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A00-204 - Sas warehouse architect concepts - Dump Information

Vendor : SASInstitute
Exam Code : A00-204
Exam Name : Sas warehouse architect concepts
Questions and Answers : 65 Q & A
Updated On : April 25, 2019
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A00-204 Questions and Answers

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A00-204 Sas warehouse architect concepts

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A00-204 exam Dumps Source : Sas warehouse architect concepts

Test Code : A00-204
Test Name : Sas warehouse architect concepts
Vendor Name : SASInstitute
Q&A : 65 Real Questions

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SASInstitute Sas warehouse architect concepts

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SAS Named respectable records Analytics accomplice of the Washington Redskins | killexams.com Real Questions and Pass4sure dumps

CARY, NC, Sep 17, 2015 (Marketwired via COMTEX) -- The Washington Redskins have some of the most loyal lovers within the NFL. To deliver those supporters with the top-quality adventure -- on and off the box -- the Redskins have chosen SAS as their authentic data Analytics associate. The Redskins plan to make use of SAS® Analytics as part of their company operations to enrich fan perception and engagement.

"Our fans are the top-rated in the NFL and we want to supply them the best possible experience when they interact with the Washington Redskins," spoke of Terry Bateman, executive vp and CMO of the Washington Redskins. "We're always searching for brand new tips on how to improved serve our clients and we suppose the SAS tools are among the many gold standard in the business and will make us a more valuable organization."

"The Redskins are all about their fanatics and look to SAS to help them enhanced serve their loyal customer base," mentioned Jim Tobin, countrywide revenue government, SAS sports business observe. "enthusiasts today recognize what they need and predict their favorite crew to be capable of carry a personalized and world-class event. The SAS utility will supply the Redskins the tools they need to be in a position to deliver that adventure."

The Redskins might be the usage of SAS advertising and marketing Automation to construct a centralized facts warehouse and design advertising and marketing courses that enchantment to fans on a person level. SAS clever advertising can be used to ship communications and promotions across multiple channels, including their many really good sites and cellular apps.

SAShelps skilled activities groups around the world such as the Orlando Magic, the Toronto Maple Leafs, The big apple Mets, fundamental League Soccer and others, increase fan insight and engagement, operations, player management and advertising and marketing

sas.com/information

About SAS

SAS is the leader in analytics. through inventive analytics, business intelligence and facts administration application and capabilities, SAS helps purchasers at greater than 75,000 websites make stronger selections sooner. due to the fact 1976, SAS has been giving customers worldwide THE power to know®.

SAS and all different SAS Institute Inc. product or service names are registered logos or logos of SAS Institute Inc. in the us of a and other nations. ® indicates united states registration. different manufacturer and product names are trademarks of their respective groups. Copyright © 2015 SAS Institute Inc. All rights reserved.

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Big data benefits accelerate at Catholic Health Initiatives | killexams.com real questions and Pass4sure dumps

Catholic Health Initiatives makes no small plans, because it really can’t—the Englewood, Colo.-based health system has more than 85,000 employees and operates 86 hospitals in 18 states, along with 40 long-term and assisted living facilities as well as multiple nursing colleges, home health agencies and academic medical centers.

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Catholic Health Initiatives’ current big data focus is on population health, but it takes a slightly different tack than many of its peers. Instead of only trying to collect bits and pieces of information about what’s going on with its patient population outside its walls, the health system is also focused on improving its inpatient performance using the value-based purchasing metrics created by the Centers for Medicare and Medicaid Services: decreasing mortality rates and hospital-acquired infections, and improving satisfaction for patients in acute-care settings.

“Most population health efforts focus only on getting data about what’s happening to patients in the outpatient settings, but we’ve found that we needed to focus on both inpatient and outpatient care, and use data that exists within our systems and that we have access to—in most cases, we cannot get reliable access to the data from affiliated physicians and the multitude of EHRs they’re using,” Reichert says. “We feel that by improving our performance in our environment we can make the biggest impact in terms of overall care quality and costs.

“What’s unfortunate is that the healthcare community is treating population health as an outpatient phenomenon, when it reality, it is both and needs to be treated as a continuum. They consider what’s happening out there as separate to what’s happening in the hospitals. But they’re both part of the same care continuum.”

While Catholic Health Initiatives is focused on the “inside” of its clinical operations, it’s conversely looking outside its data infrastructure to fuel its population health efforts. Instead of trying to wrangle all the data streams itself, it has partnered with a handful of vendors to normalize and standardize its information, which is then fed into its data warehouse to create single sources of truth for the primary data it uses for population health analytics.

The organization has a contract with Premier, Charlotte, N.C., to clean Catholic Health’s administrative data; Nashville, Tenn.-based HealthStream normalizes all the data around patient experience and then benchmarks the information against national and regional scores for Catholic Health facilities.

For hospital-acquired infections, the health system interfaces with the National Health Safety Network operated by the Centers for Disease Control and Prevention. Catholic Health uses SAS tools to wrangle, analyze and deliver standard reports across the enterprise, as well as identify problem areas, incorporate new protocols into its IT infrastructure and benchmark performance.

Underpinning those efforts are tools from Carmel, Ind.-based Clinical Architecture that interpret the variations in clinical data and expressions and maps the information to clinical standards such as LOINC lab codes and other data dictionaries.

“One of the biggest challenges has been the incredible variance in medical expressions,” Reichert says. “A single lab concept, such as a hemoglobin level, can be stored in 50 different ways, and how it’s documented varies widely amongst our EHRs in different regions,” Reichert says. “When I talk about data wrangling, that’s a good example of the challenges of getting usable data we can surface up into our analytics program.

“People typically think of data as coded or free text, but we’ve found most of it’s on a continuum—some data is very clean and standard, but most of it is somewhere between the two,” he adds. “You have a lot of work to do to interpret it and then put it in a form that can be used.”

To do so, Catholic Health utilizes a “late binding” approach; that’s strikingly different from the more commonly used “early-binding” strategy in its data warehouse. The health system uses data mining and visualization tools, including SAS Enterprise Guide and Visual Analytics, from Cary. N.C.-based SAS.

