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Applied Data Science with Python

Would you like to be able to predict key figures for your business? With Data Science and Machine Learning algorithms, it is possible to create highly accurate predictive models.

Participant profile

This course is for you, who wants to learn how to use Python in your work with Machine Learning. Maybe you are working with data on a daily basis, and you would like to gain more value by using Machine Learning models.


You are expected to have a basic understanding of programming in Python or other programming languages. Concepts such as variables, loops, if-statements and functions should be familiar.


The course consists of a theoretical overview of models and relevant theory, and with a strong focus on hands-on assignments. This way you will quickly and easily be able to use your newly aquired skills in practice.

Module 1: Tools

  • Data science requires a series of libraries that you shold be familiar with. We will go through pandas, scikit-learn and more.

Module 2: Data and plotting

  • In real life a lot of time is spent on loading and cleaning data. We will go through procedures for loading data, performing imputation and one-hot encoding.

Module 3: Supervised learning models

  • Modelling is essential to make good predictions. A subset of models from scikit-learn will be presented, one of which will be Random Forest.

Module 4: Model Evaluation

  • To ensure consistent results on new data, it is important to evaluate a model with the proper techniques. We will use cross-validation to validate the model, and go into tuning of hyperparameters to avoid overfitting.


Mathias Kvist Aarup
Mathias Kvist Aarup works as a Data Scientist, where Python is used as the primary programming language. Mathias has a master degree in Mathematical Finance from Aarhus University, where he has also conducted class room teaching for multiple years. He firmly believes that Python is the best language for Data Science at the moment.

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