Computational Tools for Business Analytics
Γρ. Κορωνάκος, Α. Μπουσδέκης, Κ. Λιαγκούρας
Περιγραφή
Το μάθημα περιλαμβάνει 5 διαλέξεις και 1 εργαστήριο/φροντιστήριο.
Περιεχόμενο:
- Business analytics with Python
- Computational Tools: Optimization techniques such as Hill Climbing, Evolution Strategies, Particle Swarm Optimization and Simulated Annealing
- Methods, algorithms and case studies of business analytics for stock market forecasting
- Implementation of python programming for solving business analytics problems
- Methods, algorithms and case studies for business analytics in Industry 4.0. Algorithms and use of Python libraries for handling the uncertainty of manufacturing data in accordance with Industry 4.0 architectures and frameworks.
- Python libraries (ΝumPy, Matplotlib, Pyomo, SymPy, etc.), jmetal metaheuristic library
Το μάθημα περιλαμβάνει 5 διαλέξεις και 1 εργαστήριο/φροντιστήριο.
Περιεχόμενο:
- Business analytics with Python
- Computational Tools: Optimization techniques such as Hill Climbing, Evolution Strategies, Particle Swarm Optimization and Simulated Annealing
- Methods, algorithms and case studies of business analytics for stock market forecasting
- Implementation of python programming for solving business analytics problems
- Methods, algorithms and case studies for business analytics in Industry 4.0. Algorithms and use of Python libraries for handling the uncertainty of manufacturing data in accordance with Industry 4.0 architectures and frameworks.
- Python libraries (ΝumPy, Matplotlib, Pyomo, SymPy, etc.), jmetal metaheuristic library
Το μάθημα περιλαμβάνει 5 διαλέξεις και 1 εργαστήριο/φροντιστήριο.
Περιεχόμενο:
- Business analytics with Python
- Computational Tools: Optimization techniques such as Hill Climbing, Evolution Strategies, Particle Swarm Optimization and Simulated Annealing
- Methods, algorithms and case studies of business analytics for stock market forecasting
- Implementation of python programming for solving business analytics problems
- Methods, algorithms and case studies for business analytics in Industry 4.0. Algorithms and use of Python libraries for handling the uncertainty of manufacturing data in accordance with Industry 4.0 architectures and frameworks.
- Python libraries (ΝumPy, Matplotlib, Pyomo, SymPy, etc.), jmetal metaheuristic library