In the early-binding approach used by most industries, such as banking and retail, data flowing into the warehouse is quickly related based on business rules and vocabularies. Those data dimensions are relatively simple to identify and link, and in early-binding strategies, all the relationships would be resolved after the data hits the warehouse so it can be provisioned and used to support analytics efforts.

By contrast, the late-binding approach enables organizations to move data from source systems into a warehouse without trying to transform the data upfront by changing its formatting to make it usable for specific purposes and committing it to a relationship. That transformation and binding happens later, when the specific data is needed for an analytics effort.

Minimal data transformation occurs until that data is then linked to a “data bus” comprising a small number of core data elements that are common to almost all analytics use cases in healthcare—these include patient ID, provider ID, date and time, facility ID and others.

The output of the efforts is hospital and physician performance data that are provided in dashboards and PDF reports and utilized by senior-level executives to improve performance. The performance of each hospital is baselined and benchmarked, then discussed at board-level meetings and operational performance reviews. Based on the data, the different hospitals and divisions are asked to perform root-cause analysis and then come up with strategies to improve performance.

When the analytics effort first started at Catholic Health Initiatives, measurable improvement in outcomes was slow and inconsistent, but it has accelerated over time.

The key has been the health system’s strategy to utilize the analytics to identify each facility’s key areas of weakness and developing a long-term focus to address those deficiencies. For example, using transparent, risk-adjusted data, Catholic Health found that five facilities accounted for 60 percent to 80 percent of the clinical opportunity for improvement. Based on those findings, a long-term improvement focus was developed—a three-year plan with yearly resetting of the baseline and goals, but maintaining the same metrics year over year.

Reichert adds that the two key elements that result in success are that executives and clinical leaders trust the data, and those same leaders understand the information at which they’re looking. “The reason we’ve spent so much effort on standardization and normalization is to establish a single source of truth for the different buckets,” he says. “That’s why we baseline and benchmark and risk-adjust—we have such a large geographic area we cover and so many different types of data and technology that we have to ensure that the information is solid.”

Catholic Health Initiatives requires senior-level executives to go through formal online and classroom training for analytics as well as medical concepts. “We found that few executives had any kind of training about how to interpret regression analyses, observed to expected ratios, inclusion/exclusion criteria, among others, as well as medical terms,” Reichert says. “And not every executive is going to know what a CLABSI {central-line associated bloodstream infection} is, for example. Too many organizations in healthcare don’t really consider if executives have the knowledge to really understand the information they’re being asked to make decisions with.”


Competitive Intelligence: The Natural Extension to Business Intelligence | killexams.com real questions and Pass4sure dumps

For many companies, competitive intelligence (CI) is vital in order to improve their visibility in the market and to increase their market share. Competitive intelligence is “the systematic, ongoing, legal collection and analysis of Information about competitors, similar products, market trends, branches, new patents and technologies and new customer expectations.“1

Business intelligence (BI) collects and analyzes information about a company to support its managers in identifying the need for action in the market. Competitive intelligence gathers and discloses important information for the company’s decision makers, but this information is found outside the company and delivers hints about the market segment in which the company is active. To clarify the differences: Business intelligence is intelligence about one’s own company and uses information from inside the company. Competitive intelligence is additional intelligence about the economical ecosystem in which the company is acting, and can be found outside the company.

It is wise for the company to use both perspectives to judge its activities in relation to the market, its customers and its products. Both business intelligence and competitive intelligence deliver facts, but also soft information that is useful for the correction of its strategy and tactics. The market conditions and parameters are dictated by customers, competitors, politics and trends, and can be collected and prepared by competitive intelligence in a form that allows decision makers to act fast in areas that need adjustment due to changes in market conditions. Business intelligence and competitive intelligence create significant information and build a solid base for wise decisions needed to achieve the company’s overall goals.

How to Work with Competitive Intelligence

A company can build a solid base of information for competitive intelligence by:

  • Collecting data
  • Extracting information
  • Gaining context for the information
  • Collecting data can be done in several ways. One method is using Web Crawlers. They systematically search Internet pages and extract content in raw form.

    Other information sources are:

  • Public websites. These can be Web presences of customers, competitors or regulators
  • News services. There are specialized services available for subscription. Usually they describe market parameters (Reuters, Bloomberg, etc.).
  • Social media. Blogs, Twitter, Facebook, etc. deliver a lot of information about different players on the market.
  • Information services. For example, Edgar Online delivers finance information about companies such as balance sheet, cash flow, income statement, and number of employees, which might be of interest to their competitors.
  • Market analysts. Their studies give insight into markets and changes. They also identify trends and make predictions.
  • Information sources are diverse and while it is expected that information is presented in different formats, most of it is also unstructured.

    In the next step, useful information needs to be extracted from the various data gathered from different information sources. Information collection and aggregation can be done by using different methods of text mining. The intervention of people is even more important in competitive intelligence than in business intelligence when it comes to evaluating information and putting different pieces of information into a meaningful and relevant relationship.

    The last step in competitive intelligence is putting information into context so that it reflects reality as accurately as possible. This step requires a lot of time. Using the well-known 80/20 rule, you should use “only” 20% of the time for collecting and extracting the data, and 80% of the time to bring the information into context. This is discussed later in the article.

    Competitive Intelligence Means Context

    The market, competitors, customers and regulators represent the context in which a company is acting.

    Competitors are an ongoing pressure; demanding customers, sinking customer loyalty and increasing price sensitivity can be addressed by having a wide and sound knowledge about a company’s economical ecosystem, which is essential for decision makers. Collecting information that feeds the knowledge base is the task of business intelligence, customer relationship management and competitive intelligence. Competitive intelligence directs the focus of the company not only to the customers, as customer relationship management does, but also to other market actors. Competitors can compete for the same customers or influence, and regulators can control the market.

