AUTOMATED DECISION MAKING IN BUSINESS AND ECONOMICS
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
The course aims to provide the methodological computer skills (with the use of applications and programming languages such as Excel, VBA, python etc.) for the analysis of large-scale data from a range of sources, such as economic databases, historical events, text, social media, sensors, images or speech with the purpose of making decisions in a range of contexts, including economic policies, business administration, public administration, health, education, law, employment, transport and economic forecasts. Students will be able
1) to process and clean raw large databases (detection and correction of typos, missing entries, interpolation, misspecified data and so on);
1) to understand the structure of a processed database, its main features and for what purposes it can be used;
2) to make rigorous statistical inferences on large processed databases via computer algoritghms;
3) to produce forecast scenarios for the variables of interest derived from the database under analysis;
4) to assess the short and long run impacts of standardized policies on the system under study;
5) to solve linear and non-linear constrained optimization problems related to business administration;
KNOWLEDGE AND UNDERSTANDING:
Students will learn the methodologies that the course aims to teach through practical exercises and examples with an approach inspired by learning by doing and trial and error.
The memorization of codes or particular procedures will not be required, students will have to develop autonomous problem solving skills that can help them in decision making processes supported by data analytics.
APPLYING KNOWLEDGE AND UNDERSTANDING:
Through the knowledge and understanding acquired the student must be able to develop skills / abilities for:
1) understanding what type of IT tool is necessary for the analysis of a database according to the same;
2) understanding the structure of a database, what types of errors may be present, how to correct them and/or evaluate their impact based on specific needs;
3) planning autonomously a data-supported decision-making strategy;
MAKING JUDGEMENTS:
Develop autonomous reflections on various issues related to decision making strategies supported by data analytics. Have the ability to integrate different programming skills and languages to manage complex and incomplete dataset, as well as to make statistically validated decision based on limited or incomplete information. The course approach seeks to link economic and business topics to data analytics to promote attitude towards problem-solving. The student must therefore be able to choose the approaches and tools necessary for decison making supported by data analytics with the ability to collect and interpret data, as well as to use information sources.
COMMUNICATION SKILLS:
Students must be able to highlight the flow of information in the description of a decision making process and learn, specifically, how to use VBA, Excel and its solver.
LEARNING SKILLS:
- to interpret different types of large dataset related to business, economics, society, innovation, historical events and media.
- to peform statistical anayses of large dataset through Excel, VBA, python;
- to translate a business or political decision-making process into a constrained optimization problem and being able to implement the corresponding solution procedure.
The course aims to provide the methodological computer skills (with the use of applications and programming languages such as Excel, VBA, python etc.) for the analysis of large-scale data from a range of sources, such as economic databases, historical events, text, social media, sensors, images or speech with the purpose of making decisions in a range of contexts, including economic policies, business administration, public administration, health, education, law, employment, transport and economic forecasts. Students will be able
1) to process and clean raw large databases (detection and correction of typos, missing entries, interpolation, misspecified data and so on);
1) to understand the structure of a processed database, its main features and for what purposes it can be used;
2) to make rigorous statistical inferences on large processed databases via computer algoritghms;
3) to produce forecast scenarios for the variables of interest derived from the database under analysis;
4) to assess the short and long run impacts of standardized policies on the system under study;
5) to solve linear and non-linear constrained optimization problems related to business administration;
KNOWLEDGE AND UNDERSTANDING:
Students will learn the methodologies that the course aims to teach through practical exercises and examples with an approach inspired by learning by doing and trial and error.
The memorization of codes or particular procedures will not be required, students will have to develop autonomous problem solving skills that can help them in decision making processes supported by data analytics.
