Teaching Methodologies
The CU adopts teaching methodologies aligned with an active pedagogical model, focusing on the integration of theory and practice to
achieve the learning objectives. These methodologies encourage active student participation and the application of mathematical concepts
to real marketing situations.
– Theoretical Presentation: Delivery of core theoretical concepts in linear algebra, functions, and differential calculus, with detailed
explanations of topics such as matrices, systems of linear equations, derivatives, and linear programming.
– Practice-Oriented Exercises: Classroom exercises that emphasize the application of linear algebra, functions, and derivatives to real-world
marketing scenarios.
– Guided Discussions: Facilitated discussions to stimulate critical thinking and connect mathematical content to business contexts.
– Case Studies: Practical case studies that demonstrate the use of mathematical techniques in data analysis, market modeling, and
campaign optimization.
– Technology Integration: Use of tools like Excel or GeoGebra to enhance mathematical understanding and streamline problem-solving.
– Supporting Materials: Provision of supplementary resources, such as exercise lists and case studies, to complement in-class learning.
– Simplex Method Training: Hands-on practice with the Simplex method and sensitivity analysis using computational tools, preparing
students for corporate applications.
– Independent Exploration: Encouragement of self-directed research to deepen understanding and explore practical applications in
marketing.
– Flipped Classroom Option: Potential inclusion of flipped classroom strategies, where students engage with preparatory materials (texts,
videos, etc.) prior to class discussions.
– Continuous Assessment: Periodic evaluation through exercises, tests, and presentations to ensure gradual comprehension of concepts
and provide timely feedback.
The practical application of CU content fosters the development of analytical and strategic competencies essential for solving real marketing
problems.
Learning Results
Learning Objectives:
O1. Apply concepts of linear algebra and differential calculus to analyze marketing problems, such as segmentation and sales forecasting.
O2. Model and interpret market phenomena using functions and mathematical models.
O3. Solve optimization problems in advertising campaigns and resource allocation.
O4. Use computational tools to address mathematical problems applied to marketing.
O5. Develop skills in interpreting quantitative data and making informed decisions based on conducted analyzes.
These objectives are aligned with the teaching method focused on applying mathematical techniques to solve real-world problems in the
context of marketing.
Program
1. Linear Algebra Applied to Marketing
1.1. Matrices and systems of linear equations.
1.2. Applications in market analysis and segmentation.
2. Real-Valued Functions of a Real Variable
2.1. Types of functions and their properties.
2.2. Interpretation of graphical representations of linear, exponential, and logarithmic functions.
2.3. Limits and continuity.
2.4. Derivatives: definition, geometric interpretation, and calculation.
2.5. Differentials and approximated values.
2.6. Applications of derivatives in optimization problems.
2.7. Modeling of advertising resource allocation.
3. Functions of Two Variables
3.1. General principles.
3.2. Partial derivatives.
3.3. Optimization with and without constraints.
4. Linear Programming
4.1. Modeling marketing problems such as budget allocation.
4.2. Simplex method.
4.3. Sensitivity analysis.
Internship(s)
NAO
Bibliography
Goldstein, L. J., Lay, D. C., Schneider, D. I. (2000). Matemática Aplicada: Economia, Administração e Contabilidade. (8ª edição). Bookman.
Harshbarger, R. J., Reynolds, J. J. (2006). Matemática Aplicada: Administração, Economia e Ciências Sociais e Biológicas. (7ª Edição).
McGraw-Hill.
Hillier, F., & Lieberman, G. (2020). Introduction to Operations Research. McGraw-Hill Education.
Pires, C. (2011). Cálculo para Economia e Gestão. Escolar Editora.
Simon, C. P., Lawrence, B. (2004). Matemática para Economistas. Bookman.
Stewart, J. (2015). Calculus: Metric Version. Cengage Learning Brooks Cole.
Sydsæter, K., Hammond, P., Strøm, A. (2012). Essential Mathematics for Economic Analysis. (4th ed.). Pearson Education Limited.
Tan, S. T. (2001). Matemática Aplicada à Administração e Economia. Pioneira Thomson Learning.
Winston, W. L. (2014). Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley.