Advanced standing in the Project Management program may be awarded by earning:. The Master of Science in Project Management is earned by completing graduate credits. International degrees are considered on an individual basis. For complete information, view the full admission requirements.
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Applied Biotechnology Online M. Criminal Justice Online M. Engineering Online M. Ask if they would sign off on how you are representing your work if audited. Alejandro M. I pursued the PMP certification for several reasons. Also, as part of a global virtual team, it made sense for our team to adopt a common methodology and vocabulary, and to implement a project management approach for our global initiatives. By earning the PMP, I could better understand the needs and connect with the people we support.
The PMP certification seems daunting at first and it is easy to be discouraged by the eligibility requirements. I guess I was only sure of my eligibility after I took the time to work on the application. I do manage projects as part of my work, but not exclusively.
For example, I used experience where I was coordinating rather than leading a project. The bulk of my leading and directing experience came from championing a global program that provided high-end industry certifications to an identified pool of top performers. As a project leader, I had to work with stakeholders to obtain their support for the project, define the certifications in scope, develop project guidelines, presentation and launch, propose final list of candidates, manage the global budget, and monitor project performance to closure.
Prepare a realistic plan and start with a target date for your exam so you have a deadline. Also, give yourself enough time to prepare without cramming. There is a lot of material to cover, and you need time. To do this, pick good, complete training materials and be consistent with your preparation. Mixing resources from various sources will get you confused. Finally, memorize the formulas! I was looking for professional credentialing opportunities that would both help support work that I was doing in the retail service industry, and also open the door to additional opportunities within my organization.
I was a district manager and did not have what I would call traditional project management experience. After seeking advice from a colleague who had already earned the PMP, I realized I had the right experience after all. My experience included leading education, training, professional development, and retail office renovation and relocation projects.
Of these types of projects, the bulk of my experience came from professional development and training projects for internal personnel.
erstwhile.jeamland.net/matemticas-unidades-didcticas-4-eso.php For example, I oversaw the planning and execution of an internal tax training school that operated over a six month period. When a course waiver is granted, the student must substitute a graduate-level elective for the core course. The first part of the course introduces the mathematical prerequisites for understanding probability and statistics.
Students admitted to the program have the option of earning the MSPM degree completely online. Group and Organization Management, 27 1 , 14— The accelerated MSPM option is for academically outstanding undergraduate students who plan to enroll in the program upon earning their baccalaureate degree. The difference in time zones between two locations is fewer than six hours, and the number of team locations is less than four. We recognize value in your professional experience.
Topics include combinatorial mathematics, functions, and the fundamentals of differentiation and integration. The second part of the course concentrates on the study of elementary probability theory, discrete and continuous distributions. Prereq: Academic background that includes the material covered in a standard course on college algebra or instructor's consent.
Students who are not pursuing a concentration must select four elective courses from the following list. In choosing electives, students should make sure that they have all prerequisites required by the selected course. At least three elective courses must be at the level or above:. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language.
Algorithms are created, decomposed, and expressed as pseudocode. The running time of various algorithms and their computational complexity are analyzed. This course is primarily the study of design of graphic algorithms. At the end of the course you can expect to be able to write programs to model, transform and display 3- dimensional objects on a 2-dimensional display. The course starts with a brief survey of graphics devices and graphics software.
Attributes of the primitives are studied as well as filtering and aliasing. Geometric transformations in 2 dimensions are introduced in homogeneous coordinates, followed by the viewing pipeline, which includes clipping of lines, polygons and text.
Hierarchical graphics modeling is briefly studied. The graphics user interface is introduced and various input functions and interaction modes are examined. This is followed by 3-d transformations and the 3-d viewing pipeline. The course ends with a study of algorithms to detect the visible surfaces of a 3-d object in both the object space and the image space. Laboratory Course.
Or instructor's consent.
The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined.
The concepts covered in the course are demonstrated using R. This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed.
Concepts are presented in context of real world examples. This course presents financial algorithms used in applications of computer science in financial decision analysis, risk management, data mining and market analysis, and other modern business processes. The course covers theoretical background on probabilistic methods used for financial decision making and their application in number of fields such as financial modeling, venture capital decision making, operational risk measurement and investment science.
Number of financial applications and algorithms are being presented for portfolio risk analysis, modeling real options, venture capital decision making, etc. The course concludes with algorithms for financial risk assessment and presents the security concepts and challenges of financial information systems.
This course is designed for IT professionals, and those training to be IT professionals, who are preparing for careers in healthcare-related IT Health Informatics.
This course provides a high-level introduction into basic concepts of biomedicine and familiarizes students with the structure and organization of American healthcare system and the roles played by IT in that system. The course introduces medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes.
IT case studies demonstrate the key roles of health informatics and how IT tools and resources help medical professionals integrate multiple sources of information to make diagnostic and therapeutic decisions. This course presents the technological fundamentals and integrated clinical applications of modern Biomedical IT. The first part of the course covers the technological fundamentals and the scientific concepts behind modern medical technologies, such as digital radiography, CT, nuclear medicine, ultrasound imaging, etc.
It also presents various medical data and patient records, and focuses on various techniques for processing medical images.
This part also covers medical computer networks and systems and data security and protection. The second part of the course focuses on actual medical applications that are used in health care and biomedical research. Electronic Health Records EHRs are application systems that automate the activities of healthcare clinicians including physicians, nurses, physician assistants, and healthcare administrative staff.
This increased use of EHRs has many challenges including complex data, high security requirements, integration to multiple application systems, a distributed user base, and broad impact on how these users work. In this course we will study the fundamental and design applications of various biometric systems based on fingerprints, voice, face, hand geometry, palm print, iris, retina, and other modalities. Multimodal biometric systems that use two or more of the above characteristics will be discussed. Biometric system performance and issues related to the security and privacy aspects of these systems will also be addressed.
Comprehensive coverage of object-oriented programming with cooperating classes. Implementation of polymorphism with inheritance and interfaces and in Java library containers. Threads, sockets, datagrams and database connectivity are also covered in this course. This course provides students with a comprehensive overview of the principles, processes, and practices of software project management.