Data Analysis with Python («PYTH04»)

• Data preparation
• Data transformation
• Quality analyses
• Analysis
• Data analysis applications

1 Basics of Numpy

• What can this package do?
• What data analyses are possible with it?

2 Introduction to Pandas

• Pandas data structures
• Functionality
• Which data analyses are possible and useful with Pandas

3 Loading and saving data

• Practical handling of the most important file formats
• Excel integration with Pandas
• SQL queries with Pandas
• API queries with Pandas
• Python connection to common cloud solutions (Google, Microsoft, AWS)

4 Preparing and cleansing data

• Dealing with missing data
• Data transformation
• Linking, combining and transforming data
• Aggregating and grouping data
• Dealing with timestamps and calendar data
• Efficiency, performance and reliability

5 Visualisation

• Basics of visualisation with Matplotlib
• Publication-ready graphics with Seaborn
• Interactive web visualisations with Plotly

6 Time series analysis

• Introduction to the methodology
• The Statsmodels package
• Automated time series modelling

7 Learning models

• Introduction to the methodology
• Overview of learning models with scikit
• Preparing data for machine learning
• Classification and regression of data
• Interpreting and presenting results

8 Introduction to geodata analysis

• Introduction to the methodology
• Geopandas and Shapely

For each point: examples of Python-based data analysis on real cases

• Workshop involving exercises and practical applications
• Participants will complete the exercises on their own laptops
• Before the course begins, participants will receive detailed information on how to prepare and install the relevant open-source Python software

This course is aimed at data analysts, business analysts, data engineers, BI specialists and database specialists.

Basic knowledge of data analysis and advanced Python knowledge according to the following course: