The aim of the seminar is to develop the main methodologies of data analysis and to provide the relevant technical skills through case studies, so that participants are able to perform all the functions required for decision making. In particular, the basic techniques of Machine Learning and Artificial Intelligence in combination with the main econometric models will be presented. The seminar deals in detail with the issues that fall within these fields, enabling participants to understand the theoretical background of these methods and mainly to specialize in their practical application in the professional environment in which they operate.

• Entrepreneurs
• Financial Management Executives
• Commercial and Marketing Executives
• Supply Chain Executives
• Project Management Professionals
• Internal Auditors
• Executives outside Financial Departments who need to be aware of the financial risks faced by their business
• Human Resources Professionals
• Legal Professionals
• Financial analysts, portfolio managers, venture capital executives who are involved in the field of investments and wish to broaden and deepen their knowledge and the tools they can use in their work.

1. Econometric data analysis models
   
   
   1. Linear Discriminant Analysis,
   2. Binomial – Multinomial logistic regressions,
   3. Models with categorical variables,
      
      
2. Artificial Intelligence (AI) and Machine Learning (ML) models
   
   
   1. Artificial Neural Networks (Multilayer Perceptron – Radial Basis Function),
   2. Decision Trees,
   3. Random Forests,
   4. Support Vector Machines.
      
      
3. Applications in Finance
   
   
   1. Customer creditworthiness assessment
   2. Forecast of the course of key economic figures
   3. Determination of independent variables and determination of the significance of each
      
      
4. Applications/Case Studies in Management Accounting using Excel
   
   
   1. Preparation of production cost report,
   2. Prepare production budgets and flexible budgets,
   3. Allocation of production overheads by activity costing method (ABC method);
   4. Calculation of optimal product mix,
   5. Maturity of accounts receivable,
   6. Fix optimization issues.
      
      
5. Development of Case Studies – International Articles
   
   
6. 1. Credit risk and customer creditworthiness forecasting.
   2. Predicting sales revenue and estimating consumer characteristics
   3. Forecasting the course of stocks / financial factors
      
      
7. Specialized Case Studies

1. 1. Development of specialized case studies in collaboration with participants to highlight the value and understand the operation of AI and ML methods

1. Combine analytical thinking with practical applications.
2. Understand the necessity of applying AI and ML methods.
3. Select the appropriate model based on the case data to be checked.
4. Manage big data databases and apply the main analysis methodologies using appropriate software.
5. Extract the necessary information from databases and carry out analysis concerning the extraction of patterns for use in financial forecasting and risk management.
6. Come up with investment strategies and policy proposals based on the conclusions of the inductive analysis