Data Science med Python - København

Learn to prepare and leverage data using Python.
Each of the below concept is illustrated with a series of practical examples to ensure it can be applied in an industrial context.
• read and write data from diverse data sources
• manipulate data
• visualise data
• apply supervised learning
This 3-day course will provide you with the knowledge required to use Python to process and visualize data efficiently, to understand the fundamental concepts of data science and to build simple predictive models with Python.

After this course, you will be able to:
• understand the foundations of the Python language (data types, data structures and functions),
• read and write data from a wide variety of data sources,
• develop clean and efficient programs in Python,
• visualize your data,
understand what data science canachieve in your organization,
• understand the various types of
predictive models,
• understand the central concepts of
overfitting and underfitting,
• preprocess the data and build simple
models using Python,
• structure your projects properly,
• use the packages that are most helpful
in your daily work as a Data Engineer
or Data Scientist.

This is an introductory course with no pre-
requisite. Having experience with Python or another programming language and good foundations in mathematics is a plus.
After this course, you will be able to:

• write Python code to read, process, write and visualize data.
• analyse which statistical models can be applied to leverage data and how to achieve it.
• read and write data with Pandas
• visualize data with Matplotlib

Keep an edge in your digitization journey: ensure solid Python competencies for data science, so you can leverage your data, using the most recent statistical packages.
Part 1: Introduction to Python:
- Data types and data structures
- Conditions and loops
- Functions
- Python environment

Part 2: Read, Write and transform data with Pandas
Part 3: Visualize data with Matplotlib
Part 4: Learning from data using SciKit learn
- Regression and classification
- Overfitting and underfitting
- Model selection
3 full days
No preparation is required.