    The collected information in a CI environment has to fulfill the following criteria:

  • Relevance of information source (or data quality). What quality does the information source have? How clear is the information content? Does this source bring you a step further in gaining insight?
  • Timeliness. How up to date is the information? Is it one year old or only a few days? In general, up-to-date information has a deeper impact on the enterprise than older information.
  • Connection. Does the extracted information have a relevant connection to the company goal? What key people are connected to an important customer?
  • Message Strength. If a piece of information is used often in documents or has a huge impact in research activities, it has great strength.
  • If we try to apply the same criteria to a BI application, we discover that these questions are just as important for competitive intelligence. However, small differences must be observed when we apply the four criteria to business intelligence and competitive intelligence:

  • Relevance of information source (or data quality). This is easier to achieve in a BI environment. Under the condition that all data sources are connected to the business intelligence/data warehouse system, high quality of information is possible if clear data quality policies are in place.
  • Timeliness. This can be achieved in a BI application with a lot of time and money. Real-time solutions or “in-memory“ applications help achieve high quality in the actuality of the information.
  • Connection. Good modeling connects the right key figures to a certain question using a query or a report.
  • Message Strength. This is easier to achieve in business intelligence if we model smart key figures and dimensions that have a strong impact on the significance of the information.
  • The results of a CI analysis can be done in various ways: from tables using different structuring criteria to graphics and diagrams to visualize the most important concepts. For example, in the blogosphere, you can use tag clouds to visualize the strength of certain tags. Everything is accepted as long as the representation of information is intuitive, understandable and ergonomic.

    BI vs. CI or BI and CI?

    Paul Gray, Professor at Claremont Graduate University in California summarizes the differences between business intelligence and competitive intelligence: “Most business intelligence focuses on fact-based decision making that is based principally on understanding and using internal factors."2

    The goal of both business intelligence and competitive intelligence is to put raw data into context and gain useful information. On the next level, you should gain new insights from the collected information. This can be achieved by using comparison, filtering, change of the perspective, or associative connection of different pieces of information. In other words, gaining intelligent information using competitive intelligence is very similar to human reasoning.

    A first difference is to be found along the linkage Data → Information → Knowledge →Wisdom (this is how Larry P. English, an authority on information quality has defined it in his book Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits) in the way business intelligence and competitive intelligence enrich their data. Business intelligence has achieved a high degree of automation in data collection and enrichment. Competitive intelligence relies more on manual steps or human intervention.

    The process of gaining results in competitive intelligence is shown in Figure 1. We again encounter the Data → Information → Knowledge → Wisdom linkage, because the enrichment procedure and putting the data into a larger context is universal. The “Intelligence” step assumes that the decision maker uses the knowledge to make a decision and then observes the implications of this decision in the enterprise, prepared to make a change if necessary. Permanent maintenance of a CI environment can be of additional value for the company when it helps drive the business in an optimal way.

    Figure 1: Description of the competitive intelligence process

    If you look at the data sources for business intelligence and competitive intelligence, you can see that business intelligence uses primarily data from inside the company. Most BI data is structured data. Competitive intelligence uses information from outside the company and is never sure that all relevant data sources are being captured. Even in the ideal situation where almost all data sources are used, you can never be sure because the competition or other market agents may not be willing to tell the truth or to look for symmetry of information. And sometimes wrong or false information (FUD = fear, uncertainty and doubt) is spread deliberately in the market. Most information within the CI environment is unstructured, which emphasizes the complementarities of business intelligence and competitive intelligence. Both business intelligence and competitive intelligence face the same big challenge: There is too much information inside and outside the enterprise that needs to be processed.

    Knowledge is both explicit and implicit, but only explicit information can be processed automatically. Competitive intelligence starts off by not knowing at the very beginning what it is actually searching for. As time goes by, some insights are gained and information is sharpened. It is a deductive procedure, and the work is similar to that of a detective. Competitive intelligence wants to gain implicit knowledge, as sometimes explicit information is of low quality. Until recently, business intelligence processed only explicit information. It uses automated procedures, data warehouse data management, complex ETL processes, and calculation of key figures to feed the knowledge workers with relevant information. Recently, predictive analytics has become more important in the BI environment, supporting the efforts to extract implicit knowledge.

    Instead of comparing business intelligence and competitive intelligence, consider how both may be integrated into the enterprise.

    Methods in Data Analysis

    Competitive intelligence follows a process that consists of two phases:

    The following diagram explains the 80/20 method.

    Figure 2: 80/20 rule in CI environment

    In the first phase, you collect 80% of the data and use 20% of the time budget. It is called secondary research due to the fact that the research and the analysis are less important than in the second phase.

    The results of the first phase will be processed in the second phase – the primary research.  The second phase uses the remaining 80% of the time allocated to the process.

    The first phase can result in data overflow. Therefore, in the second phase you should mine the intelligence from the data that was collected in the first phase. In this second phase, the human factor is very important. Valuable information is gained about the business environment of the company with reflection and interviews with specialists and experts.

    The secondary research seems to be easier because it is a quantitative method that can be easily automated using certain software technologies. The primary research is conducted by people and is difficult to automate. Curiosity, neutrality and patience are the skills a good CI employee needs.

    Can we apply the 80/20 rule from the CI environment to a BI environment? The quick answer is: YES! BI users should use 80% of their time to get the last 20% of the valuable information that is not embedded explicitly in reports and graphs. Again, the human factor is important. A BI user has to find out why certain key figures have certain values, establish relationships between disparate numbers or  connect facts with information from outside the BI systems. Unfortunately, most BI systems store data from the past. New solutions offer to analyze detailed, real-time data. In predictive analytics, which makes predictions, people are not replaceable because predictive analytics is an iterative process that is guided by the user’s experience and knowledge. BI users are also mining after intelligence that is contained in the data of the enterprise.

    Conclusion

    Complementary Properties

    What are the complementary properties of business intelligence and competitive intelligence?

    In summary, business intelligence and competitive intelligence are complementary methods to gain information that helps a company act in its market environment.

  • In a BI environment, the context of information is immediately available to the company and easy to access. In a CI environment, you must deduce the correct context by using iterative and diverse methods. The context and information sources for competitive intelligence are hard to access and the quality of information should be routinely questioned.
  • BI data sources are mostly internal while competitive intelligence collects and evaluates data that exists outside of the company.
  • Business intelligence uses mostly structured data and is focused mainly on key figures. Competitive intelligence is primarily based on unstructured data and tries to build some key figures (e.g., revenues over time of the concurrence), but it takes text more seriously than business intelligence.

  • Similarities

    Which are the common properties and similarities of business intelligence and competitive intelligence?

    Competitive intelligence is recommended as an extension to business intelligence. Together they build a kind of intrinsic intelligence of the company. Firms that use business intelligence and competitive intelligence in their strategies have a better market position, are more realistic in estimating their positioning compared to their competitors, and are able to make better decisions in the proper window of opportunity.

