Undergraduate Admission
    Graduate Admission

    Data Analytics, IT 527

    About this Course:
    This is a hands-on course that focuses on the creation, maintenance, and analysis of large informatics databases. Concepts such as data modeling, probability, linear regression, and statistical data analysis are covered in depth. In addition, this course will use large simulated equities, healthcare, insurance, and banking database systems. The student is expected to have a working understanding of relational database concepts as well as SQL.

    Course Features

    Introduction to indexing and retrieving information for reporting and business intelligence. Database organization, coding and indexing for specific industries, i.e. financial and healthcare. This course currently utilizes Oracle technologies.

    Currently Scheduled

    Prerequisites:
    Relational Database and SQL experience required for enrollment.

    Course Outline:
    Creating Tables
    Creating Oracle Tables;
    Oracle Datatypes;
    SQL Plus ;
    Creating an Oracle Table;
    Populating an Oracle Table;
    Java Code to Create DML

    SQL Review
    Oracle Aggregate Functions;
    SQL GROUP BY/HAVING Clause;
    SQL Subselect;
    SQL Examples


    Working with the Internet
    Historical Pricing (ATT);
    Oracle Spooling;
    Oracle HTTP Requests;
    Basic PL/SQL Structure


    Introduction to Quantitative Statistical Systems
    Statistical symbols;
    Standard Deviation/Variance;
    Computing Standard Deviation;
    Distribution

    Distribution and Introduction to Randomness
    Normal Distribution;
    Central Limit Theorem;
    Empirical Rule;
    Distribution
    Two Properties of Randomness;
    Properties of Random Numbers;
    Random Variables and Statistics

    ?Skewness and Kurtosis
    Skewness and Kurtosis;
    Skewness Examples;
    Online Histogram

    ?Covariance and Correlation Coefficient
    Covariance Defined;
    Mean Vector and Covariance Matrix;
    Covariance and Correlation;
    Correlation Coefficient Graph;
    Covariance and Correlation charts

    ?Introduction to Regression
    Regression Analysis;
    Slope and Intercept;
    Least Squares;
    Regression Chart


    CEU:
    4.5

    Instructor:
    Bob Hendry