Learn how to solve many commonly faced problems when applying Python for Data Science and become an even more important part of your company. This course will introduce you to the most important Data Science tools in Python to effectively solve many real life problems.

Participant profile

You have experience with programming from your daily work with data or from your education and have a basic Mathematical understanding. Basic experience with programming in Python will be helpful.

Content

1. Introduction Introduction to the Python programming language.
2. Jupyter Notebooks Interactive reporting of results to colleagues and non-technicals with Jupyter Notebooks. New knowledge about data is not worth much if you aren’t able to communicate the findings with your colleagues. Jupyter Notebooks is a strong tool for reporting.
3. Data Import Importing data from flat files and databases. Data comes from a variety of sources, it is therefore important to know how to get data into Python.
4. Data Manipulation Manipulating data in Python with a focus on the pandas package. The pandas package contains a user friendly API for data manipulation, this will help you solve the most common data manipulation challenges.
5. Data Visualization Plotting data in Python with a focus on the matplotlib package. The matplotlib package contains a user friendly API for plotting in Python. To be able to plot is essential when new knowledge about data is to be shared with colleagues.
6. Supervised Learning Program a prediction model in Python, also called supervised learning. Learn how to implement models in Python through exercises, with a focus on the most popular Machine Learning models.
7. Unsupervised Learning Clustering Basics in Python, also called unsupervised learning. Learn how to group similar observations together.