    Using competitive intelligence, the human factor is again placed in the front. The recommendation of Hans-Georg Kemper and Henning Baars that technology should not get the upper hand must be internalized: “In the reality is often the case that in BI-/CI- projects exist a pronounced and dominant orientation to technology which in many cases get a counterproductive faith – a recurrent phenomenon which is known since decades."3

    References

  • BI Journal, Vol. 15, No. 4, (TDWI), "Competitive Intelligence", Paul Gray, Professor Emeritus of Information Science, Claremont Graduate University, California
  • HMD 247, 43. Volume, February 2006, "Business Intelligence und Competitive Intelligence", Prof. Dr. Hans-Georg Kemper, Henning Baars
  • BI Journal, Vol. 15, No. 4, (TDWI), “Learning Competitive Intelligence from a Bunch of Screwballs“, Troy Hiltbrand
  • White Paper “Self-Acting Data Mining: Das neue Paradigma der Datenanalyse“, P. Neckel, www.mayato.de

  • About the Degree | killexams.com real questions and Pass4sure dumps

    The Michigan Tech Advantage

    Our degree will provide you with a broad-based education in data mining, predictive analytics, cloud computing, data-science fundamentals, communication, and business acumen. Additionally, you will gain a competitive edge through domain-specific specialization in disciplines of science and engineering. You will have the freedom to explore and develop your own interests in one or more domains. 

    Prerequisites

    Entry into this program assumes basic knowledge in statistical and mathematical techniques, computer programming, information systems and databases, and communications, obtained through a degree in business, science, or an engineering discipline.

    2019-2020 Incoming Students Course Work

    New students entering the Graduate Certificate in Data Science must follow these new course work requirements.

    Current Students —Course Work

    Our Master of Science in Data Science is a terminal degree designed to prepare students for careers in industry and government.

    Coursework Option

    This option requires a minimum of 30 credits be earned through coursework. Use of limited number research credits in partial fulfillment of the requirements for a coursework degree may be allowed in exceptional cases. Students wishing to apply research credits toward a coursework degree must obtain approval from their advisor and department. The department must then obtain approval from the Graduate School. Approval will only be granted when there is evidence that an appropriate body of work has been completed, sufficient deliverables have been produced, and circumstances beyond the students' control have made it necessary for them to change from a research-based to a coursework-based degree program.

    A graduate program may require an oral or written examination before conferring the degree.

    Distribution of coursework credit   5000–6000 series (minimum) 18 credits 3000–4000 level (maximum) 12 credits Core Courses—12 credits

    The four required core 3-credit courses focus on fundamental skills in data science analytics, data mining, and business analytics. These courses are:

    BA 5200 - Information Systems Management and Data Analytics

    Focuses on management of IS/IT within the business environment. Topics include IT infrastructure and architecture, organizational impact of innovation, change management, human-machine interaction, and contemporary management issues involving data analytics. Class format includes lecture, group discussion, and integrative case studies.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Applied Natural Resource Econ., Accounting, Business Administration
  • CS 4821 - Data Mining

    Data mining focuses on extracting knowledge from large data sources. The course covers data mining concepts, methodology (measurement, evaluation, visualization), algorithms (classification/regression, clustering, association rules) and applications (web mining, recommender systems, bioinformatics).

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Restrictions: May not be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): (CS 3425 or MIS 3100) and (MA 2330 or MA 2320 or MA 2321) and (MA 2710 or MA 2720 or MA 3710)
  • MA 5790 - Predictive Modeling

    Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780
  • UN 5550 - Introduction to Data Science

    Course provides an introduction to Big Data concepts, with focus on data management, date modeling, visualization, security, cloud computing, and data science from different perspectives: computer science, business, social science, bioinformatics, engineering, etc. Course introduces tools for data analytics such as SPSS Modeler, R, SAS, Python, and MATLAB. Two case study projects which are integrated with communication and business skills.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science
  • Foundational Courses—Maximum of 6 credits

    A maximum of six credit hours of foundational skills courses at the 3000–4000 level may be applied to the Master of Science in Data Science. These courses will build skills necessary for successful completion of the MS in Data Science. Some students will not need to take these foundational courses and will instead use the domain electives to reach the credit requirements of this program.

    The 2000-level courses listed here cannot be counted towards the requirement for the MS in Data Science but may be necessary for a given student to build their foundational knowledge.

    CS 2321 - Data Structures

    Presents fundamental concepts in data structures. Topics include abstract data types (priority queues, dictionaries and graphs) and their implementations, algorithm analysis, sorting, text processing, and object oriented design. A significant programming project is assigned.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring, Summer
  • Pre-Requisite(s): CS 1122 or CS 1131
  • CS 3425 - Introduction to Database Systems

    This course provides an introduction to database systems including database design, query, and programming. Topics include goals of database management; data definition; data models; data normalization; data retrieval and manipulation with relational algebra and SQL; data security and integrity; database and Web programming; and languages for representing semi-structured data.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): (CS 2311 or MA 3210) and CS 2321
  • MA 2330 - Introduction to Linear Algebra

    An introduction to linear algebra and how it can be used, including basic mathematical proofs. Topics include systems of equations, vectors, matrices, orthogonality, subspaces, and the eigenvalue problem. Not open to students with credit in MA2320 or MA2321.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): MA 1160 or MA 1161 or MA 1135
  • MA 3710 - Engineering Statistics

    Introduction to the design, conduct, and analysis of statistical studies aimed at solving engineering problems. Topics include methods of data collection, descriptive and graphical methods, probability and probability models, statistical inference, control charts, linear regression, design of experiments. Not open to students with credit in MA2710, MA2720, or MA3715.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring, Summer
  • Pre-Requisite(s): MA 2160
  • MA 3715 - Biostatistics

    Introduction to the design and analysis of statistical studies in the health and life sciences. Topics include study design, descriptive and graphical methods, probability, inference on means, categorical data analysis, and linear regression. Not open to students with credit in MA2710, MA2720, or MA3710.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): MA 1135 or MA 1160 or MA 1161
  • MIS 2000 - IS/IT Management

