Data Science Events Calendar

Welcome to your calendar for data science events! Dive into a curated list of courses, conferences, seminars, workshops, and key deadlines. Tailor your search to match your interests by adjusting the event category filters. For those specifically looking for PhD courses, simply modify the filter settings to include these events as well. Stay connected and up-to-date with the latest in data science, all in one place.


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International School of Chemometrics 2024 – BASIC – Basic Introduction to Chemometrics, Data Types, Data Pre-Processing, PCA, Multivariate Linear Regression and Linear Algebra for Multivariate Data Analysis

May 20 - May 24

Aim and content

The “International School of Chemometrics (ISC) – 2024” is a four-week PhD school specifically aimed at people having acquired some basic understanding of chemometrics. ISC will be offered at practitioner level.

“ISC-2024” is addressed to PhD students/post-docs, associate professors, etc. who want to acquire further knowledge on advanced multivariate data analysis from different disciplines (Chemistry, Physics, Food Science, Biology, Geology, Environmental Sciences, etc.). ISC also addresses companies or research laboratories who want to implement advanced multivariate data analysis in their daily research environment.

ISC aims at being a platform for:

– Learning data analysis methods. ISC is specifically designed for researchers who want to acquire extra knowledge on multivariate data analysis and adapt it in their routine work.

– Sharing knowledge and interchange of ideas between students covering different scientific backgrounds. One of the key points of the course is the interaction between the students and troubleshooting – always within the framework of scientific data analysis and performance.

– Meeting world-wide recognized experts of Multivariate Data Analysis in an open discussion forum environment. ISC will count with teachers that are well-recognized experts on chemometrics and multivariate data analysis in their respective fields.

This, at the same time, will offer the possibility of opening new collaborative frameworks between students and teachers. The students enrolling will have the opportunity of choosing the seminars that they consider most relevant, without the obligation of attending a minimum amount of seminars.

Seminars and dates:
Each seminar is independent and the registration is individual. The students can choose to attend the seminars which they consider more relevant for their research. There is no minimum of seminars that the student must attend.

1 – PROGRAMMING (José Manuel Amigo, Sergey Kucheryavskyi and Anders Krogh Mortensen): This online prerecorded videos are thought to be an introduction of the main aspects dealing with Matlab, R and Python. Check the “detailed information website” for more information. 1 ECTS. Start the 13th of May, 2024.

2 – BASIC (José Manuel Amigo and Morten Rasmussen): This seminar includes 2 parts. EXPLORE and LINAL. Basic introduction to Chemometrics. Data types, PCA, pre-processing, Multivariate Regression, Linear Algebra for Multivariate Data Analysis. 5 days. 2.5 ECTS. 20 – 24 May 2024. From 9 am until 4 pm, with 1 hour lunch break.

3 – INTERMEDIATE (Agnieszka Smolinska, Asmund Rinnan, Anna de Juan): This seminar includes 3 parts. DoE (Design of Experiments), VARSEL (variable selection methods) and MCR (Multivariate Curve Resolution). 5 days. 2.5 ECTS. 27 – 31 May 2024. From 9 am until 4 pm, with 1 hour lunch break.

4 – DEEP LEARNING (Jesper Løve Hinrich): This seminar is an introduction to non-linear methods and deep learning. 3 days. 1.5 ECTS. 3 – 5 June 2024. From 9 am until 4 pm, with 1 hour lunch break.

5 – CLASS (Davide Ballabio): The course will deal with the main linear classification methods like Discriminant Analysis, Partial Least Squares Discriminant Analysis (PLS-DA) and SIMCA. We will see both theoretical aspects and practical applications. The last two days will focus on more complex models like Support Vectors Machine, Random Forests and Self-Organized Maps Artificial Neural Networks. 4 days. 2 ECTS. 3 – 6 June 2024. From 9 am until 4 pm, with 1 hour lunch break.

6 – DATA FUSION (Federico Marini): Data Fusion is gaining extreme importance in science. This seminar is an introduction to the main methods to fuse your data. 2 days. 1 ECTS. 5 – 6 June 2024. From 9 am until 4 pm, with 1 hour lunch break.

