西南交通大学教师主页朱焱--中文主页--教学工作

教学工作

当前位置: 中文主页 >> 教学工作

教学成果

教学业绩 - Teaching Achievement

指导计算机学院-计算机科学与技术专业罗俊豪同学本科毕业设计，荣获西南交通大学2021届“百篇优秀本科毕业设计（论文）”。
所指导的计算机学院2018级研究生陶霄获得西南交通大学2021届“优秀毕业生”。
指导2020-2021年度大学生科创省级SRTP项目"面向学术合作者网络图数据的分析处理系统"，以“优秀”成绩结题。
获得 2017-2020年度，西南交通大学，研究生教育教学改革成果一等奖。
获得 2017-2020年度，西南交通大学，本科教育教学改革成果三等奖。
2020年1月-2020年12月, 完成教育部-华为产学合作协同育人项目，成功结题。
录制全英文“慕课”课程《The Basics of NoSQL and NewSQL Big Data Management Mechanism》。
该课程面向海内外具有计算机科学与技术基础的本科生、硕博研究生或从事计算机专业的技术人员。介绍NoSQL与NewSQL大数据管理机制的基础知识与技术，帮助学生学习掌握大数据知识和技术原理；深入探讨HBase与Hive技术的系统结构、工作机制与应用领域；基于应用实例的讨论，学习理解HBase和Hive技术的应用实践。
撰写发表两篇教改论文“直面‘云课堂’教学挑战，实施有力有效改革措施”“数据库课程思政建设教学案例分析”，收录于《西部工科高等教育教学改革论文集》并于2020年12月正式出版。
指导三位研究生参加2020年“华为杯”第十七届中国研究生数学建模竞赛，获得全国二等奖。
指导两组共五位研究生参加西南交大第十一届课外创新实验竞赛活动，分别获得优秀作品铜奖和优秀奖。

主持课程简介

课程与教学活动

— Course and Student Project

诚邀积极有为的学生加入我的团队！

如果你对我的研究领域或大学生实践项目充满好奇心和探究欲，寻找软件研发的乐趣，愿意全面提升个人专业能力，希望成功推进你的研究生学业、毕业设计或大学生实践活动，请联系我。

目前承担的各类课程

课程类别	课程名称	内容描述	课程时间
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	数据库原理	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
交大-利兹学院课程	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
交大-利兹学院课程	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

课程类别

课程名称

内容描述

课程时间

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

数据库原理

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

交大-利兹学院课程

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

交大-利兹学院课程

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;