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Data Science & Machine Learning
Last Updated: 2026-06-03 00:07:32
Abstract
This course provides a fundamental training in the areas of data science and machine learning. It is intended for managers and leaders who want to understand the typical workflow, fundamental techniques and key challenges of data science and machine learning to drive successful implementations.
Objective
After taking this course the participants - have a good understanding of the basic methods of data science and machine learning - know the typical data science workflow and can understand and assess the role and importance of each individual step - understand the importance of quantifying and communicating uncertainty in the data - know the importance and basic techniques of cleaning and organizing data and can perform simple data cleaning tasks in pandas. - can identify suitable algorithms and select the best-suited one for a given task - can apply machine learning methods as implemented in scikit-learn on tabular data - understand the basic ideas behind modern deep learning methods and can implement simple deep learning models in tensorflow - understand some key applications such as natural language processing or computer vision. - are able to apply the learned methods to practical problems in data science.
Content
We will cover the following topics - The typical data science workflow - Cleaning, organizing and preparing data for further analysis - Exploratory data analysis: Gaining an understanding through visualizing and summarizing data - Basics of statistical inference and uncertainty quantification - Correlations and regression. - Basics of Machine learning, including supervised and unsupervised learning, model evaluation and model selection - Standard algorithms such as linear regression, decision trees, k-nearest neighbors, k-means, principal component analysis - Identification of the best-suited algorithm and models for a given dataset and machine learning task - Foundations of Deep Learning - Challenges & Considerations: Potential pitfalls, threats, and ethical considerations. The theoretical parts will be complemented by practical exercises using python, pandas, numpy, matplotlib, scikit-learn, and tensorflow.
General Information
- Language
- English
- Levels
- WBZ , NDS
- Frequency
- Semesterly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Data Science & Machine Learning | No time listed | 36 h semesterly |