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Graduate Professional Development

 

Data Analytics Information PDF

 

MS in Data Analytics

Data Analytics

Data-driven Decision Making & Design (DA 6213)
Prerequisite: None.
This course familiarizes students with basic scientific processes and formalisms, such as question formulation and hypothesis development. Students will gain an understanding of how formulated questions and hypotheses can lead to data collection and analysis, as well as how data itself can be explored and summarized to generate such questions and hypotheses. The course also introduces students to foundational data analytics processes, such as the data-to-decision processes, data handling processes, and data analysis processes. Last, we discuss data provenance for data-to-decision traceability and critical scientific documentation principles important to scientific and analytic functions.

Data Analytics Tools & Techniques (DA 6223)
Prerequisite: None
Students will gain education and experience with common tools and techniques used in a variety of data analytics application areas.  Students will become familiar with database technology and leading commercial and open source analytics platforms.  Students will also learn how to use these technologies and platforms to solve data analytics problems by obtaining a basic understanding of database querying and basic scripting in analytics platforms. Students will not become scientific programmers from this course, nor will they learn the formalisms of programming per se; rather, they will learn and experience how to develop functional scripts and leverage existing analytics libraries to solve data analytics problems using software. 

Data Analytic Visualization and Communication (DA 6233)
Prerequisite: None
Since the purpose data analytics is to inform and facilitate better data-driven decisions, and transform data to information and knowledge, the ability to effectively communicate data aggregations, summarizations, and analytic findings to decision makers is very important. The ability to communicate highly complex analyses and scientific findings to a non-technical audience is challenging. This course will educate students on common mistakes and success factors in technical communication, and give them experience communicating findings orally and in writing. The course will also focus heavily on data analytics visualization approaches and tools.  Students will learn common methods for data visualization for a wide variety of data types and data analytics applications.

Data Analytic Applications (DA 6813)
Prerequisite: None
Students will obtain a big picture understanding of data analytics, including its purpose, common benefits and challenges, important analytic processes, and what is needed to perform data analytics, such as skills, tools, technology, etc.  Students will be introduced to a wide variety of data analytics applications in a wide variety of fields, such as information technology, cyber security, bioinformatics, biomedical/health, insurance and risk, finance, economics, accounting, business intelligence, crime and fraud detection, marking and customer analytics, energy and environmental, manufacturing and operations, and logistics and supply chain. Data analytics applications will be demonstrated through case-based study and guest lectures from data analytics experts and managers in the various application areas listed above.

Data Analytic Practicum I (DA 6823)
Prerequisite: DA 6813, STA 6443, DA 6213
This course equips students with practical knowledge, skills, and experience needed to conduct real-world, high-quality data analytics in an application area of interest. Students will meet formally with their peers and the instructor for the purpose of facilitating the practicum experience.  Students will simultaneously engage in formal internships and regular meetings with key members of the organizations hosting and facilitating student practicum project(s).  During this practicum, students will engage in the following steps of the data analytics process: problem defining, question formulation, hypothesis development, preliminary analytics, analytical design, data acquisition, data preparation and pre-processing, and initial data analysis.

Data Analytic Practicum II (DA 6833)
Prerequisite: DA 6823
This course continues the practicum experience in the same manner as Data Analytics Practicum I.  Students will continue and finish their major data analytics project, focusing on the analysis and presentation of results portion of the process.  The next steps will be detailed data analysis, conclusion drawing, report preparation and refinement, presentation preparation and final presentation. The practicum will culminate in a formal, completed report to the supporting organization, as well as to data analytics peers and professors.

Information Systems (IS)

Data Foundations (IS 6713)
Prerequisite: None.
The ability to understand, store, process, transform, cleanse, fuse, and share data is critical to data analytics, and it can often be the most challenging and/or most time consuming part of the data analytics process, due to the vast variety of data sources, types, and formats. This course equips students to collect/process common types of data used in data analytics, and provides them a solid understanding of various data sources, types, and formats, and how to handle and process each.  Topics include, but are not limited to: structured vs. unstructured data; data compression, encodings, and character sets; and common metadata in use today, such as geospatial data, temporal data, and linked data (e.g. social network linkages). Students will learn how to store, process, transform, cleanse, fuse, and share data.  Exemplar data will be used extensively in the course, so that students see and experience a wide variety of data and understand how to process and handle it.  Data handling exercises will be provided in the context of scenario based problems, to further improve their educational knowledge, practical skill set, and contextual understanding.

Big Data Technology (IS 6733)
Prerequisite: None
Data set size and the computer intensive nature of many analytic processes are necessarily driving data analytics tasks to the cloud – both for large scale, economic storage, and for economic distributed computing power.  The course will not focus on the in-source vs. out-source nature of the cloud infrastructure, nor the system and network maintenance thereof.  Rather, the course will teach students how and when to use distributed computing and cloud-based platforms, how to set-up, configure, use, and maintain the data, specialized data analytics software, and big data processes in these environments regardless of their physical location. Students will also gain experience with using common cloud-based data analytics platforms, as well as big data indexing, search, and retrieval platforms.

Statistics (STA)

Data Algorithms I (STA 6443)
Prerequisite: Basic statistics or equivalent
Introduction of basic statistical methods, with specific emphasis on predictive modeling algorithms. Topics include exploratory data analysis, including certain graphical methods, extracting important variables and detecting outliers; regression methods, including linear and nonlinear models; analysis of variance (ANOVA) methods, including classification models, fixed and random effects, interactions, and multiple comparisons; and multivariate analysis, including principal components analysis and factor analysis. Students will gain an understanding of when to apply and how to select various predictive modeling algorithms for various types of problems, as well as data assumptions and requirements for algorithm use, proper parameter setting, and interpreting results.

Data Algorithms II (STA 6543)
Prerequisite: STA 6443
More statistical methods, with specific emphasis on data segmentation and text analytics. Topics include classification methods, including correlation analysis, clustering analysis, association analysis, and support vector machines; network techniques including Bayesian networks, neural networks, link analyses, and decision trees; and text analytics, including text mining and extraction, natural language processing, and sentiment analysis.  Other topics may include social network analysis, trend analysis, time series methods, robust statistics and survival analysis. Students will gain an understanding of when to apply and how to select various predictive modeling algorithms for various types of problems, as well as data assumptions and requirements for algorithm use, proper parameter setting, and interpreting results.

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For more information about this program contact Program Director Max Kilger or call our Graduate Programs office at (210) 458-4641

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