2021年《大数据管理与挖掘》省级线下一流课程

 2022年《大数据管理与挖掘》校级线上线下一流课程

课程编号：054304

课程性质：专业核心课

开课单位：管理学院管理工程系

总学时：48

理论学时：32

实验学时：16

上机学时：0

学分：3

内容简介：

性质：数据挖掘是信息与计算科学专业的专业课程，本课程以数据挖掘为主要内容，讲述实现数据挖掘的各主要功能、挖掘算法和应用，并通过对实际数据的分析更加深入地理解常用的数据挖掘模型。培养学生数据分析和处理的能力。

地位：《大数据管理与挖掘》既是一门跨学科的课程，又是一门专业理论、方法、实践都很强的课程，本课程介绍大数据管理分析的基本概念。

基本任务：本课程的任务是使学生达到如下水平：①理解掌握大数据管理信息、大数据管理系统、数据仓库、数据挖掘与分析、等大数据管理信息系统的基本概念；②掌握数据仓库与OLAP、数据预处理、挖掘频繁模式、关联和相关、分类与预测、聚类分析等具体数据挖掘方法③掌握数据组织和处理的基本理论与方法；④初步掌握大数据挖掘的几种常用方法；⑤理解掌握大数据分析信息系统的基本思路和具体步骤。

核心教学内容：数据挖掘是一个强大的工具，可以帮助你找到数据中的模式和关系。但是数据挖掘自己本身不会工作，它还是需要了解你的业务、你的数据和懂得一些分析方法。数据挖掘可以发现数据中的一些隐藏的信息，但是它无法告诉你所在的企业这些数据的价值。为了确保有意义的数据挖掘结果，你必须懂得你的数据。具体课程主要内容包括数据仓库与OLAP、数据预处理、挖掘频繁模式、关联和相关、分类与预测、聚类分析等具体数据挖掘方法。

基本要求：①使学生掌握数据挖掘的基本概念和基本理论；②掌握数据挖掘的数据预处理、数据挖掘、后处理的知识体系；③掌握管理数据组织与处理的基本方法；④初步掌握几种常用的数据挖掘的软件使用方法；⑤初步具备一定的大数据挖掘的分析与设计能力。

教学方法：采用“教学训一体”方式教学。

考核所占比例：以闭卷考试方式进行考核，考核环节由作业、实验、课堂参与度（发言讨论等）、期末考核4个部分组成，占比分别为10%、30%、10%和50%。

先修课程：高等数学、大数据仓储技术、数据库系统原理、概率论与数理统计

撰写人：邵景峰

Course Big data management and mining

Course ID: 054304

Course Type: Professional core courses

Offered by(School):Management Science and Engineering

Total Hours:48

Teaching Hours:32

Experimental Hours:16

Using Computer Hours:0

Credit:3

Course Description:

Nature: This course belongs to the interdisciplinary subject of data mining, information science and management science, and it is systematic science. Its research object is the scientific management and use of big data information. Big data management and mining is the most important technical information of enterprises and institutions.

Status: Big data management and mining is not only an interdisciplinary course, but also a course with strong professional theory, method and practice. This course introduces the basic concepts of big data management and analysis.

Basic tasks:

The task of this course is to enable students to reach the following levels:

1) Understand and master the basic concepts of big data management information system, big data management system, data warehouse, data mining and analysis, etc.;

2) Master data warehouse and OLAP, data preprocessing, mining frequent patterns, association and correlation, classification and prediction, clustering analysis and other specific data Mining methods;

3)  Mastering the basic theories and methods of data organization and processing;

4) Preliminarily mastering several common methods of big data mining;

5) Understanding and mastering the basic ideas and specific steps of big data analysis information system.

Core content: Data mining is a powerful tool that can help you find patterns and relationships in data. But data mining itself will not work, it still needs to understand your business, your data and some analysis methods.

Data mining can find some hidden information in the data, but it can't tell your company the value of the data. To ensure meaningful data mining results, you must understand your data. The main contents of the course include data warehouse and OLAP, data preprocessing, mining frequent patterns, association and correlation, classification and prediction, clustering analysis and other specific data mining methods. Core content: data mining is a powerful tool that can help you find patterns and relationships in data. But data mining itself will not work, it still needs to understand your business, your data and some analysis methods. Data mining can find some hidden information in the data, but it can't tell your company the value of the data.

To ensure meaningful data mining results, you must understand your data. The main contents of the course include data warehouse and OLAP, data preprocessing, mining frequent patterns, association and correlation, classification and prediction, clustering analysis and other specific data mining methods.

Basic requirements:

1)  Enable students to master the basic concepts and theories of data mining;

2)  Master the knowledge system of data preprocessing, data mining and post-processing of data mining;

3) Master the basic methods of data organization and processing;

4)  Preliminarily master the use methods of several commonly used data mining software;

5) Have a certain ability of analysis and design of big data mining.

Teaching method: "Teaching and Training integration" teaching method.

The proportion of assessment: Closed book examination is adopted, the total score consists of homework, experiment, classroom participation (speech and discussion, etc.) and final assessment, accounting for 10%, 30%, 10% and 50%, respectively.

Prerequisite Course: Advanced mathematics, Big data storage technology, Database system principle, Probability theory and mathematical statistics

Written by: Shao Jingfeng