Final Project and Term Paper Requirements Qiang Yang, MTM521 Material.

Slides:



Advertisements
Similar presentations
Project Analysis Course ( ) Final Project Report Overview.
Advertisements

By: MSMZ. Objective After completing this chapter, you will be able to: Explain 2 contract review stage List the objective of each stage of the contract.
Evaluating Decision Support Systems Projects. Who Evaluates Technical Managers  Chief Information Officer,  Corporate IT professionals,  Database administrators,
All rights reserved © Altec ExoMars 2018 Rover Operations Control Centre Plans for rover operations training at ROCC M. Cinato.
Knowledge Translation Curriculum Module 3: Priority Setting Lesson 2 - Interpretive Priority Setting Processes.
Planning a measurement program What is a metrics plan? A metrics plan must describe the who, what, where, when, how, and why of metrics. It begins with.
EECS 349 Machine Learning Instructor: Doug Downey Note: slides adapted from Pedro Domingos, University of Washington, CSE
Chapter Three THE RESEARCH PROCESS
Introduction to WEKA Aaron 2/13/2009. Contents Introduction to weka Download and install weka Basic use of weka Weka API Survey.
Team Composition and Team Role Allocation in Agile Project Teams Brian Turrel 30 March 2015.
Unit 15 Assessment in Language Teaching. Teaching objectives By the end of the lesson, students should be able to:  know what assessment is and how it.
Business Intelligence: Essential of Business
© 2008 Prentice Hall11-1 Introduction to Project Management Chapter 11 Managing Project Execution Information Systems Project Management: A Process and.
GUHA method in Data Mining Esko Turunen Tampere University of Technology Tampere, Finland.
Data Mining: Concepts & Techniques. Motivation: Necessity is the Mother of Invention Data explosion problem –Automated data collection tools and mature.
CSCI 347 / CS 4206: Data Mining Module 01: Introduction Topic 03: Stages in Data Mining.
Advisor: Hsin-Hsi Chen Reporter: Chi-Hsin Yu Date:
Kansas State University Department of Computing and Information Sciences CIS 830: Advanced Topics in Artificial Intelligence From Data Mining To Knowledge.
AICT5 – eProject Project Planning for ICT. Process Centre receives Scenario Group Work Scenario on website in October Assessment Window Individual Work.
INFO 330 Class Project Do it for real. Overview of the Class Project Scope – Approximately the same as the sample project – Standard starting place Marketing.
CHM1303 SOLIDS AS ADVANCED POLYMER MATERIALS This course can be taken by students with basic knowledge in Organic Chemistry and Polymer Science! TOPICS:
Project Management Methodology Project Closing. Project closing stage Must be performed for all projects, successfully completed or shut off by management.
CS525 DATA MINING COURSE INTRODUCTION YÜCEL SAYGIN SABANCI UNIVERSITY.
Lecture 11 Managing Project Execution. Project Execution The phase of a project in which work towards direct achievement of the project’s objectives and.
Methodology Qiang Yang, MTM521 Material. A High-level Process View for Data Mining 1. Develop an understanding of application, set goals, lay down all.
Programmes of Work Revision lesson 5 Tuesday 29 th March.
2014 ML Project2: Goal: Do some real machine learning; learn you to use machine learning to make sense out of data. Group Project—4 (3) students per group.
The CRISP Data Mining Process. August 28, 2004Data Mining2 The Data Mining Process Business understanding Data evaluation Data preparation Modeling Evaluation.
PLANNING ENGINEERING AND PROJECT MANAGEMENT By Lec. Junaid Arshad 1 Lecture#03 DEPARTMENT OF ENGINEERING MANAGEMENT.
Advanced Database Course (ESED5204) Eng. Hanan Alyazji University of Palestine Software Engineering Department.
Project Management and Risk. Definitions Project Management: a system of procedures, practices, technologies, skills, and experience needed to manage.
