What makes this story so compelling is the social media aspect of both the crisis itself and the strategy for managing the crisis.
Courses typically are divided into weekly or topical modules. Students begin each course with skills development.
They progress into problem solving through working on specific business applications and typically use real data to gain business insights.
Among the many methods of marketers today, the written case study remains a tried and true practice to attract new customers. "B2B small business marketers say case studies and in-person events are the most effective tactics they use," the Content Marketing Institute reported in its most recent B2B. Through NSTA, you'll find leading resources for excellence in teaching and learning and experience growth through robust professional development. Plus you'll meet colleagues across all science disciplines, all grade bands and teaching stages, from the newest teacher to the veteran administrator, who share a passion for science education. LinkedIn Learning combines % of alphabetnyc.com’s courses with LinkedIn insights to give you personalized learning.
Topics include statistical independence, conditional probability, Bayes theorem, discrete and continuous distributions, expectation and variance, decision trees, sampling and sampling distributions, interval estimation, correlation and linear regression.
We then cover basic control flow, including conditionals and loops. Finally, we will cover functions, program design, basic algorithms, and data structures. Most of the course materials will be introduced through Python and the final lectures will help students to port this knowledge to R.
Topics include organizational structure and the role of different business domains, including accounting, finance, operations and marketing, and how they relate to each other in an organization.
This course focuses on application of fundamental concepts from probability and statistics to drawing inferences from data. Assignments with applications to real-world data are an integral part of the course. The course topics include relational data management systems, theory of databases and models CAP, ACID, distributed computing and storagedocument MongoDBand other models for big data.
Ensuring that there is progression of learning skills development (e.g. analysis to synthesis etc.) when using a series of case studies is important, rather than repetition of the same skills. Case . QUALITATIVE RESEARCH METHODS AND ANALYSIS Course Syllabus, Teaching Plan and Reading List The proposed research course has two separate but closely related components – qualitative research and The student will engage in a mini-research project to obtain. GP Strategies is an expert in training and development. Browse our case studies to learn how to drive efficiency in training, onboarding procedures and more. Case Study | PSM and the Race Government Accountability Office Strategic Curriculum Project.
The course also provides a basic conceptual introduction to Hadoop, Map-reduce, Hive, Apache Spark in general, the big data architecture.
Students will learn visual representation methods and techniques that increase the understanding of complex data and models. Principles will be drawn from statistics, graphic design, cognitive psychology, information design, communications and data mining.
Specific topics covered include design principles for charts and graphs and common visualization tools Tableau, Google Visualization API, Pythoneffective presentations, dashboard design and web-based visualizations.
Specific topics include linear regression logistics regression, k-nearest neighbors and SVMs and unsupervised learning principal components and clustering methods: Specific topics include model and variable selection overfitting and overconfidence; bias-variance tradeoffs; information criterions and cross-validation; model averaging and ensemble learning; feature selection; regularization, shrinkage and LASSO Estimators ; nonlinear prediction methods tree-based methods: The application of large-scale optimization models can bring a critical competitive advantage to many firms.
This course focuses on developing such optimization models for operational and strategic decision making, with applications that include vehicle routing, employee scheduling, network design and capacity planning.
Methodologies include linear programming, integer programming, nonlinear programming, constraint programming, heuristics, and column generation. Building on a set of multidisciplinary cases cutting across functional areas of business, this course integrates the three increasing levels of analytics involved in reaping business value from data in a given problem: At the micro level, it covers factors for working in and managing an effective work team, including building teams, team contracting, team coordination and team creativity.
Macro topics include team networks, informal and formal organizational networks, communication networks and innovation culture. Students learn delivery skills; how to construct arguments and problem-solve for decision makers; and how to understand what these audiences need from them.
Students are evaluated on presentation assignments that are relevant to business analytics.
Students are asked to manage a large data set, develop appropriate quantitative models and analytical insights, interact with the company, and deliver midterm and final presentations to company executives and faculty.
The application of unstructured data mining tools, such as text mining, can help firms derive high quality information from text and bring a critical competitive advantage. This course focuses on tools for analyzing unstructured text data for strategic decision making, with applications that include white space identification, understanding customer needs and perceptions of a brand, identifying market structure through user generated content, and demand forecasting with social media or customer review data.
Topics include supply chain design, demand forecasting, inventory planning, sales and operational planning, revenue management, staffing in service organizations, and healthcare management.
The underlying feature in these applications is managing the risk that arises from supply and demand mismatches with the goal of maximizing enterprise value. Strategies like interactive marketing, customer relationship management and database marketing push companies to utilize the information they collect about their customers to make better marketing decisions.
Marketing transaction data — which is a common type of big data — often forms the core set of information used for making marketing decisions. This course focuses on how analytical techniques from data mining, machine learning and statistical modeling can be applied to solve marketing problems, using a series of data intensive case studies.
Specifically, the case studies considered include pricing decision support systems using retail transaction data, understanding customer churn in the cell-phone market, upgrading freemium customers to paying customers, and lifetime cycles in direct marketing.
In addition, it highlights the way in which machine learning tools have enhanced established methods for tackling these problems. Specific topics include hiring retention and attrition, developing talent culture e.Professional Development Activity X X Research Paper X Project X Oral Presentation X Mini empirical analysis project X Critical Analysis of an article and a dissertation X X EDA EDA Mid-term and Final Examination X X Point of Law Paper X X Legal Case Studies X X Briefs X X.
Legal Case Studies X X EDA Participant/Non. The course focuses on developing this foundation knowledge through classroom discussions and case analysis with guest experts in the various aspects of mutual funds. FIN Case Studies .
Case studies and scenarios illustrating ethical dilemmas in business, medicine, technology, government, and education. The Case Study / Case Studies Method is intended to provide students and Facultys with some basic information. This Case Study Method discuss what the student needs to do to prepare for a class / classroom, and what she can expect during the case discussion.
We also explain how student performance is evaluated in a case study based course. The Department of Psychological Sciences combines the areas of study found in many psychology departments with those typically found . Economic Analysis of Investment in Real Estate Development Projects, Part 1.
2 Exhibit The “Real Estate System”: Interaction of the Space Market, Asset Market, & Development Industry in the case of a condominium project, the buyer’s ability to obtain suitable purchase Two types of project budgets are important to be developed.