7 – MULTIWAY (Beatriz Quintanilla): Methods like PARAFAC, PARAFAC2 and N-PLS will be taught. 1 day. 0.5 ECTS. 6 June 2024. From 9 am until 4 pm, with 1 hour lunch break.

8 – GLUE (José Manuel Amigo, Rasmus Bro, Federico Marini, Davide Ballabio): How NOT to Make Chemometrics and afternoon workshop to talk about your particular needs. 1 day. NO ECTS. 7 June 2024. From 9 am until 4 pm, with 1 hour lunch break.

Extremely important note: The last week, the seminars are simultaneous. Therefore:
– Attending DL means that you cannot sign up for CLASS and DF.
– Attending CLASS means that you cannot sign up for DL and DF.
– Attending DF means that you cannot sign up for DL, CLASS and MW.
– Attending MW means that you cannot sign up for CLASS and DF.
Despite this fact, attending any of the seminars of the last week will grant you access to the material of all the seminars of that week.

Further information:
e-mail of ISC (

BASIC – Basic introduction to Chemometrics, data types, data pre-processing, PCA, Multivariate Linear Regression and Linear Algebra for Multivariate Data Analysis

This seminar contains two general topics:

– EXPLORE – Data exploration and regression: Principal Component Analysis has become the most powerful and versatile tool for exploring data tables in Analytical Sciences. Here we present a course to show the main benefits and drawbacks of PCA when it is used for different kinds of analytical data: Spectroscopy, environmental assessment, sensory, experiments performance, chromatography, etc. Moreover, preprocessing of different types of data will also be addressed in the seminar as a prerequisite for having the optimal possibility for exploring the data. If PCA is the keystone of pattern recognition methods, PLS is the keystone of multivariate calibration methods. This seminar will give a general overview of different multivariate calibration strategies (Multilinear Regression, Principal Component Regression), and will focus on Partial Least Squares regression.

– LINAL – Linear Algebra: The Foundation for chemometric modeling is Linear Algebra. Why do the algorithms work? Why are the models meaningful? A math-derived answer to these questions can be found using linear algebra. The seminar will focus on hands-on experience with some fundamental linear algebra concepts, including rank, determinant, inverse, pseudo inverse, eigenvalues, singular value decomposition, orthogonality and basis sets. We will analyze a few real-life datasets, but the purpose of the seminar is to be a proficient mechanic unraveling the black box of algorithms and models, while other courses will learn you how to drive the car.

Dates and timetable: 20th of May – 24th of May. From 9 am until 4 pm (CET) with a 1-hour lunch break (30 hours)

Previous knowledge needed: None

Software needed: Feel free to work with Matlab, Python or R, or any other software that you consider (e.g. Unscrambler). The teachers will work with:

– Matlab
– PLS_Toolbox / SOLO. IMPORTANT: For the PLS_Toolbox / SOLO, a fully functional demo will be available for the School.

Teachers: José Manuel Amigo (EXPLORE) and Morten A. Rasmussen (LINAL).

Learning outcome

The intended learning outcomes are divided into two categories:

1) Individual learning outcomes: the main target for each individual seminar is to learn the basis of one data analysis method focused on several proposed examples. The students have to be able to apply the acquired knowledge to any problem related to their own research.

2) Global learning outcomes: Students attending all the seminars will, at the end of the course, be able to understand the structure of a vast amount of data structures and also to understand the problems derived from the data. Moreover, they will be independent in the application of solutions to their problems in a dedicated manner.


The price for participating is as follows (same price for online and physically present):

Academia: DKK 600 per ECTS (approx. EUR 81 / USD 98)
Industry: DKK 1500 per ECTS (aprox. EUR 200 / USD 245)

Payment must be completed before the course starts. Information on the method of payment will be provided after confirmation of registration.

All seminars include all the material that the student might need:
– Slides of the course (pdf).
– Exercises
– Datasets
– Toolboxes
– Refreshment during the lessons (coffee, tea, candies, cookies, and other amenities)
– We do NOT provide: Matlab and lunch

Best Poster Award for physically present attendants:
In order to foment collaboration between us and interchange ideas, ISC – 2024 will include different poster sessions. Therefore, we would like to invite you to submit an abstract to the school. The abstract and poster are intended to promote the interchange of ideas between us, students and teachers. See further instruction

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May 20
May 24
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