DATA ANALYTICS AND TESTING (8 SCU)

Learning Outcomes:

After completing the course, students will be able to: Capture, categorize, simplify, normalize and prepare data to be processed, work with and analyze large data sets, visually represent analysis’s conclusions to technical and non-technical audiences use the most common algorithms, to make sense of large amounts of data, which are applicable to most business and management problems, Frame and organize business questions in ways that can be answered through Big Data analysis and meet the information needs of a project, Identify types of questions for which data analysis cannot provide accurate information, Acquire, process, and analyze extremely large data sets using data mining methods to discover patterns or do data exploration, Install, run, and apply machine learning tools to different kinds of data, Formulate the machine learning process to answer domain-specific problems, Interpret the results of data analysis and data mining to make predictions and to establish the reliability of those predictions, Communicate results to management and other non-technical audiences and build predictive models using either Weka or R.

Topics:

These students will learn how to analyze large data sets and identify patterns that will improve any company’s and organization decision-making process, a professional portfolio of projects and real experience with data analysis that will give students the necessary confidence to be successful as a Data Analyst. Topics include Pre-processing Data (Filters, Missing Values), Data Mining, Decision Trees, Classification / Regression Algorithms, Presentation Skills to non-technical Audience, Normalization, Distance, Correlation, Machine Learning, Compare Items (k-NN/IBk), Predictive Revenue Model (k-NN, M5P…), Class Prediction Model (J48, k-NN), Deep Analytics and Visualization using R, Big Data – Web Mining with WEKA.

Prerequisite(s): None