1 IMM472 資料探勘 陳春賢. 2 Lecture I Class Introduction.
Data Warehousing Lecture-30 What can Data Mining do? Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research.
Prepared by: Mahmoud Rafeek Al-Farra College of Science & Technology Dep. Of Computer Science & IT BCs of Information Technology Data Mining
10 Aug 2010 ECE/BENG-493 SENIOR ADVANCED DESIGN PROJECT Meeting #2.
October 2-3, 2015, İSTANBUL Boğaziçi University Prof.Dr. M.Erdal Balaban Istanbul University Faculty of Business Administration Avcılar, Istanbul - TURKEY.
Summary „Data mining” Vietnam national university in Hanoi, College of technology, Feb.2006.
1 Introduction to Data Mining C hapter 1. 2 Chapter 1 Outline Chapter 1 Outline – Background –Information is Power –Knowledge is Power –Data Mining.
Introduction to Data Mining by Yen-Hsien Lee Department of Information Management College of Management National Sun Yat-Sen University March 4, 2003.
SHORT TERM GOALS Change Strategies according to students requirements Focus to provide latest knowledge.
What is project management?
Sotarat Thammaboosadee, Ph.D. EGIT563- Data Mining Individual Project Specification 1.
Technical Track Work Group Report Out August 22, 2012.
Method comparison: for Situational Method Engineering Mohssen Ali.
1 SBM411 資料探勘 陳春賢. 2 Lecture I Class Introduction.
1 IMM472 資料探勘 陳春賢. 2 Lecture I Class Introduction.
Project Management Methodology Project Closing. Project closing stage Must be performed for all projects, successfully completed or shut off by management.
1 SBM411 資料探勘 陳春賢. 2 Lecture I Class Introduction.
Approaches to Component 3: Interpreting Theatre Written exam.
Project Management The End Stage
Course Project Guidelines
Project Management The Roles and Responsibilities of a Project Manager
Presented by Khawar Shakeel
Introduction to Data Mining
MIS 451 Building Business Intelligence Systems
Who is this?.
BSCOM 336 RANK Lessons in Excellence--bscom336rank.com.
Data Mining: Concepts and Techniques Course Outline
RESEARCH TOOLS FOR UNDERSTANDING SPORTS CONSUMERS
כריית מידע -- מבוא ד"ר אבי רוזנפלד.
L.O. – What do we have to do in Unit 25?
Unit 5 – eProject – Starting to look at projects Unit 5
Requirements Validation – I
AICT5 – eProject Project Planning for ICT
Techniques.
ECE/CSE 576 Assignment 4 Course Project Spring 2019.
Technical Communication
Asst. Prof. Sotarat Thammaboosadee, Ph.D.
CSE591: Data Mining by H. Liu
Presentation transcript:

Final Project and Term Paper Requirements Qiang Yang, MTM521 Material

The Project Proposal Should involve the whole data mining process  Objective definition and scoping  Data collection process  Data preprocessing and cleaning process  Data mining process  Evaluation process  Reporting and Interpretation process  Final analysis of the issues discovered in the data mining process

Proposal Stage Proposal should be short and concise Should specify the objectives Should specify the potential issues and difficulties Should scope out the data format and questions/features in the data Should list the queries to be answered* Should list the type of knowledge to be discovered Should list the candidate data mining techniques chosen Should allocate tasks to each person in the team Should have a work schedule Due at the end of the first Sunday afternoon (2-3pm), with a short presentation

Final Report Due (June 10, 5pm) by . Should include a discussion of each stage in data mining process  The anticipated result and the final result  The expected issues and the final issues  The technical results and the lessons learned  Should involve each of the main data mining functions Data cleaning and reduction Classification by at least two methods Association analysis of the data Clustering analysis of the data  A (revised) data mining process according to your experience Important: discuss what you can learn from the data mining results, with which rudimentary data analysis cannot provide you.