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. Basic Excel.
The prerequisites are the same for both attending and non-attending students.
sample means and variances, probability distributions).
Geometric series.
Fundamentals of optimization in one or more variables. Basic Excel.
The prerequisites are the same for both attending and non-attending students.
Program
There are no content differences in the programs between attending and non-attending
students.
The course is divided into four thematic areas, spread over 18 classroom-based lectures,
each lasting two hours. The thematic areas are named and scheduled as follows:
1) Pivot Tables and Statistical Analysis of Datasets (first 3 lectures). The topics covered in
this module will be: main functions of Excel and their use; creation of Pivot Tables; statistical
analysis of databases using Pivot Tables.
2) VBA Language and Applications (lectures 4 to 6). The topics covered in this module will
be: introduction to VBA language; creation of routines; main commands and applications for
database processing; simulation of decision-making scenarios.
3) Solver and Decision Optimization (lectures 7 to 15). The topics covered in this module will
be: decision-making problems in contexts of scarce and constrained resources; general
functioning of Excel Solver and its applications to decision-making problems with
continuous, discrete, and binary variables, and to optimal transport problems on networks.
4) Financial Modeling and Forecasting using Excel (last 3 lectures). The topics covered in
this module will be: cash flows, Net Present Value (NPV), Internal Rate of Return (IRR), and
financial feasibility assessment of a project (NPV and IRR functions in Excel). Creation of
balance sheet and income statement prototypes. Analysis of "pro-forma financial
statements" and use of Excel's "What-If Analysis" tool.
students.
The course is divided into four thematic areas, spread over 18 classroom-based lectures,
each lasting two hours. The thematic areas are named and scheduled as follows:
1) Pivot Tables and Statistical Analysis of Datasets (first 3 lectures). The topics covered in
this module will be: main functions of Excel and their use; creation of Pivot Tables; statistical
analysis of databases using Pivot Tables.
2) VBA Language and Applications (lectures 4 to 6). The topics covered in this module will
be: introduction to VBA language; creation of routines; main commands and applications for
database processing; simulation of decision-making scenarios.
3) Solver and Decision Optimization (lectures 7 to 15). The topics covered in this module will
be: decision-making problems in contexts of scarce and constrained resources; general
functioning of Excel Solver and its applications to decision-making problems with
continuous, discrete, and binary variables, and to optimal transport problems on networks.
4) Financial Modeling and Forecasting using Excel (last 3 lectures). The topics covered in
this module will be: cash flows, Net Present Value (NPV), Internal Rate of Return (IRR), and
financial feasibility assessment of a project (NPV and IRR functions in Excel). Creation of
balance sheet and income statement prototypes. Analysis of "pro-forma financial
statements" and use of Excel's "What-If Analysis" tool.
Books
Financial Modeling, 3rd edition, Simon Benninga, 2008.
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.
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.
Bibliography
Financial Modeling, 3rd edition, Simon Benninga, 2008.
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.
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.
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.
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 computer-based test using the Moodle platform. No exemptions,
intermediate tests, or any other evaluations are provided before the actual exam.
The questions will involve specific analyses that the student must perform on datasets
provided at the time of the exam.
The exam will be open-book, and students will have access to all codes and routines
discussed in class.
Students who withdraw or do not pass the exam may retake it in the same session.
If required by the instructor, the student may be called for an interview based on any topic
covered in class.
The methods and criteria for assessing learning are the same for both attending and
non-attending students.
The final grade is expressed on a scale of thirty according to the following scheme.
Not suitable: significant deficiencies and/or inaccuracies in knowledge and understanding of
the topics; limited analysis and synthesis skills.
18-20: barely sufficient knowledge and understanding of the topics with possible
imperfections; sufficient analysis, synthesis, and judgment skills.
21-23: routine knowledge and understanding of the topics; correct analysis and synthesis
skills with consistent logical argumentation.
24-26: fair knowledge and understanding of the topics; good analysis and synthesis skills
with rigorously expressed arguments.
27-29: complete knowledge and understanding of the topics; notable analysis and synthesis
skills. Good judgment autonomy.
30-30L: excellent level of knowledge and understanding of the topics. Remarkable analysis
and synthesis skills and judgment autonomy. Solutions to problems obtained in an original
way.
intermediate tests, or any other evaluations are provided before the actual exam.
The questions will involve specific analyses that the student must perform on datasets
provided at the time of the exam.
The exam will be open-book, and students will have access to all codes and routines
discussed in class.
Students who withdraw or do not pass the exam may retake it in the same session.
If required by the instructor, the student may be called for an interview based on any topic
covered in class.
The methods and criteria for assessing learning are the same for both attending and
non-attending students.
The final grade is expressed on a scale of thirty according to the following scheme.
Not suitable: significant deficiencies and/or inaccuracies in knowledge and understanding of
the topics; limited analysis and synthesis skills.
18-20: barely sufficient knowledge and understanding of the topics with possible
imperfections; sufficient analysis, synthesis, and judgment skills.
21-23: routine knowledge and understanding of the topics; correct analysis and synthesis
skills with consistent logical argumentation.
24-26: fair knowledge and understanding of the topics; good analysis and synthesis skills
with rigorously expressed arguments.
27-29: complete knowledge and understanding of the topics; notable analysis and synthesis
skills. Good judgment autonomy.
30-30L: excellent level of knowledge and understanding of the topics. Remarkable analysis
and synthesis skills and judgment autonomy. Solutions to problems obtained in an original
way.