Semaines de l’évaluation de l’enseignement
CIV8760E - Transport Data Management
Section outline
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CIV8760E - Transport Data Management
Course Leader
Pr. Nicolas Saunier
Office: B324.2
Email: nicolas.saunier@polymtl.caCourse Teacher
François Bélisle
Email: belisle.francois@gmail.comLab Assistant
Guillaume Néven
Email:guillaume.neven@polymtl.ca -
The first week deals with the course introduction ; if we have time, we'll dive into data types, presented in the first chapter of the lecture notes.
At the end of the week, you must be able to:
- know te contexte of the course: where do transport data come from?
- distinguish the types of variables
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The second week deals with data collection methods, presented in the second chapter of the lecture notes.
At the end of the week, you must be able to:
- describe and choose the collection methods for transport data according to the context and the objectives
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The 3rd week deals with data processing, as presented in chapiter 3 of the lecture notes
At the end of the week, you will be able to:
- use the principle data structures
- design a simple algorithm to process data
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Weeks 4 and 5 focus on databases, presented in Chapter 4 of the course notes.
By the end of these two weeks, you will be able to :
- design an Entity / Association model to represent a system
- design a relational model
- use the SQL language to make queries on a relational database: database creation, display of information (filter and aggregation)
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As discussed in class, the midterm will take place this week in the same class as usual.
Here is an exercice to practice the SQL material covered in the notes
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Week 8 focuses on spatial data, presented in Chapter 5 of the course notes.At the end of the week, you should be able to:
- Know the functions of a geographic information system (GIS)
- Distinguish between different spatial data formats
- Know how to identify coordinate systems and project them
- Use simple SQL queries with spatial data and functions
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Week 9 focuses on statistical analysis, presented in Chapter 6 of the course notes.
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At the end of the week, you should be able to:
- Apply simple methods of data description
- Interpret and apply some distributions used in transport
- Calculate confidence intervals, sample sizes and perform hypothesis testing
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Week 11 focuses on statistical models, presented in Chapter 7 of the course notes.At the end of the week, you should be able to:
- choose the appropriate statistical model ;
- estimate the model and select the attributes ;
- interpret the results of estimating a model.
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Week 12 is about data visualization, presented in Chapter 8 of the course notes.
At the end of the week, you should be able to:- know the main types of graphs to represent data
- apply good data visualization practices
- develop critical thinking
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Weeks 13 and 14 are about data mining, presented in Chapter 9 of the course notes.
At the end of the week, you should be able to:- distinguish the main categories of learning methods and their field of application
- understand simple unsupervised (segmentation) and supervised (classification and regression) learning algorithms
- know how to apply these methods and interpret the results
- measure method performance and understand the risks of over-fitting
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Tables for distributions (Chi2, normal, Student)
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