Data Science and Analytics

Data science is a rapidly blossoming field of study and career with a highly multidisciplinary characteristic. The confluence of big data, massively powerful cloud computing platforms, and need of businesses from all sectors to leverage their data repositories has created a high-growth environment and demand for data scientists. Data scientists routinely leverage tools and techniques from computer science, information systems, advanced statistics, and machine learning. To satisfy the growing need for data scientist who can transform large collections of data into actionable decision making products for their employers, we are proposing the Master of Science in Data Science and Analytics.

This multidisciplinary Data Science and Analytics (DSA) degree program will consist of 34-credit hours of learning in the online and mixed mode format in which  students will  visit  campus  one time  each  academic year  for an intensive  on site learning experience.

The academic program will consist of 19 credit hours of core, fundamental data science courses; followed by 9 credits of emphasis area specific courses and 6 credits of industry relevant case studies and capstone project courses.

Data Science is an emerging discipline that, by its nature, integrates traditional disciplines. The proposed degree program will leverage prior investments in the computing disciplines across campuses and colleges within each campus. The MU Informatics Institute will coordinate this collaborative degree program by leveraging existing courses from Computer Science, Journalism, and Information Science & Learning Technologies Departments to deliver the various core and emphasis area course. Existing courses will be adapted to the online format, and new courses that are properly focused and structured for the DSA program will be developed.

Professor 
Associate Professor 
Assistant Professor 

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Graduate Faculty Member - membership is required to teach graduate-level courses, chair master's thesis committees, and serve on doctoral examination and dissertation committees.

**

Doctoral Faculty Member - membership is required to chair doctoral examination or dissertation committees.  Graduate faculty membership is a prerequisite for Doctoral faculty membership.

While MU does not offer undergraduate degrees specifically in Data Science and Analytics, the University does offer baccalaureate opportunities in a number of related areas.  

A listing of current degree programs can be found here.

INFOINST 7001: Topics in Informatics

This course provides the overview to the informatics foundations as well as introduces topics regarding the current informatics-driven areas of science. Graded on A-F basis only.

Credit Hours: 3


INFOINST 7002: Introduction to Informatics

This course provides an overview to informatics foundations in addition to introducing topics regarding the current informatics-driven areas of science. Topics to include: recent trends in informatics; database management and Big Data analytics; data visualization, bioinformatics, health informatics, geoinformatics, nursing informatics, social informatics, and legal informatics. Graded on A-F only.

Credit Hours: 3
Prerequisites: Instructor's consent


INFOINST 7010: Computational Methods in Bioinformatics

(same as CMP_SC 7010). Fundamental concepts and basic computational techniques for mainstream bioinformatics problems. Emphasis placed on computational aspect of bioinformatics including formulation of a biological problem, design of algorithms, confidence assessment of software development. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: CMP_SC 4050 and STAT 4710


INFOINST 7430: Introduction to Health Informatics

(same as HMI 7430). Introduction to the use of clinical information systems in healthcare. Topics include clinical data, standards, electronic medical records, computerized provider order entry, decision support, telemedicine, and consumer applications. Graded on A-F basis only

Credit Hours: 3
Prerequisites: departmental consent required


INFOINST 7600: Introduction to Data Science and Analytics

An introductory course in data science and analytics. The objective of the course is to give students a broad overview of the various aspects of data analytics such as accessing, cleansing, modeling, visualizing, and interpreting data. Students will perform hands-on learning of data analytic topics, using technologies such as Python, R, and open source analytic tools. Two Big Data cyberinfrastructure platforms will be introduced through case studies, allowing students to perform data analytical learning modules on modern cloud infrastructure and other relevant technologies. Graded on A-F basis only.

Credit Hours: 3
Recommended: Basic programming experience and Basic database experience


INFOINST 8001: Topics in Informatics

Organized study of selected topics. Subjects and earned credit may vary from semester to semester. Repeatable upon consent of department. Graded A-F basis only.

Credit Hours: 3


INFOINST 8005: Applications of Bioinformatics Tools in Biological Research

This service course is designed for bioinformatics non-major students from life sciences, biological sciences, plant sciences, animal sciences, biochemistry, medicine fields and others. This course will provide an introduction to the current state of the art topics in bioinformatics and the computational tools available to the research community for application to biological research questions. Students will learn how to effectively utilize the tools and software packages to analyze data and visualize the results. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: Instructor's consent
Recommended: Graduate students are expected to have basic knowledge in algorithms, databases, and molecular biology


INFOINST 8010: Problem Solving in Bioinformatics

(same as CMP_SC 8110). The course covers a variety of bioinformatics research topics such as biological sequence comparison, protein structure prediction, protein and gene function prediction, and inference and modeling of biological networks. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010


INFOINST 8085: Problems in Informatics

Independent, directed study on a topic in the area of informatics. Some sections may be graded A-F or S/U.

