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.
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
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
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:
 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.
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.
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%
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