In this course students will learn a number of important IT-concepts and computational skills. After this course students should be able to convert (econometric/OR/financial) models and ideas into sound, well-structured, and well-documented computer applications in four main modules.
- MATLAB: Students should be able to perform common tasks, such as equation solving, regression, interpolation, and visualization using efficient MATLAB methods.
- AIMMS: Students should be able to implement medium-scaled optimization problems accompanied by a user interface, which handles input and output of data, presents problem data and results, and offers the user the option to solve different variations of the problem.
- Databases: Computer science has developed various methods for storing, retrieving, and processing large amounts of data. In the databases module, students learn about one of these methods, namely the relational model, the related modeling approaches, and the SQL query language for data retrieval.
- Python: Based on the popular computer language Python in the data science community, a number of advanced computational aspects will be explained and practiced: object-oriented programming, advanced data structures, database access via Python, regular expressions, and data preparation & data mining in Python.
- efficient coding in Matlab and using the debugger/profiler
- structuring Matlab code, writing functions
- advanced methods: cell and structure arrays, anonymous functions
- visualization: advanced figure properties, 3D figures
- common tasks: equation solving, regression, interpolation
- element parameters, sparse execution, efficiency with higher dimensions
- page building, templates
- input/output using database tables
- relational databases and their theory
- relational algebra and the SQL query language
- object-oriented programming
- data types for managing large data sets
- database access via Python
- regular expressions
- data preparation and data mining in Python
|Course available for exchange students|
|Conditions of admission apply|
|Written test opportunities|
|Written test opportunities (HIST)|
|Computer tentamen (40%) / Computer exam (40%)||EXAM_01||SM 1||1||10-01-2020|
|Computer tentamen (40%) / Computer exam (40%)||EXAM_01||SM 2||2||23-06-2020||Required materials|
|Additional sources provided via Blackboard such as handouts, slides, MS SQL documentation, Python documentation.|
|MATLAB documentation, online on www.mathworks.com; selected chapters.|
|AIMMS documentation, online available on www.aimms.com; selected papers.|
|Title||:||Sams Teach Yourself Microsoft SQL Server T-SQL in 10 Minutes|
|Author||:||B. Forta (2016)|
|Title||:||How to Think Like a ComputerScientist: Learning with Python 3 Documenation|
|Author||:||Peter Wentworth, Jeffrey Elkner,Allen B. Downey and Chris Meyers (2019)|
|Assignment 2 (15%)|
|Assignment 3 (15%)|
|Assignment 4 (15%)|
|Computer exam (40%)|