Credit Hour: 1-6
Prerequisites: Instructor's consent required


INFOINST 8087: Seminar in Informatics

Students attend and/or present at informatics seminars approved by the institute. Graded on S/U basis only.

Credit Hour: 0.5-1
Prerequisites: instructor's consent required


INFOINST 8088: Lab Rotations in Informatics

This course is designed to train students in both computational/informatics and life science/hospital laboratories to foster critical research collaborations in biomedical informatics. Students are expected to write reports with their advisors and the mentor of the rotation. Graded on S/U basis only.

Credit Hour: 1-3


INFOINST 8090: Dissertation (pre-candidacy) Research in Informatics

Research leading to dissertation before comprehensive examination. Graded on S/U basis only.

Credit Hour: 1-99


INFOINST 8150: Integrative Methods in Bioinformatics

(same as CMP_SC 8150), Introduces the most popular experimental methods from the point of view of the information sources that can be used. Students will use data obtained directly from biological experiments and learn how to suggest new experiments to improve results. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010


INFOINST 8180: Machine Learning Methods for Biomedical Informatics

(same as CMP_SC 8180). Teaches statistical machine learning methods and applications in biomedical informatics. Covers theories of advanced statistical machine learning methods and how to develop machine learning methods to solve biomedical problems. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: CMP_SC 7050 and INFOINST 7010 or CMP_SC 7010 or INFOINST 7005


INFOINST 8190: Computational Systems Biology

(same as CMP_SC 8390). This course covers current theories and methods in the modeling and analysis of high-throughput experiments such as microarrays, proteomics, and metabolomics. Topics include the inference of causal relations from experimental data and reverse engineering of cellular systems. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010; INFOINST 8010


INFOINST 8210: Structural Bioinformatics of Proteins, Complexes, System

(same as CMP_SC 8120). Main course objective is to provide an introduction to the state-of-the-art methods in structural bioinformatics. The course will cover the methods that are applied to a wide range of biomolecular objects from protein domains and small proteins to large biological systems. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010
Recommended: CMP_SC 4050 or CMP_SC 7050


INFOINST 8310: Computational Genomics

(same as CMP_SC 8130). This course introduces computational concepts and methods of genomics to students. The course covers genome structure, database, sequencing, assembly, annotation, gene and RNA finding, motif and repeats identification, single nucleotide polymorphism, and epigenomics. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010


INFOINST 8350: Integrative Methods in Bioinformatics

Course objective is to introduce the most popular experimental methods from the point of view of the information sources that can be used in. Students will learn to use data obtained directly from biological experiments and how to suggest new experiments to improve results. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010


INFOINST 8390: Computational Systems Biology

This course covers current theories and methods in the modeling and analysis of high-throughput experiments such as microarrays, proteomics, and metabolomics. Topics include the inference of casual relations from experimental data and reverse engineering of cellular systems. Graded A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7010 or CMP_SC 7010; INFOINST 8010 or instructors consent


INFOINST 8450: Precision Medicine Informatics

(same as PTH_AS 7450). This course will introduce students with the theoretical and practical aspects of precision medicine informatics. Topics include: complex diseases, computational genomics/proteomics, informatics of molecular interactions and biological pathways, somatic mutations, signal transduction and cancer, biomarker discovery, machine learning and data mining for PMI, networks methods for PMI, knowledge representation and reasoning for PMI. The course will consist of a set of didactic lectures, computational assignments, in-class demonstrations of PMI methods and discussions of recent publications.

Credit Hours: 3
Prerequisites: INFOINST 8005 with C or better or INFOINST 7010 with C or better or instructor's consent


INFOINST 8610: Statistical and Mathematical Foundations for Data Analytics

An intermediate statistics class designed to build the mathematical foundation for students dealing with Big Data phenomena. Topics include discussions of probability, data sampling, data summarization, sampling distributions, statistical inference, statistical pattern analysis, hypothesis testing, regression, and nonparametric inference over multidimensional data collections. Students will engage in Big Data projects using various publicly available data sets and leveraging modern Data Science tools, techniques, and cyberinfrastructure. Graded on A-F basis only.

