Data Analytics Minor

Data Analytics applies fundamental scientific principles to the analysis of large, complex data sets. This minor will interest students with an aptitude in computer science, mathematics and especially statistics. This minor complements students studying computer science, bioinformatics, cybersecurity and management information systems. It is particularly suited to be an application area for students majoring in applied mathematics.

Required Courses for the Data Analytics Minor

A student must successfully complete, at Fontbonne, a minimum of 50 percent of the credit hours required for the minor.

  • MTH 151

    Calculus with Analytic Geometry II

    4 credits

    A continuation of MTH 150, continuation of differential and integral calculus; infinite series. Prerequisite: MTH 150 with grades of C- or better.

  • MTH 200

    Linear Algebra

    3 credits

    Topics include: vector spaces, linear transformations, and matrices. Pre- or co-requisite: MTH 151.

  • MTH 315

    Advanced Statistics

    3 credits

    Covers widely used statistical tools such as linear and nonlinear regression, analysis of variance, expected mean squares and pooling. Students will use a statistical package to analyze data sets. Prerequisites: (MTH 115 or MTH 125) and MTH 150 or consent of instructor.

  • MTH 325

    Theory and Applications of Probability

    3 credits

    Introduces the mathematical treatment of random phenomena occurring in the natural, physical, and social sciences. Topics include combinatorial analysis, binomial distribution, Poisson and normal approximation, random variables and probability distributions, generating functions, Markov chains applications. Prerequisitess: MTH 115 or MTH 125 and MTH 150

  • CIS 161

    Computer Science II

    4 credits

    Continuation of Computer Science I, with extensive programming in C++ language and introduction to Java. Includes string handling, file I/O, storage and static variables, structures, bitwise operations, and C++ library. Students in this course who are majoring in mathematics, computer science, cyber security or management information systems must earn grades of B- or better to progress to CIS 210 and above. Prerequisite: CIS 160.

  • CIS 330

    Database Management Systems

    3 credits

    Fundamental design principles of database systems. Implementational design using data models, relational algebra, and relational calculus. Relational implementation with SQL, Microcomputer DBMS, and distributed database systems. Prerequisite: CIS 111 and CIS 161.

  • BNF 200

    Bioinformatics Programming Languages

    3 credits

    Covers an introduction to scripting languages and their applications to biological data (currently Python programming language and the R-statistical analysis program will be taught). Prerequisites: (MTH 125 or MTH 115) and BIO 112 (or an introductory biology course which can be concurrent).

  • MTH/CIS 335

    Survey of Data Analysis and Visualization

    3 Credits

    Provides a survey of the concepts and skills associated with data analytics and visualization. The course will focus on probability sampling and complex survey data collection methods used to analyze and visualize data collected in statistical surveys. Mobile, online and multi-mode surveys will be the sources of the data collection process. Application areas such as marketing and social engineering will be studied, along with the ethical considerations one must keep in mind when interpreting data. Prerequisites: BNF 200 and MTH 325 (or co-requisite). SP (odd)

  • MTH/CIS 455

    Machine Learning I

    3 Credits

    Covers data analysis methods to recognize trends and patterns of big data. The emphasis will be on classification, regression and model fitting. Application areas such as healthcare, finance, game playing, marketing and internet fraud detection will be studied. (3 credits) Prerequisites: MTH 115 and BNF 200. FA (odd)

  • MTH/CIS 470

    Machine Learning II

    3 Credits

    Focuses on deep learning, which is a subset of machine learning, to predict the characteristics of data based on the hierarchical structure of the data. Deep learning covers advanced, supervised/unsupervised methods of classification and regression, structured prediction and anomaly detection with an emphasis on the development of the algorithms used to simulate high-level abstractions of data based on low-level layers. Models such as unsupervised Bayesian, Inference procedures and non-parametric models will be discussed. Prerequisites: MTH 455. SP (even)

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