22 courses with the subject MSDS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MSDS 510Computer Programming Foundations for Data Science. . Pre-requisite(s): None3
Introduction to computer programming for data science using Python, R, and SAS. • Introduction to Python. Python syntax to write basic computer programs; Using the interpreter; Built-in and user-defined functions; Introduction to object-oriented programming in Python. • Introduction to R. Simple graphing; R Basics: variables, strings, vectors; Data Structures: arrays, matrices, lists, dataframes; Programming Fundamentals: conditions and loops, functions, objects and classes, debugging. • Introduction to SAS Programming. The SAS Operating Environment; SAS Programming Essentials: SAS Program Structure, SAS Program Syntax; Getting Data In and Out of SAS; Printing and Displaying Data; Introduction to SAS Graphics. There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSDS 515Data Conscientiousness. . Pre-requisite(s): None. Using Excel, JavaScript, Python3
SAS, SQL, and R to develop Data Conscientiousness: ability to immediately recognize the issues involved in data organization that will need to be addressed to tackle a specific problem. Developing skills in all of the preprocessing, scrubbing, cleaning tools (“search and rescue” operations), data imputation and handling of missing values, checking for adherence to data standards, and all of the rest of the time-consuming and dirty work of data projects. Linking structured and unstructured data sources and recognizing how to reshape data to get it into a computer-friendly format (i.e., rows and columns) required by analytical and statistical methods. A gentle introduction to statistics to enable understanding of the statistical difference between observations and variables, along with knowledge of the different scales of measurement so as not to end up with nonsensical analytical results. 3 credit hours, Spring & Summer. Pre-requisite(s): None. There are no pre- requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSDS 520Mathematical & Statistical Foundations for Data Science. . Pre-requisite(s)3
Undergraduate Calculus or Elementary Statistics. Techniques for building and interpreting mathematical models of real-world phenomena in and across multiple disciplines, including linear algebra, discrete 300
MSDS 535Further Mainstream Programming Languages for Data Science. . Pre3
requisite(s): MSDS 525. This course covers other useful mainstream programming languages for data science, beyond Python, R, SQL, and SAS. These “other” potential programming languages supplement the ability to crunch numbers and equip the data scientist with good all-round programming skills. Programming languages covered will vary depending on industry popularity. While some of the programming languages may not be covered in detail, examples include Java, Scala, Julia, MATLAB, JavaScript, TensorFlow, Go, Spark.
MSDS 545Introduction to Computational Software Engineering. . Pre- requisite(s): MSDS3
525. Introduction to systems development for computational science. Design, develop, and deploy a set of software components to produce a scalable, reliable, and reproducible experimental system for scientific investigation; Use a variety of approaches to software development team organization, and select techniques that are appropriate in different circumstances. MSDS 550 Computational Machine Learning. 3 credit hours. Pre-requisite(s): (MSDS 530 or MSBD 530), (MSDS 535 or MSBD 540). Introduction to machine learning with business applications. Survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, 301
MSDS 610Network and Graph Theory for Data Science. . Networks are discrete3
mathematical objects that describe systems of entities with pairwise relationship. Over the past several decades, technological advances in data collection and extraction have fueled an explosion of data in the form of networks from seemingly all corners of science. This course aims at providing the mathematical foundations of networks with a particular emphasis on their applications in modern data science, using tools from algorithmic graph theory and linear algebra. The topics include basic graph theory, network statistics, search algorithms, community detection, duality theorems and applications. The course will utilize python (e.g., 302
MSDS 620Signal Processing for Big Data. . This course introduces fundamentals of signal3
processing along with its applications in wearable sensor devices. The course includes topics on signal acquisition, techniques on processing the signals captured, including time domain approaches for event detection, time-varying signal processing for understanding the dynamical aspects of complex systems, and finally the application of machine learning algorithms to build predictive models for early insights.
MSDS 655AI in Cyber Security. . What is artificial intelligence (AI)? What does it mean for3
cybersecurity? And how AI can be integrated to achieve the goals of cybersecurity? This course designed to answer the above questions. In this course, a mix of key AI technologies will be introduced to support the understanding of the decision-making process when cybersecurity is concerned. The course will address key AI technologies in an attempt to help in understanding their role in cybersecurity. AI deficiently will complement and strengthen the cybersecurity practices and will improve their applications in enhancing our security.
MSDS 700Fundamentals of Database Management Systems. . Introduction to database3
concepts and the relational database model. Topics include ER Model, Relational Model, Relational Algebra, SQL, normalization, Indexing, Normal Forms, design methodology, DBMS functions, Security, Transaction Management, data-base administration, and other database management approaches such as client/server databases, object-oriented databases, and data warehouses. Strong emphasis on database system design and application development.
