Summer 2017 Predictive Analytics  (CSCI-GA 3033)

CSCI-GA 3033 Predictive Analytics - Course Webpage

  • Agenda and Topics

    Predictive Analytics Lifecycle


    Defining Analytics Problems


    Understanding Datasets


    Data Pre-processing algorithms


    Data Dimensionality Reduction 


    Feature Selection


    Data Similarity Measures


    Data Clustering Algorithms

                         Partitioning Algorithms
                         Hierarchal Algorithms
                         Density-based Algorithms

                         Biologically Inspired Algorithms

    Data Classification Algorithms

    Decisions Trees
    Support Vector Machines
    Naïve Bayes Classification Algorithm
    Neural Networks
    Linear Regression

    Mining Association Rules

    Sentiment Analysis

                  Recommender Systems

    Collaborative Filtering

    Content based Filtering

    Trust based Recommendations

    Predictive Analytics Use-cases

    Introduction to Large-scale Data Analytics Frameworks

                                Hadoop and MapReduce
                                Introduction to Apache Spark                                                                                                                                                                                            
    Please visit the course webpage to learn more details.
                                                                                                                                                                                         
     


  • Course Work

    Final grades for the course will be determined using the following weights:

                  15% Assignments

                  20%  Analytics Project

                  25% Midterm

                  30% Final Exam

                  5% Quizzes and Participation

                  5% Term Paper

  • Course Mailing List & Other Business

    Late Submission of Assignment

    Programming assignments must be uploaded before or on the due date. There will be a 10% loss for every day late submission.
    Assignments that are submitted three days after the original due date will NOT be accepted.
    In case of an emergency that prevents you from submitting your homework on time, please notify the intrsuctor of the course -- Otherwise the penalty will apply to the homework's grade.  

    Course Mailing List and NYU Classes

    NYUClasses is available for this class. You will need to check NYU Classes regularly for class notes. You will also be recieving regular emails from the intstructor about course notes, grades and guidelines.

    Academic Integrity

    Every student must submit her or his own work.
    All references used in the assignment must be cited.
    Please review  the department policy  that also applies to this course.