Overordnede kursusmål
This course introduces the portfolio of data science tasks and
techniques necessary for exploring, manipulating, visualizing and
analysing data (descriptive analytics), as well as for building
prediction models using machine learning (predictive analytics)
that can be used to gain insights and support decisions
(prescriptive analytics). It is designed with Management
Engineering students in mind (i.e. some programming background, but
not the core skill), particularly – but not exclusively - those
related to studies on mobility and logistics and business
analytics. Therefore, it contains a strong hands-on component, with
specific real-world cases from mobility and business contexts.
The course also includes an introduction to Python programming,
data wrangling, problem formulation, and the basic suite of machine
learning algorithms.
Læringsmål
En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:
- Run Python scripts that load and analyse small/medium-sized
datasets
- Convert a raw dataset into an actionable form to solve a
concrete problem
- Apply basic data structures and algorithms to manipulate
data
- Relate available problems and data in a mobility context with
techniques to tackle them
- Extract and analyse insights from the application of methods
for descriptive and predictive analytics
- Visualize complex temporal and spatial patterns
- Argue for the choice of appropriate data analysis and
predictive analytics algorithms
- Appropriately train and test a statistical model to answer a
problem
- Critically evaluate the results of a data sciences analysis and
recommend actions
- Explain important data mining concepts, such as overfitting,
bias, regularisation, etc.
Kursusindhold
The classes are taught in an interactive manner, with theoretical
parts, intermingled with practice with Jupyter Notebooks.
Main topics are: Introduction to Python programming and Pandas,
data visualisation, forecasting and regression models,
classification, clustering, dimensionality reduction. The methods
will be exemplified through different cases within e.g.
transportation, business management and marketing.
Litteraturhenvisninger
Each lecture has its own recommended texts, that should be
available publicly online. For the student interested in a book to
follow, we recommend
"The elements of statistical learning", Trevor Hastie,
Robert Tibshirani and Jerome H. Friedman
"Python for Data Analysis", Wes McKinney
Sidst opdateret
22. april, 2021