    Focuses on the theory and application of the information-systems discipline within an organizational context, and identifies the roles of management, users, and information systems professionals. Covers the use of information systems and implications for decision support to improve business processes, and addresses the ethical, legal, and social issues of IT.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring, Summer
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman
  • Pre-Requisite(s): BUS 1100 or CS 1121 or CS 1131 or ENG 1101 or (ENG 1001 and ENG 1100) or SAT 1200
  • MIS 2100 - Introduction to Business Programming

    Develops business problem solving skills through the application of a commonly used high-level business programming language. Topics include the nature of the business programming environment, fundamentals of the language (e.g., programming constructs, data management, manipulation of simple data structures), structured programming concepts, desirable programming practices and design, debugging and testing techniques.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • MIS 3100 - Business Database Management

    Emphasizes database principles that are constant across different database software products through concrete examples using a relational database management system. Provides a well-rounded business perspective about developing, utilizing, and managing organizational databases.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): MIS 2000(C)
  • MKT 3600 - Marketing Data Analytics

    Focuses on data-driven consumer insights for marketing decision-making. Topics include scientific research methodology, survey research, social media data-analysis, multivariate data analysis, information visualization, and report writing and presentations.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or BUS 2100) and MKT 3000
  • SAT 3002 - Application Programming Introduction

    Students will develop problem solving skills through the application of a commonly used high-level programming language. Topics include: nature of the programming environment; fundamentals of programming languages; structured programming concepts; object-oriented programming concepts; desirable programming practices and design; and debugging and testing techniques.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-1)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Class(es): Junior, Senior
  • SAT 3210 - Database Management

    Introductory course on database management. Topics include data modeling, database design, implementation techniques, SQL Language, database administration and security.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-2)
  • Semesters Offered: Fall, Summer
  • Restrictions: Must be enrolled in one of the following Major(s): Computer Network & System Admn; Must be enrolled in one of the following Class(es): Junior, Senior
  • Pre-Requisite(s): SAT 1200 or CS 1111 or CS 1121 or CS 1131 or CS 1142 or MIS 2100
  • SAT 4600 - Web Application Development

    An introduction to the building and administration of web applications. Topics covered include: Apache web server development; Tomcat application server; HTML; cascading style sheets; JavaScript; JQuery; server side includes; server side application development; web services; SSL/TLS; and authentication/authorization.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-2)
  • Semesters Offered: Spring
  • Pre-Requisite(s): SAT 3002 or SAT 3310
  • Electives—Minimum of 6 credits

    Two courses must be taken from the list of approved elective courses:

    BA 5740 - Managing Innovation and Technology

    An evolutionary strategic perspective is taken viewing how technology strategy evolves from underlying technological competencies, patterns of innovation, sources of external technological knowledge and modes of transfer.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Applied Natural Resource Econ., Accounting, Business Administration
  • CS 5841 - Machine Learning

    This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): CS 4821
  • CS 5471 - Computer Security

    This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CS 3411 or CS 4411
  • FW 5083 - Programming Skills for Bioinformatics

    Students will learn computer programming skills in Perl for processing genomic sequences and gene expression data and become familiar with various bioinformatics resources.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2017-2018 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • MA 5781 - Time Series Analysis and Forecasting

    Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715) and (MA 3720 or EE 3180)
  • PH 4395 - Computer Simulation in Physics

    Role of computer simulation in physics with emphasis on methodologies, data and error analysis, approximations, and potential pitfalls. Methodologies may include Monte Carlo simulation, molecular dynamics, and first-principles calculations for materials, astrophysics simulation, and biophysics simulations.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Spring
  • Pre-Requisite(s): PH 3300 and PH 4390 and (PH 2400 or PH 3410)
  • PSY 5210 - Advanced Statistical Analysis and Design I

    An overview of data analysis methods including visualization, data programming, and univariate statistics such as t-test and ANOVA.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-1)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2018-2019 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • UN 5390 - Scientific Computing

    Set in a Linux environment, course offers exposure to Foss tools for developing computational and visualization workflows. Students will learn to translate problems into programs, understand sources of errors, and debug, improve the performance of and parallelize the code.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate
  • Domain Specific Courses—Maximum of 12 Credits

    To complete the Master of Science in Data Science, students must earn the remaining of the required 30 credits through completion of approved domain-specific Data Science courses. Students may choose domain-specific courses from one or more domains. Each student will consult with her/his advisor in order to determine the appropriate mix of elective courses and domain-specific courses, given the student’s background, interests, and career aspirations.

      Biomedical Engineering

    BE 5550 - Biostatistics for Health Science Research

    An overview course of biostatistical methods used in the health sciences. Topics include a review of undergraduate statistical concepts, NIH, CDC, and FDA guidelines for clinical trial research, proper use of biostatistical methods including anova models, logistic regression, risk analysis, survivorship analysis and any other statistical methods that are common in the enrolled students' discipline.

  • Credits: variable to 4.0
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): MA 2720 or MA 3710
  • Business and Economics

    BA 5610 - Operations Management

    Applications and case studies focusing on contemporary issues in operations and quality management to include lean manufacturing practices, ERP, quality and environmental management systems/standards, Six Sigma, statistical process control, and other current topics.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Applied Natural Resource Econ., Accounting, Business Administration
  • Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710
  • BA 5800 - Marketing, Technology, and Globalization

    The course facilitates students' improvement of analytical skills, information processing techniques, and cultural competence in the globalized marketing environment. Focuses are placed on strategic marketing management, high-tech product marketing, global consumer behavior, branding, and online marketing.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Data Science, Applied Natural Resource Econ., Accounting, Business Administration
  • EC 4200 - Econometrics

    Introduces techniques and procedures to estimate and test economic and financial relationships developed in business, economics, social and physical sciences.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): (EC 2001 or EC 3002 or EC 3003) and (BUS 2100 or MA 2710 or MA 2720 or MA 3710) and (MA 1135 or MA 1160 or MA 1161)
  • MIS 3400 - Business Intelligence

    Focuses on generation and interpretation of business analytics relative to organizational decision making. Includes core skills necessary for constructing data retrieval queries in a relational database environment.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: On Demand
  • Pre-Requisite(s): MIS 2000 and (MIS 3100 or CS 3425)
  • Chemical Sciences