Credit Hours: 3
Recommended: Basic understanding of mathematical principles of vectors and matrices, and Basic course in probability and statistics


INFOINST 8620: Database and Analytics

Covers the Fundamental concepts of current database systems and query methods with emphasis on relational model and non-relational techniques in Big Data environments. Topics include entity-relationship model, relational algebra, indexing, query optimization, normal forms, tuning, security, NoSQL, and data analytics skills in both relational and non-relational environments. Project work involves modern relational DBMS systems and NoSQL environments. Graded on A-F basis only.

Credit Hours: 3
Recommended: Basic understanding of mathematical principles of vectors and matrices, and Basic course in probability and statistics


INFOINST 8630: Data Mining and Information Retrieval

The course introduces the main concepts and techniques of data mining and information retrieval. It covers a variety of data mining topics and methods to extract hidden and predictive patterns from large data collections. Furthermore, theory and techniques for the modeling, indexing, and retrieval of relational, non­relational, text­based and multimedia databases is covered. Topics include introduction to data mining process, mining frequent patterns, and pattern analysis, as well as different information retrieval models and evaluation, query languages and operations, and indexing/searching methods. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7600 and INFOINST 8620
Recommended: Basic understanding of mathematical principles of vectors and matrices; Basic course in probability and statistics; Basic course in databases and data analytics


INFOINST 8640: Big Data Security

This course provides an overview of state-of-the-art topics in Big Data Security, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security). Securing sensitive data, personal data and behavioral data while ensuring a respect for privacy will be a focus point in the course Graded on A-F only.

Credit Hours: 3
Prerequisites: INFOINST 7600 and INFOINST 8620


INFOINST 8650: Big Data Visualization

Covers the Fundamental concepts of current visualization concepts and technologies. Unlike many data visualization courses, this one focuses on principles of visualization design and the grammar of graphics. These principles are then implemented in popular contemporary visualization technologies. Students will develop an advanced knowledge of the appropriate selection, modeling, and evaluation of data visualizations. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: INFOINST 7600 and INFOINST 8620
Recommended: Basic understanding of mathematical principles of vectors and matrices; Basic course in probability and statistics; Basic course in databases and data analytics


INFOINST 8660: Data and Information Ethics

Introduces the ethics related to Big Data in industry, business, academia, and research settings. Students will learn the social, ethical, legal and policy issues that underpin the big data phenomenon. Discussions and case studies will help guard against the repetition of known mistakes and inadequate preparation. The course content will follow the guidelines to be developed by the Council for Big Data, Ethics, and Society. Graded on A-F basis only.

Credit Hour: 1
Prerequisites: INFOINST 7600 and INFOINST 8650


INFOINST 8810: Research Methods in Informatics

(same as HMI 8810). Research Methods in Health and Bioinformatics is a writing intensive course that provides students with an understanding of research proposal development, literature searching, research synthesis, research designs, evaluation methods, and ethics. Graded A-F basis only.

Credit Hours: 3
Prerequisites: Second semester or later in PhD program or instructor's consent


INFOINST 8860: Content Management in Biomedical Informatics

(same as CMP_SC 8160). This course introduces theory and techniques for content extraction, indexing, and retrieval of biomedical media databases. Topics include biomedical media databases, feature extraction methods, advanced database indexing structures, query methods, and result visualization. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: CMP_SC 7380 and INFOINST 7010


INFOINST 8870: Knowledge Representation in Biology and Medicine

(same as HMI 8870) The main topics presented in the course are: logic systems, knowledge representation methods, production systems and representation of statistical and uncertain knowledge. Graded A-F basis only.

Credit Hours: 3
Prerequisites: HMI 7430 and HMI 7440


INFOINST 8880: Machine Learning Methods for Biomedical Informatics

(same as CMP_SC 8180) This course teaches statistical machine learning methods and their applications in biomedical informatics. The course covers theories of advanced statistical machine learning methods and teaches how to develop machine learning methods to solve biomedical problems. Graded on A-F basis only.

Credit Hours: 3
Prerequisites: CMP_SC 7050 and INFOINST 7010 or CMP_SC 7010 or INFOINST 7005


INFOINST 9090: Dissertation (post-candidacy) Research in Informatics

Research leading to Ph.D. dissertation after comprehensive examination. Graded on S/U basis only.

Credit Hour: 1-99