Fundamental programming for data analytics, 6 credits

Grundläggande programmering för dataanalys, 6 hp

TNK128

Main field of study

Computer Science and Engineering

Course level

First cycle

Course type

Programme course

Examiner

Nils Breyer

Director of studies or equivalent

Erik Bergfeldt

Education components

Preliminary scheduled hours: 0 h
Recommended self-study hours: 160 h

Available for exchange students

Yes

Possible deviation from course syllabus/programme syllabus

ECV = Elective / Compulsory / Voluntary

Course offered for		Semester	Period	Timetable module	Language	Campus	ECV
6MTSL	Intelligent Transport Systems and Logistics, Master's Programme	1 (Autumn 2023)	1	1	English	Norrköping	C
6MDIB	Master's Programme in Digital Construction Management	1 (Autumn 2023)	1	1	English	Norrköping	C

Main field of study

Computer Science and Engineering

Course level

First cycle

Advancement level

G1X

Course offered for

Master's Programme in Intelligent Transport Systems and Logistics
Master's Programme in Digital Construction Management

Prerequisites

Admission requirements for master level studies

Intended learning outcomes

In this course, you will learn how to use programming for problem solving and analysis of data.

After completing the course, the student should be able to:

Write scripts for data analysis using Python
Use basic data structures for problem solving in Python
Apply tools available in some commonly used Python packages
Generalize programming skills in Python to other script languages, specifically Matlab

Course content

Introduction to different types of programming paradigms and languages
Python basics: programming environment and documentation, program flow, variables, comments, numerical operators, loops, conditional statements
Python data structures and looping techniques: tuples, lists, dictionaries, sets, iterators, and generators
Python standard libraries and essential third-party packages for data manipulation, numerical computing, and visualization
Debugging of code
Data retrieval from various sources, such as json files, csv files, html files, XML files, databases or APIs
Introduction to Matlab programming and toolboxes

Teaching and working methods

Lectures, tutorials and labs

Examination

DAT1	Computer exam Python	3 credits	U, 3, 4, 5
LAB1	Laboratory work in Python	1.5 credits	U, G
LAB2	Laboratory work in Matlab	1.5 credits	U, G

Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.

Grades

Four-grade scale, LiU, U, 3, 4, 5

Department

Institutionen för teknik och naturvetenskap

Code	Name	Scope	Grading scale
DAT1	Computer exam Python	3 credits	U, 3, 4, 5
LAB1	Laboratory work in Python	1.5 credits	U, G
LAB2	Laboratory work in Matlab	1.5 credits	U, G

Grades for examination modules are decided in accordance with the assessment criteria presented at the start of the course.

Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)

There is no course literature available for this course in studieinfo.

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize

		I	U	A	Modules	Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
	1.1 Knowledge of underlying mathematics and science (G1X level)
	1.2 Fundamental engineering knowledge (G1X level)
	1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)
	1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)
	1.5 Insight into current research and development work
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
	2.1 Analytical reasoning and problem solving
	2.2 Experimentation, investigation, and knowledge discovery
	2.3 System thinking
	2.4 Attitudes, thought, and learning
	2.5 Ethics, equity, and other responsibilities
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
	3.1 Teamwork
	3.2 Communications
	3.3 Communication in foreign languages
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
	4.1 External, societal, and environmental context
	4.2 Enterprise and business context
	4.3 Conceiving, system engineering and management
	4.4 Designing
	4.5 Implementing
	4.6 Operating
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
	5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development
	5.2 Economic conditions for knowledge development
	5.3 Identification of needs, structuring and planning of research or development projects
	5.4 Execution of research or development projects
	5.5 Presentation and evaluation of research or development projects

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