APPLYING KNOWLEDGE AND UNDERSTANDING:
Through the knowledge and understanding acquired the student must be able to develop skills / abilities for:
1) understanding what type of IT tool is necessary for the analysis of a database according to the same;
2) understanding the structure of a database, what types of errors may be present, how to correct them and/or evaluate their impact based on specific needs;
3) planning autonomously a data-supported decision-making strategy;
MAKING JUDGEMENTS:
Develop autonomous reflections on various issues related to decision making strategies supported by data analytics. Have the ability to integrate different programming skills and languages to manage complex and incomplete dataset, as well as to make statistically validated decision based on limited or incomplete information. The course approach seeks to link economic and business topics to data analytics to promote attitude towards problem-solving. The student must therefore be able to choose the approaches and tools necessary for decison making supported by data analytics with the ability to collect and interpret data, as well as to use information sources.
COMMUNICATION SKILLS:
Students must be able to highlight the flow of information in the description of a decision making process and learn, specifically, how to use VBA, Excel and its solver.
LEARNING SKILLS:
- to interpret different types of large dataset related to business, economics, society, innovation, historical events and media.
- to peform statistical anayses of large dataset through Excel, VBA, python;
- to translate a business or political decision-making process into a constrained optimization problem and being able to implement the corresponding solution procedure.
Prerequisites
Fundamentals of elementary statistics (probability, conditional probability, expected values, sample means and variances, probability distributions).
Geometric series.
Fundamentals of optimization in one or more variables.
Geometric series.
Fundamentals of optimization in one or more variables.
Program
Introduction to Excel: main functions and use.
Creating Pivot Tables.
Statistical analysis of databases through Pivot tables.
Basics of modeling and financial evaluation.
Introduction to the VBA language: creation of routines, main commands and applications for processing databases.
Decision problems in contexts of scarce or constrained resources: general functioning of Excel solver and its applications to decision problems.
Creating Pivot Tables.
Statistical analysis of databases through Pivot tables.
Basics of modeling and financial evaluation.
Introduction to the VBA language: creation of routines, main commands and applications for processing databases.
Decision problems in contexts of scarce or constrained resources: general functioning of Excel solver and its applications to decision problems.
Books
Microsoft Excel VBA and Macros (Office 2021 and Microsoft 365), Bill Jelen and Tracy Syrstad, Pearson Education (US), 2022.
Using Excel for Business and Financial Modelling: A Practical Guide.
Danielle Stein Fairhurst, John Wiley & Sons Inc, 2019.
Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics. Cliff Ragsdale, South-Western College Pub, 2017.
Using Excel for Business and Financial Modelling: A Practical Guide.
Danielle Stein Fairhurst, John Wiley & Sons Inc, 2019.
Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics. Cliff Ragsdale, South-Western College Pub, 2017.
Bibliography
Microsoft Excel VBA and Macros (Office 2021 and Microsoft 365), Bill Jelen and Tracy Syrstad, Pearson Education (US), 2022.
Using Excel for Business and Financial Modelling: A Practical Guide.
Danielle Stein Fairhurst, John Wiley & Sons Inc, 2019.
Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics. Cliff Ragsdale, South-Western College Pub, 2017.
Using Excel for Business and Financial Modelling: A Practical Guide.
Danielle Stein Fairhurst, John Wiley & Sons Inc, 2019.
Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Business Analytics. Cliff Ragsdale, South-Western College Pub, 2017.
Teaching methods
Lecture will take place face-to-face. The teaching is frontal and integrated with the constant use of computer applications and the moodle platform for exercises and intermediate checks. The teaching method adopted is based on learning through concrete examples and problems.
Exam Rules
The exam consists of a written test via pc and on the moodle platform.
The questions will concern specific analyses that the student will have to carry out on databases provided at the exam.
The exam will be in open book format and students will have access to all the codes and routines discussed in class.
Students who withdraw or fail the exam are allowed to take the exam again in the same exam session.
The questions will concern specific analyses that the student will have to carry out on databases provided at the exam.
The exam will be in open book format and students will have access to all the codes and routines discussed in class.
Students who withdraw or fail the exam are allowed to take the exam again in the same exam session.