Courses

Courses are offered on a Fall, Spring and Summer Semester schedule. The Fall and Spring Semester includes three concurrent courses, Each class is held once per week, for three hours. The Summer Semester has one course. It will take two years to complete the program.

MSDS 510 Computer Programming Foundations for Data Science

3 credit hours, Spring, Summer. Pre-requisite(s): None.

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 515 Data Conscientiousness

3 credit hours, Spring & Summer. Pre-requisite(s): None. There are no pre-requisites for this course.

Using Excel, JavaScript, Python, 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.

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.

MSBD 520 Introduction to Biostatistics

3 credit hours, Fall, Spring. Pre-requisite(s): Elementary Statistics.

Principles of biostatistics and the analysis of clinical and epidemiological data. Descriptions and derivations of statistical methods as well as demonstrations of these methods using SAS. Topics include basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and nonparametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance.

MSDS 525 Data Management Foundations for Data Science

3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 510, 515.

The concepts and structures used to store, analyze, manage, and present (visualize) information and navigation using Python, SQL, SAS, and QGIS. Topics will include information analysis and organizational methods, and metadata concepts and applications. Students will be assisted to identify disparate data sources needed to perform analysis for a given real-world problem. Typically, data from a single source will not be adequate to perform the required analysis. Students will pull data from the disparate data sources and import it into SAS and use several SAS procedures to detect invalid data; format, validate, clean the data; and impute the data if it is missing. This will prepare the data for statistical analysis and decision modeling in SAS.

• Python Lists, Sets, Strings, Tuples, and Dictionaries; Reading and manipulating CSV files, and the Numpy library; Introduction to the abstraction of the Series, Pandas, and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as Groupby, merge, and pivot tables effectively.
• Introduction to Databases and basic SQL; Using string patterns and ranges to search data and to sort and group data in result sets; Working with multiple tables in a relational database using join operations; Using Python to connect to databases and then create tables, load data, query data using SQL, and analyze data using Python.
• Introduction to Data Step in SAS; Processing Data in Groups; Manipulating Data with Functions; Data Extraction and Preparation, Concatenating, Merging and Interleaving Tables; Using SQL in SAS to query and join tables.
• Preparing comprehensive plans to manage spatial and non-spatial health-related data; building versioned enterprise databases; and knowing how to implement best practices for managing databases for health projects and organizations.

MSBD 530 Statistical Methods for Biomedical Data Science

3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 510, (MSDS 520 or MSBD 520).

Principles of biostatistics focusing on statistical modeling approaches to the analysis of continuous, categorical, and survival data. Regression modeling includes the links between regression and analysis of variance (parameterization), multiple regression, indicator variables, use of contrasts, multiple comparison procedures and regression diagnostics. The course will generalize these modeling concepts to different types of outcome data including categorical outcomes (i.e., logistic and log-linear modeling) and survival outcomes (i.e., proportional hazards analysis). Students are taught to conduct the relevant analysis using SAS and R.

Students also choose one of two Concentration Courses, MSBD 535 or MSBD 536.

MSBD 535 Precision Medicine Informatics

3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 525, (MSDS 530 or MSBD 530).

This course will focus on the inherent translational informatics challenges, concerns, and opportunities afforded by precision medicine to provide a more accurate, personalized characterization of patient populations based on various characteristics including molecular (e.g., genomic, proteomic), clinical (e.g., comorbidities), environmental exposures, lifestyle, patient preferences and other information. Informatics is a necessary component to tackle precision medicine. This includes managing Big data, advanced concepts of a huge variety of genomic sequencing datasets emerging in the post-genomic era from several sequencing platforms, creating learning systems for knowledge generation, providing access for individual involvement, and ultimately supporting the optimal delivery of precision treatments derived from translational research.

MSBD 536 Population Health Informatics

3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 525.

Uses a problem- or inquiry-based learning approach where students will take the lead in designing and implementing data- and technology-driven projects that generate data analytics-based solutions for complex public health issues and develop useful data-driven decision strategies. Students will also reproduce and replicate several case studies that illustrate the power of technologies like GIS, GPS, drones, spatial narratives, and web visualization in population health. They will examine the impact of technology on population health informatics and vice versa.