(BIP) Applied Data Modelling: A Case Study in Atmospheric Pollution - ICB018AS2AE - Opinto-opas

(BIP) Applied Data Modelling: A Case Study in Atmospheric Pollution, 3 cr - ICB018AS2AE

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Current implementations

(BIP) Applied Data Modelling: A Case Study in Atmospheric Pollution ICB018AS2AE-3001 03.02.2025-25.05.2025 3 op (ITE5PAICB1, ...) +-
Teaching methods and instruction

• Blended Intensive programme
• In co-operation with FH Joaneum
• Virtual part: (3.3.-12.5.2025)
• Intensive part at Pasila Campus 19.-25.5.2025

Information regarding the virtual part:

In this phase, students will use hands-on lecture materials to learn the fundamentals of applied data modelling. The materials will be available as Jupyter Notebooks, which provide an interactive way for students to engage with the included topic. Each notebook comes with executable code, additional information as well as small tasks where students can immediately apply their newfound knowledge. Each notebook is also available with a solution, so students may compare their own results with the suggestions provided by the lecturer. The topics that will be covered are:
• Python Warmup
• Pandas Warmup
• Data Visualization in Python
• Regression Analysis in Python
• Tree-Based Methods in Python
• Explainability & Diagnostic Methods
To help students stay on track in their learning, there will be some online meetings, where the lecturer and the participants will meet and discuss setup questions as well as questions arising from the virtual exercises. The meetings are:
• Kick-Off: 05.03.2025 at 18:00 - 20:00
• Tutorial 1: 26.03.2025 at 18:00 - 20:00
• Tutorial 2: 23.04.2025 at 18:00 - 20:00
The virtual part culminates in a small task that will ensure students know all the required skills for the in-person part. This task will have no provided solution by the lecturer. Students are required to hand in this task until the given deadline and will be given feedback by the lecturer prior to the in-person phase. The task deadline is: 12.05.2025

Information regarding the intensive part held at Pasila Campus:

In this phase, students meet in Haaga-Helia for the in-person part of this intensive programme. Here, students will split into groups of 2-3 people and be provided with a new dataset and a list of tasks. Students will be required to complete these tasks over the course of the week. Students will be able to apply their acquired skills to a real-life dataset and associated research questions in teams. At the end of the week students will summarise their findings, confer with their colleagues and present their results to an audience. The course will conclude with this presentation.

Learning material and recommended literature

Bibliography
-Hörmann, S., Jammoul, F., Kuenzer, T., & Stadlober, E. (2021, February). Separating the impact of gradual lockdown measures on air pollutants from seasonal variability. Atmospheric Pollution Research, 12, doi: 10.1016/j.apr.2020.10.011.
-James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.

Campus

Pasila Campus

Teaching language

English

Internationality

In co-operation with FH Joaneum

Timing

03.02.2025 - 25.05.2025

Enrollment

04.02.2025 - 14.02.2025

Content scheduling

Virtual part 3.3.-12.5.2025
Intensive part at Pasila Campus 19.-25.5.2025
Groups
- ITE5PAICB1
- CONTACT
- BLENDED
Teachers

Juhani Heikkinen, Jukka Remes

Seats

5 - 15

Degree Programme

ITBBA Business Information Technology

R&D proportion

0.00 cr

Virtual proportion

1.50 cr

Evaluation scale

H-5

Past implementations

No past implementations yet.

After this course, students have acquired the following skills:
-Students can link theoretical modelling skills to applied data problems in python.
-Students have honed their investigative skills to identify pitfalls of real-life modelling.
-Students can translate applied research questions to specific modelling tasks, can extract answers from given data and evaluate the quality of these answers.
-Students can visualize and present their findings in an appropriate manner to stakeholders.
-Students are well-prepared to handle a data analytics task in the future, e.g., for their thesis.

Concept

This Problem-Based Learning course teaches the fundamentals of applied data analytics and modelling in python. In preparation to the intensive course, students will learn how to use python to handle and visualize data and implement models in asynchronious lessons. Students will review how classic modelling methods such as regression analysis, decision trees, and random forests are used in practice using python. The lecturer provides a comprehensive overview of these topics, as well as selected subjects from exploratory data analytics and diagnostic methods, focussing on maintaining explainable and parsimonious models. This foundational knowledge prepares students for the hands-on portion of the course.

During the intensive course, students are divided into small and diverse groups, emphasizing collaboration between universities. With guidance and support from the lecturer, these groups will apply the discussed methods to a provided dataset. The central research question they aim to address focuses on investigating how human activities influence the intensity and spread of various atmospheric pollutants at different locations within a city. By collaborating, students will utilize their analytical skills to explore the data, identify patterns, and interpret their findings. Finally, students will present their results to each other and discuss the different approaches chosen within each group to identify differences between the provided locations.

The class emphasizes collaborative learning and practical application. Each group will work through the data analysis process, from initial exploration to final interpretation. Students will review their collective results, compare methodologies, and discuss their conclusions. This approach not only reinforces their understanding of data analytics and modelling techniques but also enhances their problem-solving abilities and teamwork skills. The ultimate goal is for students to formulate well-supported answers to the research question, demonstrating their ability to apply theoretical concepts to real-world problems.

Teaching Plan

1) Asynchronious Part: Students are provided with a select number of jupyter-notebooks to work through in their own time, at the latest to be finished prior to the in-person part. These lectures include:

-A Python Warmup
-A Pandas Warmup
-Data Visualization in Python (seaborn)
-Regression Analysis in python (statsmodels)
-Tree-Based Methods in Python (sklearn)
-Explainability Methods (learning to use shap values)

2) In Person Part: Students are provided with the atmospheric_pollution dataset. In the intensive program, they will go through the following steps:

-Build a Team of 2-3 people of different universities.
-Review their provided data with respect to quality (missings, outliers, data understanding)
-Identify relationships between potential features and the responses pm10 and no2. Think of the nature of these relationships and let them influence the modelling.
-Model the response for 2015 - 2019 with respect to the previously identified features. Evaluate and diagnose these models and attempt to improve. This is a cyclic step. Investigate which features influence the response and how.
-Provide prediction for 2020 and see whether a lockdown effect can be identified (Start: March 16th) for either one of the responses.
-Summarize findings, present, and discuss with colleagues to learn from their chosen paths and pitfalls.

??Bibliography
-?Hörmann, S., Jammoul, F., Kuenzer, T., & Stadlober, E. (2021, February). Separating the impact of gradual lockdown measures on air pollutants from seasonal variability. Atmospheric Pollution Research, 12, doi: 10.1016/j.apr.2020.10.011.
-?James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.

Prerequisites:
-Fundamentals in Statistics, Data Analytics or Machine Learning
-Fundamental Programmingskills in python (pandas, matplotlib, seaborn) helpful, but not necessary