Program Overview

Object-Oriented Python Programming for Business

Course Overview

The overarching objective of this course is to teach students the art and science of planning, developing, and applying object-oriented programs in Python. This programming style generates comprehensive software by combining self-contained and independently developed and validated mini-programs called objects, which are made to interact with one another within a program and are intended to represent real-world items.

Course Details

• ECTS: 5
• Form of instruction: Classroom instruction
• Form of examination: Take-home assignment (Assign)
• Language of instruction: English
• Level: Bachelor
• Location: Aarhus

Course Content

The course will cover the application of object-oriented Python programming to solve linear and non-linear business problems from a variety of fields, including, but not limited to, Microeconomics, Macroeconomics, Econometrics, and Finance. In the course, students will deepen their Python programming expertise through daily hands-on coding activities using the free comprehensive Spyder Integrated Development Environment (IDE), for Windows and Mac, and a set of Python program templates provided by the lecturer.

Description of Qualifications

This course engages students in the planning, development, and application of procedural, structured, and object-oriented Python programming to solve linear and non-linear business problems in a variety of fields. Students will generate results in tabular and graphical forms, model entire business enterprises, reuse code through class object inheritance and composition for rapid software development and extension, and develop an object-oriented Python programming style and vocabulary that will allow students to communicate and interact beneficially with computer scientists who develop large-scale business software.

Knowledge

Students will acquire knowledge about:

• Python programming fundamentals including expressions and operators, conditional statements, iterations and loops, and functions
• Python language data types, lists, dictionaries, tuples, and input/output operations through screen & keyboard and external data files
• Advanced Python language characteristics including libraries, modules, classes, objects, decorators, and linear and multiple inheritance structures
• Fundamentals of debugging and troubleshooting
• Applications of procedural, structured, and object-oriented Python programming to solve a variety of complex linear and non-linear business problems through large-scale data processing and utilization of numerical methods, from the NumPy and SciPy Python libraries
• Data visualization and animation through matplotlib
• Modeling in Python software of entire business enterprises through object-oriented programming

Skills

Through comprehensive daily hands-on Python programming activities, students will be able to:

• Decompose a business problem statement in terms of simpler tasks amenable to implementation in procedural functions and object-oriented classes and objects
• Organize business data in Python lists, dictionaries, and tuples
• Analyse and sort business data according to specified analysis and sorting rules
• Develop procedural, structured, and object-oriented Python programs to solve a variety of specific linear and non-linear business problems by combining separately developed and tested Python class objects
• Solve linear and non-linear systems of business equations using a Python implementation of Newton’s method
• Perform regression analyses on historical economics and financial data sets, based on a Python implementation of the least-squares method
• Use the NumPy and SciPy scientific-computing libraries to process large-scale business data sets
• Visualize and animate business data sets and solutions to business problems using the Matplotlib libraries
• Critically evaluate equivalent Python implementations of a software-based solution of a business problem

Competences

Students will develop proficiency and practice in the planning, development, and application of procedural, structured, and object-oriented Python programs for business applications. Students will be able to solve accurately linear and non-linear problems from a variety of business fields, including, but not limited to:

• Microeconomics
• Macroeconomics
• Econometrics
• Finance Students will develop Python solution expertise to investigate problems in:
• Consumer behavior
• Benefit maximization subject to budget constraints
• Self-contained and open economies
• Profit maximization and cost minimization Students will be able to apply Python programming to perform regression analyses on time series, such as those in the transnational categories in the Federal Reserve Economic Data website Students will manage to develop and use Python programs to perform calculations related to:
• Loans
• Annuities
• Cash flows
• Stocks and Bonds Based on the knowledge, skills, and programming experience acquired in this course, students will be able to apply object-oriented Python programming successfully in advanced business courses, including, but not limited to:
• Optimisation for Prescriptive Analytics
• Business Analytics with Python: Pricing and Revenue Management
• Applied Financial Econometrics
• Financial Engineering Students will be able to collaborate productively with computer scientists who develop business application software

Academic Prerequisites

• Familiarity with basic programming concepts (control and data structures) even from another programming language
• Quantitative methods including linear algebra and matrices
• Microeconomics (business economics), Macroeconomics, Mathematics, and Statistics

Literature

• Guetta D., Griffen M., Python for MBA’s , Columbia Business School Publishing, 2021
• Schneider J.B., Broschat S.L., Dahmen J., Algorithmic Problem Solving with Python , 2019 (available for free from the instructor)
• Alhabeeb M. J., Mathematical Finance , Wiley, 2012
• Lubanovic B., Introducing Python – _Modern Computing in Simple Packages, _O’Reilly, 2020
• Hilpisch Y. Financial Theory with Python – A Gentle Introduction , O’Reilly, 2022
• Python documentation at
• MatPlotLib documentation at
• NumPy documentation at
• Pandas documentation at
• SciPy documentation at

Examination

• Form of examination: Take-home assignment (Assign)
• Form of co-examination: No co-examination
• Assessment: 7-point grading scale
• Permitted exam aids: All

Requirements for Taking the Exam

In order to participate in the exam, there is an 80% attendance requirement.

Comments

The exam consists of a portfolio of written assignments, which students work on during the course. The assignments are individual, and students receive collective feedback before submitting a finalized version for grading. The finalized version should contain all assignments in one PDF file and be submitted through WISEflow at the end of the course. The portfolio consists of the following three assignments:

• Assignment 1: Development of an object-oriented Python program to solve an econometrics problem
• Assignment 2: Development of an object-oriented Python program that models an entire business enterprise
• Assignment 3: Utilization of NumPy and SciPy libraries to process large-scale business data and of MatPlotLib to visualize and animate the data The final portfolio should be no longer than 50,000 characters (including blanks).

Re-exam

Re-exam: written take-home exam (max. 36,000 characters including spaces). The dates for the first retake are:

• 27th October .00 noon: You will receive your exam question via WISEflow.
• 3rd November noon: Deadline for submitting via WISEflow. The dates for the second retake are:
• January 30th .00 noon: You will receive your exam question via WISEflow.
• February 6th .00 noon: Deadline for submitting via WISEflow. The format for the second re-take is the same as for the re-take.

Expected Student Workload

• Classroom attendance: 52 hours
• Preparation: 75 hours
• Feedback activity: 5 hours
• Papers (prerequisites): 15 hours
• Exam: 10 hours

Type of Course

• Type of course: Summer University
• Primary programme: Bachelor's Degree Programme in Economics and Business Administration
• Related programmes: Bachelor's Degree Programme in Business Administration and Commercial Law
• Department: Department of Economics and Business Economics
• Faculty: Aarhus BSS
• Location: Aarhus
• Maximum number of participants: Maximum 40 participants.
  • 10 seats are reserved for international exchange students from AU partner universities.
  • If there are more eligible applicants than available seats, they will be distributed according to the overall selection criteria and then the following selection criteria:
    • Randomized draw.