    CH 4610 - Introduction to Polymer Science

    Introductory study of the properties of polymers. Includes structure and characterization of polymers in the solid state, in solution, and as melts. Topics include viscoelasticity, rubbery elasticity, rheology and polymer processing. Applications discussed include coatings, adhesives, and composites.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): CH 1122 or (CH 1160 and CH 1161)
  • CH 5410 - Advanced Organic Chemistry: Reaction Mechanisms

    Advanced study of mechanistic organic and physical organic chemistry intended to bring the student to the level of current research activity. Topics may include methods for determining organic reaction mechanisms, chemical bonding as it applies to organic compounds, structure-reactivity relationships, molecular rearrangements, and molecular orbital theory.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • CH 5420 - Advanced Organic Chemistry: Synthesis

    Advanced study of organic reactions and synthetic organic chemistry intended to bring the student to the level of current research activity. Topics may include retrosynthetic analysis and synthesis design, synthons, protecting groups, and analysis of syntheses from recent literature.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • CH 5509 - Transport and Transformation of Organic Pollutants

    Assessment of factors controlling environmental fate, distribution, and transformation of organic pollutants. Thermodynamics, equilibrium, and kinetic relationships are used to quantify organic pollutant partitioning and transformations in air, water, and sediments. Use of mass balance equations to quantify pollutant transport.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2005-2006 academic year
  • Pre-Requisite(s): ENVE 4501 or CEE 4501 or CH 3510
  • CH 5515 - Atmospheric Chemistry

    Study of the photochemical processes governing the composition of the troposphere and stratosphere, with application to air pollution and climate change. Covers radical chain reaction cycles, heterogeneous chemistry, atmospheric radiative transfer, and measurement techniques for atmospheric gases.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CH 3510 or ENVE 4501 or ENVE 4504 or CEE 4501 or CEE 4504
  • CH 5516 - Aerosol and Cloud Chemistry

    This course is focused on the chemistry of atmospheric aerosols and cloud processes. Students will learn about methods for chemical characterization, the chemical composition of aerosol and the chemical reactions pertinent to secondary aerosol and cloud composition.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2019-2020 academic year
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • CH 5560 - Computational Chemistry

    Focuses on the theory and method of modern computational techniques applied to the study of molecular properties and reactivity through lecture and computer projects. Covers classical mechanical as well as quantum mechanical approaches.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2010-2011 academic year
  • Pre-Requisite(s): CH 3520
  •   Cognitive and Learning Sciences

    PSY 5220 - Advanced Statistical Analysis and Design II

    Course covers multivariate statistics such as ANCOVA, Multiple Regression, factor analysis, clustering, machine learning, and mixture modeling.

  • Credits: 3.0; Repeatable to a Max of 12
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): PSY 5110
  • Computer Science

    CS 4425 - Database Management System Design

    This course covers the design issues concerning the implementation of database management systems, including distributed databases. The topics include data storage, index implementation, query processing and optimization, security, concurrency control, transaction processing, and recovery.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: On Demand
  • Pre-Requisite(s): CS 3425
  • CS 4471 - Computer Security

    This covers fundamentals of computer security. Topics include practical cryptography, access control, security design principles, physical protections, malicious logic, program security, intrusion detection, administration, legal and ethical issues.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Restrictions: May not be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CS 3411 or CS 4411
  • CS 4811 - Artificial Intelligence

    Fundamental ideas and techniques that are used in the construction of problem solvers that use Artificial Intelligence technology. Topics include knowledge representation and reasoning, problem solving, heuristics, search heuristics, inference mechanisms, and machine learning.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
  • Pre-Requisite(s): CS 2321 and CS 3311
  • CS 5321 - Advanced Algorithms

    Design and analysis of advanced algorithms. Topics include algorithms for complex data structures, probabilistic analysis, amortized analysis, approximation algorithms, and NP-completeness. Design and analysis of algorithms for string-matching and computational geometry are also covered.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): CS 4321
  • CS 5331 - Parallel Algorithms

    Advanced topics in the design, analysis, and performance evaluation of parallel algorithms. Topics include advanced techniques for algorithm analysis, memory models, run time systems, parallel architectures, and program design, particularly emphasizing the interactions of these factors.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): CS 4431 and CS 4331
  • CS 5441 - Distributed Systems

    Covers time and order in distributed systems; mutual exclusion, agreement, elections, and atomic transactions; Distributed File Systems, Distributed Shared Memory, Distributed System Security; and issues in programming distributed systems. Uses selected case studies.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): CS 4411 and CS 4461
  • CS 5496 - GPU and Multicore Programming

    Introduction to Graphics Processing units (GPU) and multi-core systems, their architectural features and programming models, stream programming and compute unified driver architecture (CUDA), caching architectures, linear and non-linear programming, scientific computing on GPUs, sorting and search, stream mining, cryptography, and fixed and floating point operations.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CS 3411 and CS 3421
  • CS 5631 - Data Visualization

    Introduction to scientific and information visualization. Topics include methods for visualizing three-dimensional scalar and vector fields, visual data representations, tree and graph visualization, large-scale data analysis and visualization, and interface design and interaction techniques.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): CS 4611 or CS 5611
  • CS 5760 - Human-Computer Interactions and Usability Testing

    Current issues in human-computer interaction (HCI), evaluation of user interface (UI) design, and usability testing of UI. Course requires documenting UI design evaluation, UI testing, and writing and presenting a HCI survey, concept or topic paper.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): CS 4760
  • CS 5811 - Advanced Artificial Intelligence

    Course topics include current topics in artificial intelligence including agent-based systems, learning, planning, use of uncertainty in problem solving, reasoning, and belief systems.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): CS 4811
  • CS 5821 - Computational Intelligence - Theory and Application

    This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate
  • Electrical and Computer Engineering

    EE 5496 - GPU and Multicore Programming

    Introduction to Graphics Processing Units (GPU) and multi-cores, their architectural features and programming models, stream programming, and compute unified driver architecture (CUDA), caching architectures, linear and non-linear programming, scientific computing on GPUs, sorting and search, stream mining, cryptography, and fixed and floating point operations.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): CS 3411 and EE 4173
  • EE 5500 - Probability and Stochastic Processes

