数据挖掘网课代修 | ISMM1-UC 742 Business Intelligence Syllabus

本次美国网课代修主要为数据挖掘BI项目相关，以下为该门课程的Syllabus

Course Description:
This course focuses on concepts and techniques that are used to analyze raw data and convert it to useful information.
Main focus will be placed on internal processes that take place before data is analyzed, on analysis of data and post analysis interpretation of results. At the end of the course students will be able to analyze data and assist primary decision makers with knowledge-based decisions.

Regarding this Iteration:
This iteration focuses on the concepts and techniques that are used with: Weka, R, Microsoft BI, Tableau or any other research software. Some of the topics include: data aggregation, data manipulation, decision trees Fuzzy Logic and many other algorithms and processes. At the end of the course students will have full understanding of best practices and approaches that are currently used as industry standards.

Course Structure:
The class will meet on Wednesdays for 150 minutes (from 6:20 p.m. to 8:50 p.m.). Each class session will begin with a lecture covering the weekly topic. Each session will have a 10-minute break. The course will have lab and lecture materials.

The goal of this course is to introduce students to concepts and techniques of data mining. This course will introduce and explain concepts such as Neural Networks, Trees, Binning, Bayesian Classification and many others. Students will use these concepts and techniques and apply them to their own data sets. Students will perform all steps that are required to successfully mine information from data sets. Final outcome is discovery and presentation of business-related knowledge that should be useful to any corporation. Students will be required to present a specific case where they analyzed a specific data set, discovered useful information, conducted predictive learning and used many concepts described in class. Upon completion of the course students are expected to know the following:

• Understand the full process of data mining and data warehousing
• Have working knowledge of different data mining software packages
• Select and apply appropriate methods and techniques and perform data mining
• Be able to describe concepts like binning, decision trees and predictive analysis

Perform actual data mining from the data source selected by studentsCourse Expectations:

You are expected to attend all sessions from start to finish. If you fail to come to a session, arrive more than 20 minutes into a session, leave more than 10 minutes prior to the end of class or leave class for any other purpose for more than 20 minutes you will be considered absent for that class session. If you accumulate more than four absences (excused or unexcused) regardless of your performance in the course, you will not be able to meet the course expectations and will fail the course.

You are also expected to complete all labs and to do so by the assigned due dates. Assessments must be submitted solely through the Assignments function on NYU Classes.
The instructor will provide at his discretion up to one makeup examination.

Readings / Textbook Information:
The required text for this course that is available through the NYU Bookstore is:

[1] Ian H. Witten. 2011. Data Mining: Practical Machine Learning Tools and Techniques. Third Edition. Morgan Kaufmann. ISBN: 978-0123748560

Students can use later versions of this book as well.

Software

For the most part, we will be using the following software: 1) WEKA, 2)Tableau, 3)Microsoft BI, 4) SPSS or other relevant applications. While this software will be available on lab computers should you choose to use personal machines, you will be required to download and install the software on your own. The instructor is available for consultation, but you are ultimately responsible for such endeavors. Note that all software that we are using is accessible without charge through the respective developer websites.

Assessment

The categories for assessment and weights for each category are:
Class Participation = 10% (with a maximum of 2% awarded per class session)
Midterm Examination = 30%
Final Examination = 30%
Project = 30%