For each assignment you will post all of the files you created in the Assignment Section of Black board before the due date and time penalty of 10% for each day exists for late submissions. Your team will use the same data set for each team assignment unless specified to do otherwise. In case the number of the students is odd, the instructor will have the discretion to place the remaining student in a team whose team members should do everything possible to work together as a team of three. Thus, a substantial amount of your work will be finding a good team partner and making sure you do not disappoint your partner by not contributing. Problems within teams will not be solved by instructor involvement.
If you feel more comfortable, feel free to cc: your emails to me. It is to your best advantage to document (email) your communications to avoid complications, animosity, and blame games. If there are differences on these two basic criteria, chances are you will not collaborate effectively and there will be problems down the road. No instructor's involvement should be expected unless in the case of a student dropping from the class.Ĭhoose your partner carefully, identify if your goals in this course are common and if the level of commitment is the same. Finding a team partner is solely students' responsibility. The team should be of exactly 2 students. Team assignments will enforce a specific data mining method or principle. Each exam has a time limit of 90 minutes. Therefore, team assignments, discussions, class attendance and good note taking are essential elements for success. Usually, students will be asked to interpret results from applying a specific data mining method, such as confusion matrix and classification false positive/negative rates.
The exams will be multiple choice questions, administered on Blackboard during class and will cover the content from the text, material presented in the lectures and material from the team assignments. Commencing with several singular technique projects and concluding with the comprehensive semester project, students will reinforce their oral skills by way of presentations as well as written and critical thinking skills by the use of executive memos requiring quantitative analysis and evaluation. Working as a team, students will demonstrate proficiency in applying data mining analytical techniques on an advanced real world business problem that examines a large amount of data to discover new information in addition to analyzing and evaluating technique effectiveness with a less than perfect constantly evolving technology by presenting a self-designed semester project. Additionally, students will become familiar with and demonstrate proficiency in applications such as neural networks, linear regression, cluster analysis, market basket analysis and decision trees. Students will be introduced to advanced concepts such as data mining applications, data warehouses, web mining, text mining, and ethical aspects of data mining. Students will reinforce the learning of business intelligence concepts by means of data analysis techniques to make better business decisions through proper data preparation and simple tools for solving data mining problems. Detailed case studies and using leading mining tools on real data are presented. Course also identifies industry branches that most benefit from DM, such as retail, target marketing, fraud protection, health care and science, and web and e-commerce. Introduces the core concepts of data mining (DM), its techniques, implementation, and benefits.
Students should have access to Excel spreadsheet software and are assumed to be familiar at an intuitive level with general business practices of collecting, storing and using data.
Students should have a working knowledge of basic math (algebra) and Microsoft Excel. You can read more about XLMiner on the tool's web site:
There will be a request form to set up download arrangements and I will submit the form when all the students are ready to download the software. Students enrolled in the class will be able to download copies to their computers at no extra charge.
ISBN: 978-0-12-381479-1 Morgan Kaufman Publishers: 2011ĭata Mining Software - XLMiner® for Windows - a comprehensive data mining add-in for Excel, with neural nets, classification and regression trees, logistic regression, linear regression, Bayes classifier, K-nearest neighbors, discriminant analysis, association rules, clustering, and principal components analysis. Jiawei Han, Micheline Kamber and Jian Pei Text: Data Mining Concepts and Techniques, Third Edition