Data Analytics for Smart Cities, 6 credits

Dataanalys för smarta städer, 6 hp

TNK117

The course is disused.

Main field of study

Electrical Engineering Transportation Systems Engineering

Course level

Second cycle

Course type

Programme course

Examiner

Nikolaos Pappas

Director of studies or equivalent

Erik Bergfeldt

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Available for exchange students

Yes
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CKTS Communications, Transport and Infrastructure, M Sc in Engineering 9 (Autumn 2020) 1 3 English Norrköping E
6CKTS Communications, Transport and Infrastructure, M Sc in Engineering (Master Profile Smart Cities) 9 (Autumn 2020) 1 3 English Norrköping C
6MTSL Intelligent Transport Systems and Logistics, Master's Programme 3 (Autumn 2020) 1 3 English Norrköping E

Main field of study

Electrical Engineering, Transportation Systems Engineering

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Master's Programme in Intelligent Transport Systems and Logistics
  • Communications, Transport and Infrastructure, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Basic knowledge in linear algebra, calculus, statistics and probability theory as well as computer programming.

Intended learning outcomes

In this course, you will learn how to utilize and learn from data in order to gain insights for decisions, especially in the area of smart cities. We will examine real world examples from for example traffic management, logistics, telecommunications and crowd sensing. After completing the course, the student should be able to:

  • Identify the most common statistical methods used in data analytics
  • Explain the differences in characteristics between different type of data analytics methods and give examples of when they should be applied
  • Understand, explain and apply relevant concepts and methods in data analytics to solve practical problems
  • Use selected statistical methods for prediction, classification and decision making
  • Evaluate and choose among different methods for a specific problem instance
  • Use existing data sets to train and evaluate selected methods for real-world applications
  • Implement methods and algorithms for data analytics in a programming language

We will mainly use the statistical software Matlab to build models and work with data.

Course content

The course aims to provide knowledge in data analytics, especially for applications related to smart cities. The course will cover both supervised and unsupervised learning. The focus will be on classification and prediction, but also include clustering, anomaly detection and dimensionality reduction. Example content includes statistical inference, correlation, linear regression, logistic regression, K-nearest neighbour, support vector machines, hidden Markov models, neural networks, k-means clustering and principal component analysis.

Teaching and working methods

Lectures, tutorials and labs.

Examination

KTR1Written Test2 creditsU, 3, 4, 5
UPG1Assignments2 creditsU, 3, 4, 5
LAB1Laboratory Work2 creditsU, G

The final grade is weighted by the distribution of credits of the partial examinations.

Grades

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

Other information

About teaching and examination language

The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows: 

  • If teaching language is Swedish, the course as a whole or in large parts, is taught in Swedish. Please note that although teaching language is Swedish, parts of the course could be given in English. Examination language is Swedish. 
  • If teaching language is Swedish/English, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English (depending on teaching language). 
  • If teaching language is English, the course as a whole is taught in English. Examination language is English. 

Other

The course is conducted in a manner where both men's and women's experience and knowledge are made visible and developed. 

The planning and implementation of a course should correspond to the course syllabus. The course evaluation should therefore be conducted with the course syllabus as a starting point.  

Department

Institutionen för teknik och naturvetenskap

Director of Studies or equivalent

Erik Bergfeldt

Examiner

Nikolaos Pappas

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Course literature

Other

    • Piegorsch, W. W. (2015). Statistical data analytics: foundations for data mining, informatics, and knowledge discovery. John Wiley & Sons.
    • Christopher M. Bishop (2006). Pattern Recognition and Machine Learning, Springer.
    • Hastie, Tibshirani and Friedman (2013). An Introduction to Statistical Learning, Springer.

    Additional Material:

    • Gallager, R. G. (2013). Stochastic processes: theory for applications. Cambridge University Press.
Code Name Scope Grading scale
KTR1 Written Test 2 credits U, 3, 4, 5
UPG1 Assignments 2 credits U, 3, 4, 5
LAB1 Laboratory Work 2 credits U, G

The final grade is weighted by the distribution of credits of the partial examinations.

Other

  • Piegorsch, W. W. (2015). Statistical data analytics: foundations for data mining, informatics, and knowledge discovery. John Wiley & Sons.
  • Christopher M. Bishop (2006). Pattern Recognition and Machine Learning, Springer.
  • Hastie, Tibshirani and Friedman (2013). An Introduction to Statistical Learning, Springer.

Additional Material:

  • Gallager, R. G. (2013). Stochastic processes: theory for applications. Cambridge University Press.

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)
X
X
KTR1
UPG1
Calculus, Probability
1.2 Fundamental engineering knowledge (G1X level)
X
X
KTR1
LAB1
UPG1
Modeling, Programming
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)
X
X
KTR1
LAB1
UPG1
Statistical Inference, Prediction
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)
X
X

                            
1.5 Insight into current research and development work
X
Examples from current research activities
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving
X
X
KTR1
LAB1
UPG1
Labs and problem solving
2.2 Experimentation, investigation, and knowledge discovery
X
X
LAB1
UPG1

                            
2.3 System thinking
X
X
KTR1
LAB1
UPG1
Design Tradeoffs
2.4 Attitudes, thought, and learning

                            
2.5 Ethics, equity, and other responsibilities

                            
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork
X
LAB1
Labs in pairs
3.2 Communications
X
LAB1
UPG1
Written reports
3.3 Communication in foreign languages
X
English is the course language
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
X
UPG1
Requirements for different applications
4.4 Designing
X
X
LAB1
UPG1
Implementation of algorithms
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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There are no files available for this course.