境中应用。

№11.1理解并掌握工程管理原理与经济决策方法，能够识别大数据领域相关工程项目管理与经济决策中的关键因素。

№11.2能够将工程管理原理和经济决策方法运用于跨学科的复杂工程项目中。

№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。

№12.1理解不断探索和学习的必要性，具有自主学习的方法，了解拓展知识和能力的途径。

№12.2具有自主学习意识和终身学习的意识，能够根据社会环境和个人角色变化有不断学习和适应发展的能力。

№1.Engineering Knowledge: An ability to apply knowledge of mathematics, science, engineering fundamentals and engineering specialization to the solution of complex engineering problems.

№1.1 Being able to apply knowledge in mathematics, natural sciences, engineering fundamentals and Big Data to describe Big Data-related engineering problems, and to establish mathematical models of related subjects;

№1.2 Being able to explain the physical meaning of said models using knowledge in mathematics, natural sciences, engineering fundamentals and Big Data, and to make proper reasoning and explanation to the models.

№1.3 Being able to analyze, compute and design Big Data -related problems using knowledge in mathematics, natural sciences, engineering fundamentals and Big Data.

№1.4 Being able to compare and combine solutions using knowledge in mathematics, natural sciences, engineering fundamentals and Big Data.

№2.Problem Analysis: An ability to identify, formulate and analyze complex engineering problems, reaching to substantiated conclusions using basic principles of mathematics, science, and engineering.

№2.1 Being able to analyze what is required to solve a particular Big Data -related engineering problem, describe detailed requirements and identify potential constrains before reaching target outcomes

№2.2 Being able to build abstract models according to the descriptions of detailed requirements of a particular Big Data-related engineering problem

№2.3 Being able acquire knowledge and methodology through literature retrieval and material searching, analyze problems and reach effective conclusions according to the abstract model established to solve a particular Big Data -related engineering problem.

№3.Design/Development Solutions: An ability to design solutions for complex engineering problems and innovatively design systems, components or process that meet specific needs with societal, public health, safety, legal, cultural and environmental considerations.

№3.1 Being able to design and develop software modules after careful disintegration and division of Big Data-related engineering problems according to specific needs.

№3.2 Being able to catch up with the current status and trends in Big Data-related technological development and to demonstrate innovation in the solution design.

№3.3 Being able to compare the feasibility and performance of different solutions and choose the better ones taking into consideration the background of said complex engineering problems, systematic characters used and procedures of designing etc. with an overall assessment on social, health, safety, legal, cultural and environmental concerns.

№4.Research: An ability to conduct investigations of complex engineering problems based on scientific theories and adopting scientific methods including design of experiments, analysis and interpretation of data and synthesis of information to provide valid conclusions.

№4.1 Being able to perform systematic analysis and build models on Big Data -related complex engineering problems based on scientific principles and using scientific methods.

№4.2 Being able to design experiments, build experimental platforms, and acquire data for complex engineering systems.

№4.3 Being able to conduct comprehensive information analysis on the data acquired, and to reach reasonable and effective conclusion that in turn guides solution design.

№5.Applying Modern Tools: An ability to create, select and apply appropriate techniques, resources, and modern engineering and IT tools, including prediction and modelling, to complex engineering activities, with an understanding of the limitations.

№5.1 Being able to develop, choose and use proper technology, resources, modern engineering and information technology tools to predict and simulate complex Big Data -related engineering problems and understand its constrains.

№5.2 Being able to use instruments well to observe and analyze the performance of Big Data systems, and to use diagrams, formulas and others to express and solve Big Data design problems with awareness of its limitations.

№6.Engineering and Society: An ability to apply reasoning informed by contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to professional engineering practice.

№6.1 Being well-equipped with basic knowledge of society, health, law, safety and culture.

№6.2 Being able to give a reasonable evaluation on the impact of Big Data -related engineering practices and complex engineering problem solutions on society, health, safety, law, and culture, with an understanding of duties that needs to be undertaken.

№7.Environment and Sustainable Development: An ability to understand and evaluate the impact of professional engineering solutions in environmental and societal contexts and demonstrate knowledge of and need for sustainable development.

№7.1 Having a good knowledge of the guidelines, policies, laws and regulations on Big Data -related industries and service sectors.

№7.2 Having a good understanding of the relation between Big Data industry and the environment, of the impact of engineering practice on environment and the sustainable development of society, and of the role technology can play in reducing these negative impacts and its constrain.

№8.Professional Standards: An understanding of humanity science and social responsibility, being able to understand and abide by professional ethics and standards responsibly in engineering practice.

№8.1 Having a good knowledge in humanities, developing strong critical thinking, interpersonal skills, and scientific spirit, with an awareness of the social responsibilities that needs to be undertaken.

№8.2 Being able to understand and abide by professional ethic and norms during the carrying-out of Big Data projects, having a good legal sense and being ready to take responsibility for the country and the society.

