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The Master of Science Concentration in Analytics combines the mathematical and statistical training of the traditional MS in Statistics with enhanced computational and data analytic training for those planning careers in information intensive industries or research. The program includes fundamental training in mathematical and applied statistics as well as specialized training in data management, analysis, and model building with large datasets and databases. The specialized courses have an emphasis on statistical computing, data management, and statistical learning, which encompasses the more statistical topics that fall under the broader title of data mining. Students are encouraged to gain experience in a business or consulting environment as part of the program.

The prerequisites for the program include calculus through multivariable calculus, linear algebra equivalent to MATH 257, and an introduction to mathematical statistics and probability equivalent to STAT 400. Students in this program should also have prior exposure to computing using business software, statistical software such as SAS or SPSS, and an interactive programming environment such as C, R or Matlab.

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The concentration requires completing 10 courses, organized around five broad areas of expertise. The first course in probability and statistics, STAT 410, may be waived for students entering with credit for the same or an equivalent course. The 10 required courses by area are described below, and a flat list of courses can be found in the Academic Catalog.

STAT 410 is a course in probability and mathematical statistics and prepares students for STAT 510, a practical advanced graduate level course mathematical statistics course. These courses form the foundation for statistical inference that is encountered throughout the remainder of the curriculum. STAT 410 may be waived for students entering with credit for the same or an equivalent course.

The choice of STAT 425 or STAT 527 provides thorough coverage of linear regression and data analysis that is fundamental for further study in analytics. STAT 527 is the more advanced course required for PhD students. The second course is a selection of one of several traditional courses in foundational areas of statistics.

STAT 542 is an advanced course in statistical learning that covers stat-of-the-art and proven methods for classification, clustering, model selection, and predictive modeling in the context of large data sets. The second advanced analytics course is a choice of advanced statistical computing theory, multivariate analysis, data mining, and machine learning courses.

The major in statistics and analytics is an excellent program for students interested in statistical data science, operations research, and actuarial science, as well as in fields such as business, economics, public policy and health, psychology, and biomedicine, where the decision and statistical sciences play an increasingly important role.

For more information on hospitalizations visit the Health Data Reporting System (HDFR). For HFDR and Vital Statistics data tables and reports, please visit Data and Statistics. For information and data on cancer cases in Alaska please visit the Alaska Cancer Registry or contact cancerregistry@alaska.gov. For all other Health Analytics programs please contact us at healthanalytics@alaska.gov.

The Master of Science in Data Science and Analytics (MSDSA) program at Kennesaw State University (KSU) is a professional degree program which prepares a diverse student body to utilize cutting edge data science and analytics techniques for careers in business, industry, government, and health care. Students graduate with the essential knowledge, techniques, and tools gained from working with faculty who integrate real-world data into their courses and multiple opportunities to work directly with corporate partners.

The MSDSA program is a 36 semester-hour graduate program designed to help students gain the skills, knowledge, and experiences they need to be practicing data scientists and analytics professionals. Our students come from wide variety of academic and professional backgrounds, but by the time they graduate all are prepared for a career in data science and analytics. Highlights of the program include:

Below are the latest available statistics on the work-at-home/telework population in the U.S. based on our analysis of the 2005-2019 American Community Survey (ACS, a U.S. Census Bureau product). New ACS numbers, for the prior year, are typically released each Fall but due to the pandemic, the government may not have data for 2020.

BASIC STATISTICS: Whether the basic level of statistics logging, including report, document, and cube executions, user sessions, project sessions, prompt answers, subscription deliveries, and history list message executions and modifications, in enabled. Returned as a boolean value.

Computation is a fundamental component of data analytics, and computing is fully integrated into the statistical methods courses from the first semester. You will learn both R and Python and develop the critical thinking skills you need to adapt to innovations in technology post-graduation.

Erin Blankenship is a professor in the Department of Statistics. Her areas of expertise include nonlinear fixed- and mixed-effects models, ecological statistics and statistics education. Since joining the university in 1999, she has placed a high priority on students by improving not only her own teaching methods, but also making her colleagues better teachers in the process.

Two main statistical methodologies are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena. To make an inference upon unknown quantities, one or more estimators are evaluated using the sample.

I will not go into great detail here since my last post described Predictive Analytics, but I will summarize a description of analytics. Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, machine learning, computer programming and operations research to quantify performance or predictions. Analytics often favors data visualization to communicate insight.

Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation, the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Finally, analytics refers to the field of data science that involves making predictions about future events. By using different statistical techniques like linear regression an algorithm analyses past data to make predictions about the future events.

Our researchers are nationally recognized experts in data analytics. Their work includes investigating statistical problems arising in privacy and security analytics, the statistical analysis of literary style, and the statistical foundations of geometric and topological data analysis.

Anand Vidyashankar focuses on statistical problems arising in privacy and security analytics. He is collaborating with scientists from McKesson Corporation to identify sources of risk and statistical methods to measure and mitigate risk in real-time environments. The work involves integrating aspects of regularity guidelines with novel statistical methods in ultra-high dimensions to develop next-generation privacy and security guidelines.

Prerequisites:\r\n\r\n6.431x\r\n14.310Fx or 6.419x\r\n18.6501x\r\n6.86x\r\n \r\n\r\nDescription: Solidify and demonstrate your knowledge and abilities in probability, data analysis, statistics, and machine learning in this culminating assessment.

This MicroMasters program in Statistics and Data Science (SDS) was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach comprised of four online courses and a virtually proctored exam that will provide you with the foundational knowledge essential to understanding the methods and tools used in data science, and hands-on training in data analysis and machine learning. You will dive into the fundamentals of probability and statistics, as well as learn, implement, and experiment with data analysis techniques and machine learning algorithms. This program will prepare you to become an informed and effective practitioner of data science who adds value to an organization.

From working on projects, to entering competitions, Cavalytics aims to be the driving force in pursuing statistical projects, learning about advancements in the field of statistics, and workshopping to understand the ins and outs of the world of data. ff782bc1db