硕博研究生课程

（全英语）

大数据智慧管理与

分析机制基础

   大数据（Big Data）是海量、动态、多样化、爆炸式增长的信息财富。学习掌握大数据特点，研究并掌握针对性、革新性的数据管理和处理机制，才能解决大数据应用面临的挑战，发挥大数据的威力，挖掘大数据中蕴含的智慧，使基于大数据的决策更快速、更合理、更准确，从而提升教育智能、商务智能，以及社会各行各业的智慧能力。 
   本课程通过介绍大数据智慧管理机制必备的基础知识和技术，使研究生入门大数据领域，理解大数据的特点与挑战；学习掌握数据仓库、OLAP、NoSQL与NewSQL知识体系和技术原理；理解掌握数据仓库、HBase与Hive技术的系统结构、模型设计、工作机制、优劣对比和应用策略。通过特设的课程设计，提升学生大数据技术的应用实践能力。

  秋季学期

  每周2学时

计算机学院

本科生课程

     本课程是计算机科学与技术、软件工程以及信息类相关专业的一门专业技术基础必修课。

    内容包括现实世界数据抽象，基本数据模型，数据库系统的结构和组成，关系数据库理论，数据库设计过程。课程侧重深入地阐述关系数据库理论及规范化理论，数据库的设计，操作语言，操作优化，并发控制，数据库安全及完整性控制等。使学生掌握数据库的基本理论、数据库的组织和结构，数据库系统的设计和开发方法，了解当前数据库的最新技术及最新发展。实验教学着重有关数据库应用系统的开发方法和基本技术。

    通过课程学习，学生理解并掌握数据库系统关键技术，具有开发基于数据库的信息系统应用的基本能力。

  春季学期

  每周3学时

  +独立实验课

  每周4学时，

  共9周

Databases

（Yan Zhu, Kelvin）

Module summary:      Databases are a common component of many computer systems, storing and retrieving data about the state of a system. This module covers the principles of the design, architecture, implementation of database systems and the role of database management systems. In order to understand the design of database system an understanding of relational theory is required as well as the tools and techniques for decomposing systems and modelling them in an appropriate manner.This module introduces the tools for manipulating data in databases and design principles that ensure data security and integrity.

Objectives:     This module provides a foundation in the design and implementation of databases with an emphases on relational database systems.

Learning outcomes:      On successful completion of this module a student will have demonstrated the ability to: - describe the purpose and architecture of database management systems. - use appropriate tools to manipulate database systems. - design and implement a database using appropriate tools. - apply relational modelling techniques to real world situations. - apply normalisation and explain the advantages and disadvantages of normalisation. - describe the ethical, legal and security related issues concerning the implementation and administration of databases and their management systems.

   Spring

   Semester

   3h/week, 

   10 weeks

Data Mining

（Eric, Yan Zhu)

Module summary:      This module explores the knowledge discovery process and its application in different domains such as text and web mining. You will learn the principles of data mining; compare a range of different techniques and algorithms and learn how to evaluate their performance.

Objectives:     On completion of this module, students should be able to: -Identify all of the data, information, and knowledge elements, for a computational science application. -understand the components of the knowledge discovery process -understand and use algorithms, resources and techniques for implementing data mining systems; -understand techniques for evaluating different methodologies -demonstrate familiarity with some of the main application areas; -demonstrate familiarity with data mining and text analytics tools.

Learning outcomes:      On completion of the year/program students should have provided evidence of being able to: -demonstrate a broad understanding of the concepts, information, practical competencies and techniques which are standard features in a range of aspects of the discipline; -apply generic and subject specific intellectual qualities to standard situations outside the context in which they were originally studied; -appreciate and employ the main methods of enquiry in the subject and critically evaluate the appropriateness of different methods of enquiry; -use a range of techniques to initiate and undertake the analysis of data and information; -adjust to professional and disciplinary boundaries; -effectively communicate information, arguments and analysis in a variety of forms;

   Spring

   Semester

   3h/week, 

   10 weeks