№9.Individual and Teams: An ability to function effectively as an individual, and as a member or leader in diverse teams and in multi-disciplinary settings.

№9.1 Being able to work well with team members in Big Data -related research, development and production projects;

№9.2 Being able to understand and learn knowledge and methods of other disciplines in a multi-disciplinary team, to engage in the management of the team and act with good leadership skills.

№10.Communication: An ability to communicate effectively on complex engineering problems with the engineering community and with society at large, such as being able to comprehend and write effective reports and design documentation, make effective presentations, give and receive clear instructions, and communicate in cross-cultural contexts with international perspective.

№10.1 Being able to express oneself well and conduct effective communication with peers and the public on complex engineering problems by ways of report-writing, designing, public speech, instruction responding etc.

№10.2 Having a good command of foreign languages and global outlook, and being able to communicate in a cross-cultural context.

№11.Project Management: Demonstrate knowledge and understanding of engineering management principles and methods of economic decision-making, to function in multidisciplinary environments.

№11.1 Being able to understand and master management fundamental in engineering and economic decision-making methods, and to identify key factors in the managing and economic decision-making of Big Data related projects.

№11.2 Being able to apply knowledge in engineering management and economics in complex interdisciplinary engineering projects.

№12.Lifelong Learning: A recognition of the need for, and an ability to engage in independent and life-long learning with the ability to learn continuously and adapt to new developments.

№12.1 Understanding the need of continuous study, being able to study independently and knowing ways to expand knowledge and improve oneself.

№12.2 Having a good sense of independent learning and lifelong learning, and being able to learn continuously and adapt to the surroundings.

专业简介（Program Profile）

数据科学与大数据技术专业依托珠三角大数据产业优势，紧密围绕产业需求，突出大数据行业前沿发展，深入推进学科交叉，培养具有国际视野的高水平国际化大数据精英人才。本专业注重实践环节和创新能力培养，突出理论课与实训课相结合的培养特色，强化工程训练，实现国际接轨，造就基础扎实、工程能力强、英语和协作能力好的复合型大数据研究与工程人才。

本专业涉及包括自然科学、工程技术、信息技术的大量理论知识与技术方法，聚焦行业需求，注重前沿交叉，深耕产学合作，注重国际交流与校企合作。德赢新版app毕业后可以继续攻读相关领域的硕士博士，也可以在国家机关和企事业单位从事大数据研究、大数据分析、大数据应用、大数据决策等工作。

The program of Data Science and Big Data Technology aims to cultivate top talents in big data with a global outlook. The design of the program takes full advantages of local industrial resources in big data, catering to the needs of the industry and with an eye on the latest development in this area. We emphasize the cultivation of practical skills and innovation abilities in its students by combining theoretical courses with practice course. Engineering skills of the students are constantly strengthened during their 4-year study. We are dedicated to educating all-round high-caliber big data researchers and engineers who has a solid basic knowledge, good engineering skills, English-proficient and with good teamwork spirit.

The program involves courses that introduce rich theoretical knowledge and technical methods in natural science, engineering technology, and information technology. With particular focus on current industrial needs and emphasis on inter-disciplinary teaching, we are constantly deepening our industry-university ties and expanding international exchanges. Upon graduation, students can choose between pursuing master or doctoral level study, and conducting big data research, analysis, application or decision-making in governmental departments or companies,

专业特色（Program Features）

本专业通过国际及产业师资力量，开设在数据科学、大数据、人工智能、计算智能和云计算等领域方向的国际化前沿课程及产学融合课程，培养德赢新版app在数据科学与大数据技术领域的创新研究和工程实践能力。

This program provides courses in Data Science, Big Data, Artificial Intelligence, Computational Intelligence, Cloud Computing and many others that are characterized with international and industrial elements by introducing faculty teams internationally and from the industrial sectors. We aims to cultivate innovative research ability and practical engineering skills in the area of data science and big data technology in students.

授予学位（Degree Conferred）

工学学士学位 Bachelor of Engineering

核心课程（Core Courses）

大数据导论、数据结构、计算机网络、计算机安全与数据安全、计算机组成与体系结构、操作系统、数据库系统、计算机与软件工程概论、数据挖掘、算法设计与分析、大数据平台构架与技术、云计算与大数据平台、神经网络与深度学习。

Introduction to Big Data, Data Structures, Computer Network, Computer Security and Data Security, Computer Organization and Architecture, Operating System, Database System, Introduction to Computer and Software Engineering, Data Mining, Algorithm Design and Analysis, Architecture and Technology of Big Data Platform, Cloud Computing and Big Data Platform, Neural Network and Deep Learning.

