Data Analytics Certificate

New certificate, expand your knowledge and skills in rapidly-developing and in-demand areas, including data and preprocessing, classification, clustering, association analysis, anomaly detection and more. 

Program Courses

  • CSC 116 - Information Computing, 3 credit hours
  • DAT 301 - Introduction to Data Analytics, 3 credit hours
  • DAT 310 - Data Analytics I, 3 credit hours
  • DAT 320 - Data Analytics II, 3 credit hours
  • DAT 330 - Data Analytics Capstone, 3 credits
  • MAT 156 - Statistics, 3 credit hours

Course Descriptions

  • CSC 116 - Information Computing
    This course presents the basic concepts of Information Systems (IS) and computer literacy. The course incorporates theory as well as hands-on practice, which focuses on spreadsheets and database management systems (DBMS). 

  • DAT 301 - Introduction to Data Analytics
    Through this course students develop an in-depth knowledge of the following statistics principles: Probability and odds, Binary Logistic Regression, Multinomial Logistic Regression, Principal component analysis, and Factor Analysis. 

  • DAT 310 - Data Analytics I
    Through this course students develop an in-depth knowledge of the following statistics principles: Review of Multiple Regression Analysis, Logistic Regression Analysis, Discriminant Analysis, Cluster Analysis, Time Series Analysis, and Forecasting Techniques.

  • DAT 320 - Data Analytics II
    Through this course students will learn the fundamental concepts of data mining and an extensive hands-on experience in applying the concepts to real world applications. Students will have an in-depth knowledge of the following statistics principles: Introduction to data mining, Data and Preprocessing, Classification, Clustering, Association Analysis, Anomaly Detection, and Data Mining Case Studies.

  • DAT 330 - Data Analytics Capstone
    Through this course students will learn the fundamental concepts of data mining and an extensive hands-on experience in applying the concepts to real world applications. Students will have an in-depth knowledge of the following statistics principles: Introduction to data mining, Data and Preprocessing, Classification, Clustering, Association Analysis, Anomaly Detection, and Data Mining Case Studies.

  • MAT 156 - Statistics
    This course is recommended for business, economics, mathematics, science and social sciences students. The course focuses on obtaining, presenting and organizing statistical data. Course topics covered include descriptive measures, probability, probability distributions, binomial distributions, normal distributions, sampling distributions, confidence intervals, hypothesis testing, linear regression, and correlation. A graphing calculator with statistics functions is required.