    Theory of probability, random variables, and stochastic processes, with applications in electrical and computer engineering. Probability measure and probability spaces. Random variables, distributions, expectations. Random vectors and sequences. Stochastic processes, including Gaussian and Poisson processes. Stochastic processes in linear systems. Markov chains and related topics.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • EE 5521 - Detection & Estimation Theory

    Detecting and estimating signals in the presence of noise. Optimal receiver design. Applications in communications, signal processing, and radar.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical Engineering, Computer Engineering
  • Pre-Requisite(s): EE 5500
  • EE 5726 - Wireless Sensor Networks

    Introduces the concepts of wireless sensor networks. Topics include sensor network coverage and sensor deployment, time synchronization and sensor node localization, network protocols, data storage and very, collaborative signal processing. Introduce sensor network programming network reliability and tolerance.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Pre-Requisite(s): (CS 4461 or EE 4272 or EE 5722) and (EE 3170 or EE 3173) and (CS 1129 or CS 2141)
  • EE 5821 - Computational Intelligence - Theory and application

    This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate
  • EE 5841 - Machine Learning

    This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): CS 4090
  • Forest Resources and Environmental Science

    FW 5084 - Data Presentation and Visualization with R

    This course is designed for graduate students majoring in forestry, wildlife, ecology, and natural resource management and data science to develop fundamental but essential skills for data presentation and visualization through generating informative graphs with R.

  • Credits: 2.0
  • Lec-Rec-Lab: (1-0-2)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2017-2018 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • FW 5411 - Applied Regression Analysis

    Regression as a tool for the analysis of forest and environmental science data. Topics include multiple linear, curvilinear and non-linear regression, hierarchical and grouped data and mixed-effects models. Emphasis is placed on application of tools to real-world data.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • FW 5412 - Regression in R

    Use of R for basic data manipulation, statistical summary and regression. Topics include installing R, data import and export, basic statistics, graphics and fitting of linear, non-linear and mixed-effects models.

  • Credits: 1.0
  • Lec-Rec-Lab: (0-1-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2018-2019 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Co-Requisite(s): FW 5411
  • FW 5540 - Remote Sensing of the Environment

    Remote sensing principles and concepts. Topics include camera and digital sensor arrays, types of imagery, digital data structures, spectral reflectance curves, applications, and introductory digital image processing.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-1-0)
  • Semesters Offered: Fall
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Co-Requisite(s): FW 5541
  • FW 5550 - Geographic Information Science and Spatial Analysis

    Use of geographic information systems (GIS) in resource management. Studies various components of GIS in detail, as well as costs and benefits. Laboratory exercises use ArcGIS software package to solve resource management problems.

  • Credits: 4.0
  • Lec-Rec-Lab: (3-0-3)
  • Semesters Offered: Fall
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710
  • FW 5555 - Advanced GIS Concepts and Analysis

    This course moves beyond the fundamentals of GIS to explore the application of GIS technology to environmental monitoring and resource management issues. Students learn graphic modeling techniques, network analysis, 3D visualization, geodatabase construction and management, and multivariate spatial analysis.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): FW 5550
  • FW 5556 - GIS Project Management

    Course provides exposure to data collection techniques, web mapping applications, and advanced database structures. Students will investigate GIS system design, GIS project planning and data management, learn map atlas creation and cartographic techniques, and discuss geospatial ethics.

  • Credits: 3.0
  • Lec-Rec-Lab: (1-0-4)
  • Semesters Offered: Spring
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): FW 5550
  • FW 5560 - Digital Image Processing: A Remote Sensing Perspective

    Presents the theory and quantitative procedures of digital image processing using remotely sensed data. Emphasizes image acquisition, preprocessing, enhancement, transformation classification techniques, accuracy assessment, and out-products. Discusses linkages to GIS. Also covers evaluating applications of the technology to current resource management problems via peer-reviewed literature.

  • Credits: 4.0
  • Lec-Rec-Lab: (3-0-1)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2019-2020 academic year
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
  • Pre-Requisite(s): FW 5540
  • Geological and Mining Engineering and Sciences

    GE 5150 - Advanced Natural Hazards

    Exploration of how to develop comprehensive plans to mitigate the impact of natural hazards on humans. Requires a project and report.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • GE 5195 - Volcano Seismology

    Will prepare students, including those with no seismology background, to interpret seismic and acoustic signals from volcanoes. Topics: basic seismology, monitoring techniques, tectonic and volcanic earthquakes, infrasound, deformation over a range of time scales.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-1)
  • Semesters Offered: Spring
  • Pre-Requisite(s): (MA 1160 or MA 1161 or MA 1135) and GE 2000 and PH 2100
  • GE 5250 - Advanced Computational Geosciences

    Introduction to quantitative analysis and display of geologic data using R/Matlab, covering basic R/Matlab syntax and programming, and analysis of one-dimensional (e.g. time series) and two-dimensional datasets (e.g. spatial data). Techniques are applied to geological datasets. Requires an in-depth project, report, and presentation.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-1)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • GE 5600 - Advanced Reflection Seismology

    Principles and application of reflection seismic techniques. Includes acquisition, data processing, and 2D/3D data interpretation. Project and report required.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-1-0)
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • GE 5870 - Geostatistics & Data Analysis

    This course covers the handling of spatial and temporal data for knowledge discovery. Major topics include spatial interpolation, clustering, association analysis, and supervised and unsupervised classification. Students will learn how to use geostatistical and pattern recognition tools for geoscience applications.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-1)
  • Semesters Offered: Fall, Spring
  • Pre-Requisite(s): GE 3250
  • Mathematics

    MA 4330 - Linear Algebra

    A study of fundamental ideas in linear algebra and its applications. Includes review of basic operations, block computations; eigensystems of normal matrices; canonical forms and factorizations; singular value decompositions, pseudo inverses, least-square applications; matrix exponentials and linear systems of ODEs; quadratic forms, extremal properties, and bilinear forms.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): (MA 2320 or MA 2321 or MA 2330) and MA 3160
  • MA 4710 - Regression Analysis

    Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715
  • MA 4720 - Design and Analysis of Experiments

    Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The SAS statistical package is an integral part of the course.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715
  • MA 5201 - Combinatorial Algorithms

    Basic algorithmic and computational methods used in the solution of fundamental combinatorial problems. Topics may include but are not limited to backtracking, hill-climbing, combinatorial optimization, linear and integer programming, and network analysis.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2010-2011 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • MA 5221 - Graph Theory