特色课程（Featured Courses）

n 新生研讨课：工程导论I 

n 专题研讨课：工程导论I

n 慕课：数据结构

n 学科前沿课：云计算与大数据平台

n 跨学科课程：神经网络与深度学习

n 本研共享课：大数据平台构架与技术

n 校企合作课：计算机与软件工程概论、IT商业模式与创业、大数据应用案例与实践

n 竞教结合：算法设计与分析、数据结构

n 创新实践课：IT商业模式与创业 （“三个一”课程）

n 创业教育课：IT商业模式与创业

n 工作坊：数据挖掘课程实训

n 专题设计课：大数据应用案例与实践 

n 劳动教育课：工程训练I

n Freshmen Seminars: Introduction to Engineering I

n Special Topics: Introduction to Engineering I

n MOOC: Data Structures

n Subject Frontiers Courses: Cloud Computing and Big Data Platform

n Interdisciplinary Courses: Neural Network and Deep Learning

n Bachelor-Master's Integrated Courses: Architecture and Technology of Big Data Platform

n Cooperative Courses with Enterprises: Introduction to Computer and Software Engineering, IT Business Model and Entrepreneurship，Big Data Application Case and Practice

n Contest-Teaching Integrated Courses: Algorithm Design and Analysis，Data Structures

n Innovation Practice: IT Business Model and Entrepreneurship （Three Ones Courses）

n Entrepreneurship Courses: IT Business Model and Entrepreneurship

n Workshops: Data Mining Course Training

n Special Designs: Big Data Application Case and Practice

n Labor Education Courses: Engineering Training I

一、各类课程学分登记表（Registration Form of Curriculum Credits）

1.学分统计表（Credits Registration Form）

备注：德赢新版app毕业时须修满专业教学计划规定学分，并取得第二课堂3个人文素质教育学分和4个创新能力培养学分。

2.类别统计表（Category Registration Form）

备注：

1.通识课计入选修一项中；

2.实验教学包括“专业教学计划表”中的实验、实习和其他；

3.创新创业教育学分：培养计划中的课程，由各土耳其里拉兑换人民币教学指导委员会认定，包括竞教结合课程、创新实践课程、创业教育课程等学分；

4.必修学时＋选修学时=总学时数；理论教学学时＋实验教学学时=总学时数；必修学分＋选修学分=总学分数；集中实践教学环节学分＋理论教学学分＋实验教学学分=总学分数

二、课程设置表（Courses Schedule）

备注：学时中其他可以为上机和实践学时。

二、课程设置表（续）（Courses Schedule）

备注：学时中其他可以为上机和实践学时。

德赢新版app根据自己开展科研训练项目、学科竞赛、发表论文、获得专利和自主创业等情况申请折算为一定的专业选修课学分（创新研究训练、创新研究实践I、创新研究实践II、创业实践等创新创业课程）。每个德赢新版app累计申请为专业选修课总学分不超过4个学分。经德赢新版app批准认定为选修课学分的项目、竞赛等不再获得对应第二课堂的创新学分。

三、集中实践教学环节（Practice-concentrated Training）

四、第二课堂（Second Classroom Activities）

第二课堂由人文素质教育和创新能力培养两部分组成。

1.人文素质教育基本要求

德赢新版app在取得专业教学计划规定学分的同时，还应结合自己的兴趣适当参加课外人文素质教育活动，参加活动的学分累计不少于3个学分。其中新增大学体育教学团队开设课外体育课程，高年级本科生必修，72学时，1学分，纳入第二课堂人文素质教育学分。

2.创新能力培养基本要求

德赢新版app在取得本专业教学计划规定学分的同时，还必须参加国家创新创业训练计划、广东省创新创业训练计划、SRP（德赢新版app研究计划）、百步梯攀登计划或一定时间的各类课外创新能力培养活动（如学科竞赛、学术讲座等），参加活动的学分累计不少于4个学分。

4.Second Classroom Activities

Second Classroom Activities are comprised of two parts, Humanities Quality Education and Innovative Ability Cultivation.

1) Basic Requirements of Humanities Quality Education

Besides gaining course credits listed in one's subject teaching curriculum, a student is required to participate in extracurricular activities of Humanities Quality Education based on one's interest, acquiring no less than three credits. The advanced undergraduates must complete one of courses of Humanities Quality Education which has seventy-two class hours (it's equivalent to one credit which belongs to Humanities Quality Education Credit of Extracurricular Class) offered by the College Physical Education Teaching Group.

2) Basic Requirements of Innovative Ability Cultivation

Besides gaining course credits listed in one's subject teaching curriculum, a student is required to participate in any one of the following activities: National Undergraduate Training Programs for Innovation and Entrepreneurship, Guangdong Undergraduate Training Programs for Innovation and Entrepreneurship, Student Research Program (SRP), One-hundred-steps Innovative Program, or any other extracurricular activities of Innovative Ability Cultivation that last a certain period of time (e.g. subject contests, academic lectures), acquiring no less than four credits.