Masters Degree Data Analytics
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To satisfy the Computational Data Analytics Track, students must have taken CS 7641 or CSE/ISYE 6740 as one of their Statistics, Track, or Additional Electives. Database Systems Concepts and Design (CS 6400) Study of fundamental concepts with regard to rational databases. Topics covered include database design, query processing, concurrency control, and recovery. Credit not given for both CS 6400 and CS 6754. Information Visualization (CS 7450) Study of computer visualization principles, techniques, and tools used for explaining and understanding symbolic, structured, and/or hierarchical information. Includes data and software visualization. Students cannot receive credit for both CS 7450 and CS 4460. Theoretical/computational foundations of analyzing large/complex modern datasets, including the fundamental concepts of machine learning and data mining needed for both research and practice Cross-listed with CSE 6740. Computational Science and Engineering Algorithms (CSE 6140) This course will introduce students to designing high-performance and scalable algorithms for computational science and engineering applications.
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Deterministic Optimization (ISYE 6669) An introduction to deterministic optimization methodologies including approaches from linear, discrete, and nonlinear optimization including algorithms, computations, and a variety of applications. Special Note Select at least two courses from the list. Digital Marketing (MGT 6311) Become familiar with the key concepts and techniques utilized in modern digital marketing. Understand the primary characteristics of various online channels including mobile marketing, email marketing, and social media marketing. Gain awareness of important concepts and best practices in the use of digital marketing tools (search engine optimization, pay-per-click advertising, etc. ). Data Analysis for Continuous Improvement (MGT 8803) Financial Modeling (MGT 8803) Special Note The number of elective hours required depends on how many Introductory Core courses (if any) are replaced with electives. Students who take two of these additional electives from the Track Electives list of either the Business Analytics track or the Computational Data Analytics track will satisfy the requirements for two tracks.
Data Analytics in Business (MGT 6203) Teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, and challenges related to analyzing business data. Special Note Select two courses from the list. Machine Learning/Computational Data Analytics (CS 7641 or CSE/IYSE 6740) Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications. Time Series Analysis (ISYE 6402) Basic forecasting and methods, ARIMA models, transfer functions. Nonparametric Data Analysis (ISYE 6404) Nonparametric statistics and basic categorical data analysis. Design and Analysis of Experiments (ISYE 6413) Analysis of variance, full and fractional factorial designs at two and three levels, orthogonal arrays, response surface methodology, and robust parameter design for production/process improvement. Regression Analysis (ISYE 6414) Simple and multiple linear regression, inferences and diagnostics, stepwise regression and model selection, advanced regression methods, basic design and analysis of experiments, factorial analysis.
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Master degree data analytics salary
Master of Science in Data Analytics Degree Program Overview University of the Potomac is pleased to offer the newly added Master of Science in Data Analytics among our roster of career-driven master degree programs. This program prepares individuals to use a variety of statistical and quantitative methods, computational tools, and predictive models to help businesses, nonprofits, and government agencies in forecasting, risk assessment, making critical decisions, and ultimately, enabling them to be more successful in a wide range of data-rich environments. Master of Science in Data Analytics Program Learning Goals Graduates of the Master of Science in Data Analytics program will be able to: Design, implement, populate and query relational databases for operational data. Design, implement, populate and query data warehouses for informational data. Harness very large data sets to make business decisions. Evaluate the use of data from acquisition through cleansing, warehousing, analytics, and visualization to the ultimate business decision.
Master's degree data analytics
Computational Statistics (ISYE 6416) This class describes the available knowledge regarding statistical computing. Topics include random deviates generation, importance sampling, Monte Carlo Markov chain (MCMC), EM algorithms, bootstrapping, model selection criteria, (e. g. C-p, AIC, etc. ) splines, wavelets, and Fourier transform. Bayesian Statistics (ISYE 6420) Rigorous introduction to the theory of Bayesian Statistical Inference. Bayesian estimation and testing. Conjugate priors. Noninformative priors. Bayesian computation. Bayesian networks and Bayesian signal processing. Various engineering applications. Data Mining and Statistical Learning (ISYE 7406) Topics include neural networks, support vector machines, classification trees, boosting, and discriminant analyses. Special Note Select one course from the list. Simulation (ISYE 6644) Covers modeling of discrete-event dynamic systems and introduces methods for using these models to solve engineering design and analysis problems. Probabilistic Models (ISYE 6650) An introduction to basic stochastic processes such as Poisson and Markov processes and their applications in areas such as inventory, reliability, and queuing.
The course focuses on algorithms design, complexity analysis, experimentation, and optimization, for important science and engineering applications. Web Search and Text Mining (CSE 6240) Basic and advanced methods for web information retrieval and text mining: indexing and crawling, IR models, link and click data, social search, text classification and clustering. Big Data Analytics in Healthcare (CSE 6250) Analysis of variance, full and fractional factorial designs at two and three levels, orthogonal arrays, response surface methodology, robust parameter design for production/process improvement. Applied Analytics Practicum (MGT 6748) Practical analytics project experience applying ideas from the classroom to a significant project of interest to a business, government agency, or other organization. More Special Note Select two courses from the list. All C-track students must take CSE/ISYE 6740 or CS 7641 as either a Statistics elective or a C-track elective. An introduction to deterministic optimization methodologies including approaches from Special Note Select at least two courses from the list.
Masters degree in data analytics degree
Great! Here's What You Need To Do: General Admissions Requirements Complete an admissions interview conducted in person or via online methods. Sign and submit an attestation of high school (or equivalent) completion. Equivalencies include a GED Certificate. Home schooled students must present a diploma that meets the requirements of the state in which it was issued. (Students with non-US credentials please see International Student Admissions Requirements below). Submit a completed application Arrange for official transcripts from all colleges/universities previously attended to be submitted to the Office of Records and Registration, University of the Potomac. Submit grade reports or scores from any recognized college equivalency examinations (e. g., CLEP, DANTES, and Advanced Placement). Submit certificates from any corporate education training or professional development programs. (Note: An ACE evaluation form may be required to determine appropriate credit for corporate educational training. )
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- Masters degree data analytics
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