ADMN 864 (1ON) - New Product Development
Term: Spring 2018 - E-term III (01/16/2018 - 03/09/2018)
Grade Mode: Letter Grading
CRN: 56668
Start Date | End Date | Days | Time | Location |
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1/16/2018 | 3/9/2018 | Hours Arranged | ONLINE |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
1/16/2018 | 3/9/2018 | Hours Arranged | ONLINE |
Start Date | End Date | Days | Time | Location |
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3/19/2018 | 5/10/2018 | Hours Arranged | ONLINE |
Course Contents
This course will expose students to the strategic role human resources play in maximizing the value of the workforce. Organizations in today's economy rely not only on the knowledge and skills of their employees, but also on their engagement with the work and each other and their commitment to the organization's mission. As such, the management of human talent is a responsibility of every manager, in partnership with the HR function, and is vital to the success of any organization. The coverage of course concepts will be approached from both from a general manager's perspective as well as from an HR perspective, considering human resource systems and practices available to build a skilled and motivated workforce.
Learning Objectives
This course is intended to provide practical tools for analyzing and valuing a company’s equity. Primarily an applications course, Applied Equity Analysis reviews financial statement items and key ratios, covers several valuation models, and focuses on the implementation of finance theories introduced in prior finance courses to stock analysis and valuation problems.
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
1/16/2018 | 3/9/2018 | Hours Arranged | ONLINE |
Managing Growth and Innovation deals with central concepts and related applications at the intersection of technological innovation, organizational growth, and corporate entrepreneurship or intrapreneurship.
Technological innovation is increasingly becoming the source of sustainable competitive advantage for firms, especially in disruptive business environments. Yet, building an organization to successfully and repeatedly bring technological innovations to market is a daunting managerial challenge. As organizations get more established, they often lose the innovative edge, finding that their very existence is threatened by their inability to innovate successfully. Leaders with a deep understanding of innovation and change management, which are central to intrapreneurship, can help organizations counteract this tendency.
Throughout the course, we will examine four aspects of technological innovation and their impact on organizational growth: exploring, executing, leveraging, and renewing innovation. The course will draw on rigorous theoretical understanding of innovation in disruptive environments and develop a toolbox of strategies and practices managers use to effectively manage innovation and organizational growth when operating under these operating contexts.
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
3/19/2018 | 5/10/2018 | Hours Arranged | ONLINE | |
4/20/2018 | 4/22/2018 | FSU | 8:00am - 5:00pm | PCBE 215 |
*MBA weekend on-campus dates: Friday-Sunday, April 20-22, 2018. Further course preparation and assignments may be required prior to or following on-campus dates. Instructor will contact registered students at start of term.
Start Date | End Date | Days | Time | Location |
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1/16/2018 | 3/9/2018 | W | 5:40pm - 9:15pm | PCBE 215 |
COURSE DESCRIPTION
With improvements in computing technology and the ability to generate/collect vast amounts of data,
many organizations are quickly finding themselves data rich yet information poor. The goal of this
course is to expose students to techniques and technologies that will enable them to become key
players in helping organizations transform unstructured and structured data from various sources
including, social media, the web, databases and archival data, into meaningful and insightful information
that facilitates effective decision making.
COURSE OBJECTIVES
By the end of this course students should have an in depth understanding of the following:
1) Data extraction and cleansing:
a) Exploring and evaluating potential sources of data
b) Extracting and cleansing unstructured and structured data
c) Preparing data for storage or analysis
2) Data visualization and presentation:
a) Creating dashboards
b) Creating standard visualizations
c) Creating interactive visualizations
3) Data modeling and storage:
a) Modeling data (conceptual, logical and physical data modeling)
b) Designing and storing data in relational databases
This is a project-driven, hands-on course that is designed to give students exposure to real world data
management challenges. Students taking the course will learn and demonstrate their skills in using to
the following technologies:MS Excel, Tableau, R and SQL.
Start Date | End Date | Days | Time | Location |
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3/19/2018 | 5/10/2018 | T | 5:40pm - 9:15pm | PCBE 235 |
This course will introduce modern predictive and learning analytics techniques. The main emphasis will be on the applied aspects of these techniques with programming in the R language (an open source software that has gained tremendous popularity recently). Each lecture is designed to introduce new methods followed by real data applications from various applied fields (marketing, operations, finance, economics, and sports analytics). In introducing these predictive analytics tools, the course will feature discussions on four broadly defined areas of focus: 1) finding the most appropriate model that best represents the data, 2) selecting the optimal set of predictors, 3) reducing the dimension of data and dealing with correlated predictors, 4) improving prediction performance. A summary of topics that will be covered in the course is as follows: linear and non-linear regression analysis (ridge, Lasso, K-nearest neighbor, non-linear splines, neural networks), classification methods (logistic regression, linear discriminant analysis, support vector machines), tree based methods (regression/classification trees, bagging, boosting, random forests), unsupervised learning methods (principle components analysis, k-means clustering, hierarchical clustering). The course will be a mix of lectures and hands-on examples in class.
Start Date | End Date | Days | Time | Location |
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1/16/2018 | 3/9/2018 | Hours Arranged | TBA |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
3/19/2018 | 5/10/2018 | R | 5:40pm - 9:15pm | PCBE 115 |
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
3/19/2018 | 5/10/2018 | R | 5:40pm - 9:15pm | PCBE 115 |