MANAGERIAL ANALYTICS

This course belongs to the elective (free choice) activities offered under the umbrella of the master's degree program Management. The course final goal is to answer the following question: "What is analytics and how to use them to make better decisions?" In essence, the course presents and discusses some widely used analytical and conceptual tools useful to examine and resolve decision problems in a business context. In particular, it has been conceived to provide students knowledge related to the most important prescriptive techniques to support business decisions with particular attention to their formulation, implementation, and analysis by means of electronic spreadsheet and other dedicated software. Particular emphasis will be given to the application: with the support of class examples, the student will learn how to represent complex managerial problems in an analytical way, how to identify and quantify the fundamental variables involved and their links. The course also aims to provide skills and competencies on the use of data available to implement analytical models to support decisions, how to read the results provided by the models and how to interpret them to propose appropriate solutions to the managerial challenges under analysis.

Knowledge and understanding skills
Through the attendance of classes, the individual and group-based activities proposed through the Moodle platform, as well as through the individual study the student will acquire the following knowledge and understanding skills:
learn and classify some of the most used analytical and conceptual tools (descriptive, predictive and prescriptive) needed to examine and solve complex decision problems;
learn and understand how to translate a complex decision problem into models that can be solved analytically;
learn how to hierarchize a complex problem into simpler instances that are appropriately integrated with each other;
acquire familiarity with some basic techniques to support managerial decisions such as multi-criteria decision analysis, hierarchical quantitative techniques, decision trees;
learn how some recent technologies such as blockchains and artificial intelligence can be fruitfully applied to solve problems related to managerial issues

Ability to apply knowledge and understanding.
Through the individual study, by reading the materials suggested by the instructor, through the discussion of business cases, the interaction with external experts, the execution of software experiments, the development of homework based on practical examples, students will have acquired the following skills to apply their knowledge:
be able to use the main techniques to support business decisions, based on analytical and conceptual tools, useful for examining and solving management problems;
be able to use spreadsheets to visualize, analyze and solve practical cases of complex decisions;
be able to interpret the data and results provided by mathematical-statistical techniques and dedicated software with respect to complex decision-making problems;
be able to understand the role of some innovative tools such as blockchains and artificial intelligence to design complex architectures and run prescriptive analysis to support decisions

Making judgments, communication, lifelong learning skills.
Thanks to the discussion of business cases and interaction with peers, instructors and external experts, the student will learn:
how to formulate rational justifications to support their own judgments (analytical thinking);
how to understand the relative strengths and limits of their judgment based on hypotheses, data, and models (critical thinking);
understand the implications of analytics in problem-solving and decision making (complex problem solving);
how to formulate and communicate adequately their analysis and their economic-financial interpretation of company facts, also using dedicated software (communication skills).

This course emphasizes applications over theory. Some (few) basic knowledge in Mathematics are appreciated. Familiarity with the software Excel is welcome. For a reference course in mathematics (at the bachelor level) see http://www.unive.it/data/course/257915/programma

1. Managerial analytics in a nutshell. Descriptive, Predictive and Prescriptive analytics. What’s on?
2. Prescriptive analytics: optimization and decision trees
3. Methodologies based on multicriteria decision analysis and real applications
4. Decision trees: classical representation, simulations and recent development of Machine Learning
5. Artificial intelligence and its role in solving new challenges in the field of management

Most of the teaching material used in class will be prepared and made available by the instructor on the Moodle platform.

Suggested additional readings
Oakshott L. (2016) Essential Quantitative Methods for Business, Management and Finance VI Ed. Palgrave (also available in ebook).
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.

The evaluation is based on the commitment during classes, on ongoing activities and an oral exam. The final grade is assessed on a 30-point basis. 18 points are the minimum grade to receive the 6 ECTS related to this course.

Student’s commitment (10%): the instructor monitors and evaluates the participation/commitment of students during classroom activities and on Moodle.
Moodle Quizzes / Exercises (20%): students will be offered quizzes and/or exercises through the Moodle platform to be solved individually.
Group work (30%): students will be divided into work groups. Each group will be assigned a topic in which some of the methodologies seen in class must be implemented in order to make a "good" decision. The group must recognize, organize and process the significant data available in order to obtain analytical results to solve the proposed problem.
Oral exam (40%): students will be offered theoretical questions or short exercises related to the materials analyzed in the classroom. The student will be also asked to show his/her contribution in developing the group work.

Evaluation of non-attending students.
Although highly recommended, participation in classes is not strictly mandatory. Non-attending students will be required to perform activities on Moodle (20% final grade), an individual assignment with a final written report (30%) and an oral exam (50%).

The course consists of 15 classes of two academic hours each; the activity in class is both theoretical and practical: active participation is highly recommended. Experiments in Excel will be proposed during classes to master the material and provide insights for decision making and problem-solving.
The teaching activities are displaced in frontal activities (about 50%), practical activities (about 30%), flipped-room activities (around 20%). During frontal classes the technical and theoretical issues related to the course topics will be addressed; during the practical activities experiments in Excel will be proposed live to master the material and provide insights for decision making and problem-solving; during the "flipped" activities, the students will be in charge to explain classmates some topics selected by the teacher and, possibly, to present the results of the group work to the class. The course is accompanied by an online platform offered on Moodle, where the instructor will propose activities and exercises to be solved during the course.

The participation of international students is warmly encouraged. Bachelor-level international students should be aware that the techniques used in class, the teaching methodologies and the examination procedures are designed for a master-level course.

Accessibility, Disability and Inclusion.
Concerning Accommodation and support services for students with disabilities and students with specific learning impairments, Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

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