Michele Volpi: Catalogue data in Autumn Semester 2022

Name Dr. Michele Volpi
Address
Swiss Data Science Center (SDSC)
ETH Zürich, OAT W 29
Andreasstrasse 5
8092 Zürich
SWITZERLAND
E-mailmichele.volpi@sdsc.ethz.ch
DepartmentEnvironmental Systems Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
701-3001-00LEnvironmental Systems Data Science: Data Processing Restricted registration - show details
**Students who have taken 701-3001-00L Environmental Systems Data Science in autumn semester 2020 or 21 are not allowed to take this lecture. The content is similar.**

Number of participants is limited to 80.

Course registration starts on 31.08.2022.
Priority is given to the target groups until 23.09.2022,

Target groups
Agricultural Sciences MSc
Environmental Sciences MSc
Atmospheric and Climate Science MSc
Environmental Sciences PhD
Agricultural Sciences PhD

Waiting list will be deleted on 30.09.2022
2 credits2GL. Pellissier, E. J. Harris, J. Payne, M. Volpi
AbstractStudents are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses.
Learning objectiveThe students are able to
● frame a data science problem and build a hypothesis
● describe the steps of a typical data science project workflow
● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models
● critically think about the limits and implications of a method
● visualise data and results throughout the workflow
● access online resources to keep up with the latest data science methodology and deepen their understanding
Content● The data science workflow
● Access and handle (large) datasets
● Prepare and clean data
● Analysis: data exploratory steps
● Analysis: machine learning and computational methods
● Evaluate results and analyse uncertainty
● Visualisation and communication
Prerequisites / Notice252-0840-02L Anwendungsnahes Programmieren mit Python
401-0624-00L Mathematik IV: Statistik
401-6215-00L Using R for Data Analysis and Graphics (Part I)
401-6217-00L Using R for Data Analysis and Graphics (Part II)
701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften
701-3003-00LEnvironmental Systems Data Science: Machine Learning Restricted registration - show details
Number of participants is limited to 80.

Course registration starts on 31.08.2022.
Priority is given to the target groups until 23.09.2022,

Target groups
Agricultural Sciences MSc
Environmental Sciences MSc
Atmospheric and Climate Science MSc
Environmental Sciences PhD
Agricultural Sciences PhD

Waiting list will be deleted on 30.09.2022
3 credits2GL. Pellissier, E. J. Harris, J. Payne, M. Volpi
AbstractStudents are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode
Learning objectiveThe students are able to
• select an appropriate model related to a research question and dataset
• describe the steps from data preparation to running and evaluating models
• prepare data for running machine learning with dependent and independent variable
• build and validate regressions and neural network models
• understand convolution and deep learning models
• access online resources to keep up with the latest data science methodology and deepen their understanding
Content• The data science workflow
• Data preparation for running and validating machine learning models
• Get to know machine learning approaches including regression, random forest and neural network
• Model complexity and hyperparameters
• Model parameterization and loss
• Model evaluations and uncertainty
• Deep learning with convolutions
LiteratureBuilding on existing data science resources
Prerequisites / NoticeMath IV, VI (Statistics); R, Python; ESDS I