PROG1000 Introduction to Programming Course description

This course focuses on the development of basic programming techniques, analytical thinking, comprehension of code, and problem-solving skills achieved through a programming-based approach. It aims to develop basic programming skills relevant for professional use within the realm of business and administration. It provides theoretical and practical exposure to different programming technologies and programming concepts such as object-oriented programming, web programming, etc.

To understand some of the concepts presented in this course, a knowledge of mathematics at high school algebra level is a definite advantage but not a requirement.

Language of instruction is English.

None.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills, and general competence:

Knowledge

The student has

• basic understanding of the operation and capabilities of software
• an understanding of what an algorithm is and the ability to use algorithmic problem-solving to address real-life problems in business and administration
• basic knowledge of the methods used to debug software
• basic knowledge of how processes within the realm of business and administration can be automated using software
• insight on how software are written, and an understanding of various types of programming languages and their function in various areas of business and administration.

Skills

The student has acquired an ability to

• format and write basic code
• explain how problem-solving principles are used in programming
• understand how to frame and elicit unstructured business and/or administration problems in order to solve them through programming,
• understand the steps required to make software more efficient

General competence

The student is

• proficient in planning and implementing a project plan for software development for business and administration
• able to identify and remediate bugs
• able to identify specific business and/or administration requirements that can be solved with programming
• able to communicate these requirements in a structured manner
• able to recognize the place that programming has within the domain of business and administration

The course will take a hands-on learning approach in addition to learning the theoretical concepts behind programming. Course participants will work in groups on a project relevant to the field of business and administration.

The following coursework requirement must be approved in order for the student to take the exam:

• Coursework 1: A set of homework exercises based on course-related topics. To complete these exercises, students must submit (1) a code repository, which contains the programming code used to answer the exercises, and (2) a written project report, which explains the programming code used to answer the exercises (maximum of one page, A4-size paper, single-spaced, 2 cm margins).

The course requirement can be completed individually or in a group of up to four students. To receive approval for the coursework requirement, all questions and exercises must be sufficiently answered. 

The purpose of the coursework requirement is to give students practical experience with the concepts covered in class. It also aims to help students reflect on how the course topics can be applied to different problems and datasets.

All required coursework must be completed and approved by the given deadline in order for the student to take the exam. If one or more coursework requirements have not been approved, the student will be given one opportunity to submit an improved version by a given deadline.

The exam in the course is a supervised exam of 4 hours. 

No aids are permitted.

The course will give an understanding of the status and potential for the use of sensors and models for decision support and system diagnostics in urban water systems. Since each sensor only measures in a single location, we need models to interpret the sensor data and give us an idea about what is happening throughout the system. The combination of models and sensor data will also be used for forecasting purposes and to increase data security by estimating if sensor data is realistic or not. The student will learn about the types of sensor data available now and in the foreseeable future and how to utilize the data for decision support and diagnostic purposes. The student will learn how to do model-based inference to learn about the state of a system by combining models and sensor data. The students will be exposed to numerous real-life cases. The course will cover the following key aspects: Simple conceptual modelling, Bayesian inference, Ensemble-based system diagnostics, forecasting, Real time control, data in water distribution systems, opportunities and challenges for smart control of urban water systems.

The students will make use of software such as Matlab and Python.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student will have

• broad knowledge about the various sensor types used within the different fields of urban water;
• deep knowledge about the most common properties, deficiencies and uncertainties for the various sensor types;
• insight into the current state-of-the-art of sensor and model usage in current urban water management;
• good understanding of the different requirements for models for design, diagnostics and forecasting.

Skills

The student

• can design and implement simple hydrological/hydraulic models;
• can interpret data using a model;
• can use a model to critically assess sensor data and balance conflicting data against each other for systemwide diagnostics.

General competences

The student will know how to

• integrate knowledge into practice in the water industry to apply this to a range of scenarios, to critically analyse, evaluate, interpret and report on information from a range of sources and to solve complex problems systematically and creatively;
• critically assess sensor data.

The teaching will consist of a combination of:

Lectures & discussions

Independent studies including video recordings and online exercises

Coursework assignments

Guest lectures

Practical use of tools and software.