IST 718: Big Data Analytics

This course offers a broad introduction to analytical processing tools and techniques for information professionals. Students develop a portfolio of resources, demonstrations, recipes and examples of various analytical techniques while growing their specialization in one or more areas of interest. Students will learn to obtain, screen, clean, link, manipulate, analyze and display data while creating summaries, overviews, models, analyses and basic tables, histograms, trees and scattergrams. They will use Python and Apache Spark to explore classic and modern machine learning techniques (such as deep learning) within a big data context, including sentiment analysis via supervised learning, recommendation systems via unsupervised learning and predicting credit scoring via random forest machine learning.

Learning Objectives:

  • Understand complex data structures, transformation of data structures and manipulation of data elements
  • Understand essential analysis techniques, including descriptive statistics, summarization and elementary modeling
  • Understand scripting methods, including debugging methodologies, for handling data in R and other tools
  • Appreciate the range of applicability of information analytics to real problems in areas such as business, science and engineering
  • Develop ability to match available analytical methodologies to the information needs of clients and users and present results in a meaningful way

Tools & Concepts:

  • Machine Learning
  • Python (Apache Spark, Hadoop 2.0, Spark ML, Pandas, Matplotlib)
  • Describing and modeling data sets
  • Volume and velocity of data
  • Data analytics and inferencing
  • Efficient debugging and problem solving

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