MSDS 710Mathematical and Statistical Theory. . This course will cover fundamental3
mathematical background for statistical theories. Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. The course will cover probabilities, multivariate distribution and special distribution, statistical inference, maximum likelihood methods, sufficiency, test of hypotheses, inference about normal methods, nonparametric statistics, Bayesian statistics.
MSDS 720Advanced Statistics. . Utilize current statistical techniques to assess and analyze3
biomedical and public health related data. Read and critique the use of such techniques in published research. Review of linear models, matrix algebra, and multiple analysis of variance. Introduction to random effects models, understanding and computing power for the GLM, GLM assumption diagnostics, transformations, polynomial regression, coding schemes for regression, multicollinearity. Determine what analytical approaches are appropriate under different research scenarios.
MSDS 725Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference &
Optimization. 3 credit hours. Study of Monte Carlo methods, a diverse class of algorithms that rely on repeated random sampling to compute the solution to problems whose solution space is too large to explore systematically or whose systemic behavior is too complex to model. Introduction to important principles of Monte Carlo techniques and their power. Bayesian analysis and Markov chain Monte Carlo samplers, slice sampling, multigrid Monte Carlo, Hamiltonian Monte Carlo, parallel tempering and multi-nested methods, and streaming methods such as particle filters/sequential Monte Carlo. Related topics in stochastic optimization and inference such as genetic algorithms, simulated annealing, probabilistic Gaussian models, and Gaussian processes. Applications to Bayesian inference and machine learning. Python or R for all programming assignments and projects. 303
MSDS 727Digital Image Processing and Understanding. . This course presents fundamental3
concepts and techniques in digital image processing and understanding. Both theoretical material and computing techniques are introduced. The analytical tools and methods which are currently used in digital image processing are introduced and applied to practical scenarios. Basic digital computing knowledge and programming skills are reinforced by solving real world problems. Computational studies may be performed in R or Python.
MSDS 730Deep Learning. . Deep learning is a sub-field of machine learning that focuses on3
learning complex, hierarchical feature representations from raw data. The dominant method for achieving this, artificial neural networks, has revolutionized the processing of data (e.g. images, videos, text, and audio) as well as decision making tasks (e.g. game-playing). Its success has enabled a tremendous amount of practical commercial applications and has had a significant impact on society. In this course, students will learn the fundamental principles, underlying mathematics, and implementation details of deep learning. This includes the concepts and methods used to optimize these highly parameterized models (gradient descent and backpropagation, and more generally computation graphs), the modules that make them up (linear, convolution, and pooling layers, activation functions, etc.), and common neural network architectures (convolutional neural networks, recurrent neural networks, etc.). Applications ranging from computer vision to natural language processing and decision-making (reinforcement learning) will be demonstrated. Through in- depth programming assignments, students will learn how to implement these fundamental building blocks as well as how to put them together using a popular deep learning library, PyTorch.
MSDS 740Big Data Privacy and Security. . Security issues related to the safeguarding of3
sensitive personal and corporate information against inadvertent disclosure; Policy and societal questions concerning the value of security and privacy regulations, the real world effects of data breaches on individuals and businesses, and the balancing of interests among individuals, government, and enterprises; Current and proposed laws and regulations that govern information security and privacy; Private sector regulatory efforts and self-help measures; Emerging technologies that may affect security and privacy concerns; and Issues related to the development of enterprise data security programs, policies, and procedures that take into account the requirements of all relevant constituencies; e.g., technical, business, and legal.
MSDS 750Individual Studies. Variable hours per semester may be offered (1–3
). This course provides students an opportunity to delve into a special study of interest related to data science selected by the student under the guidance of a faculty member. The student and faculty member meet weekly to discuss the studies; the student will be required to write a comprehensive review paper on the semester’s studies.
MSDS 870Literature Review. Variable hours per semester may be offered (1–3
). This course provides doctoral students with advanced research skills and strategies for conducting a literature review leading to a dissertation. Through this course, students will produce an extensive and integrative literature review related to their dissertation topic. Students will search, retrieve, summarize, and synthesize relevant studies to produce a comprehensive literature review.
MSDS 890Dissertation and Defense. , variable hours may be offered. The completion of12
Ph.D. dissertation is the culmination of the doctoral degree in this graduate program. The research topic of the dissertation must be related to the Ph.D. in Data Science program. ** For newer courses and programs not listed here, please see the program and course information listed on the school’s website for recent news and updates: https://sacsmeharry.org/. 305