    Review of basic graph theory followed by one or more advanced topics which may include topological graph theory, algebraic graph theory, graph decomposition or graph coloring.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall - Offered alternate years beginning with the 2003-2004 academic year
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): MA 5301 or MA 4209
  • MA 5401 - Real Analysis

    A graduate-level study of the Lebesgue integral including its comparison with the Riemann integral; the Lebesgue measure, measurable functions and measurable sets. Integrable functions, the monotone convergence theorem, the dominated convergence theorem, and Fatou's lemma.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • MA 5627 - Numerical Linear Algebra

    Design and analysis of algorithms for the numerical solution of systems of linear algebraic equations, least-square problems, and eigenvalue problems. Direct and iterative methods will be covered.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): MA 4330 or MA 4630
  • MA 5630 - Numerical Optimization

    Numerical solution of unconstrained and constrained optimization problems and nonlinear equations. Topics include optimality conditions, local convergence of Newton and Quasi-Newton methods, line search and trust region globalization techniques, quadratic penalty and augmented Lagrangian methods for equality-constrained problems, logarithmic barrier method for inequality-constrained problems, and Sequential Quadratic Programming.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2002-2003 academic year
  • Pre-Requisite(s): MA 4330 or MA 4610 or MA 4630 or MA 5627
  • MA 5701 - Statistical Methods

    Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • MA 5741 - Multivariate Statistical Methods

    Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Pre-Requisite(s): (MA 4710 or MA 4720) and MA 2320
  • MA 5750 - Statistical Genetics

    Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2015-2016 academic year
  • MA 5761 - Computational Statistics

    Introduction to computationally intensive statistical methods. Topics include resampling methods, Montes Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software S-plus.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Pre-Requisite(s): MA 4770(C)
  • MA 5791 - Categorical Data Analysis

    Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring - Offered alternate years beginning with the 2005-2006 academic year
  • Physical Sciences

    PH 4390 - Computational Methods in Physics

    An overview of numerical and computer methods to analyze and visualize physics problems in mechanics, electromagnetism, and quantum mechanics. Utility and potential pitfalls of these methods, basic concepts of programming, UNIX computing environment, system libraries and computer graphics are included.

  • Credits: 3.0
  • Lec-Rec-Lab: (2-0-3)
  • Semesters Offered: Fall
  • Pre-Requisite(s): PH 2020 and PH 3410
  • Social Sciences

    SS 5005 - Introduction to Computational Social Science

    An introduction to computational methods for the social sciences. The course provides an introduction to complexity theory and Agent-Based Modeling. Students will apply what they have learned in this course to develop a pilot simulation to understand any social phenomena of their choosing.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
  • SS 5315 - Population and Environment

    This course investigates relationships between the world's population, population change, population distribution, resource consumption, and environmental and social consequences. Addresses local and global relationships and the population processes (mortality, fertility, and migration) involved.

  • Credits: 3.0
  • Lec-Rec-Lab: (3-0-0)
  • Semesters Offered: On Demand
  • Pre-Requisite(s): SS 5400(C) or SS 3760 or FW 3760
  • Systems Administration Technology

    SAT 5001 - Introduction to Medical Informatics

    Course covers fundamental subjects such as medical decision support systems, telemedicine, medical ethics and biostatistics. Topics include consumer health informatics, international health care systems, global health informatics, translational research informatics and homecare. Students will see medical informatics from diverse perspectives. Scientific writing and communication will be encouraged.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • SAT 5002 - Application Programming Introduction

    Students will develop problem solving skills through the application of a commonly used high-level programming language. Topics include: nature of the programming environment; fundamentals of programming languages; structured programming concepts; object-oriented programming concepts; desirable programming practices and design; and debugging and testing techniques.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-1)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • SAT 5121 - Introduction to Medical Sciences, Human Pathophysiology, Healthcare

    Course provides basic concepts in medicine and human pathophysiology to introduce a molecular understanding of human metabolism and disease. Topics also include physical examination of patient, taking medical history, laboratory medicine, disease management and treatment, medical diagnostics, clinical workflow, and medical special/subspecialities.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • SAT 5141 - Clinical Support Modeling

    Course addresses complex medical decisions, evidence-based medicine, disease management and comprehensive laboratory informatics. Topics include improving physical order entry and healthcare, using medical literature, clinical case discussions, meaningful use of medical data, enhancing patient and care-giver education, disease prevention, and public health and environmental health informatics.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Fall, Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Co-Requisite(s): SAT 5114
  • SAT 5161 - Data Warehousing and Business Intelligence

    Identifies database solutions and key elements of an enterprise data warehouse. Explains how to apply best practices for development of data warehouses, the role of business intelligence and data mining in supporting the strategic business decision process, and OLAP (Online Analytical Processing) and its use in reporting and analyzing database and data warehouse information. Defines security practices for a data warehouse environment.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-1)
  • Semesters Offered: Fall, Summer
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): SAT 3210
  • SAT 5241 - Designing Security Systems

    Provides an overview of techniques used in the design of secure systems with a primary focus on real-world case studies. Students will examine attacks on deployed systems and investigate how these vulnerabilities have been addressed. Practical advantages and shortcomings of several notions of provable security will also be examined. Students will be expected to read, understand, and present recent research papers.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): SAT 5111
  • SAT 5600 - Web Application Development

    An introduction to the building and administration of web applications. Topics covered include: Apache web server development; Tomcat application server; HTML; cascading style sheets; JavaScript; JQuery; server side includes; server side application development; web services; SSL/TLS; and authentication/authorization.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-2-2)
  • Semesters Offered: Spring
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Pre-Requisite(s): SAT 5002
  • SU 5010 - Geospatial Concepts, Technologies, and Data

    High-level review of geospatial data acquisition systems, sensors and associated processing technologies. Course considers geospatial metadata generation principles, interoperability, and major tools for manipulation with geospatial data. Course may help in transition of non-geospatial majors to geospatial field.

  • Credits: 3.0
  • Lec-Rec-Lab: (0-3-0)
  • Semesters Offered: On Demand
  • Restrictions: Must be enrolled in one of the following Level(s): Graduate
  • Sample Schedules


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