Course Template
Data Analytics for Business – Company Key Figures with Python
Analysis and Visualization of Company Key Figures with Python.
Didactic Objective
This course template introduces structured analysis of business data with Python, combining the subject-specific foundations of data analysis with practical programming. The focus is on processing tabular company data, calculating key figures, and interpreting them through suitable visualizations. Step by step, methods for evaluating, comparing, and forecasting company key figures are built up. This supports the development of competence in the field of data-based analysis and decision support.
Competence Focus Areas
- Importing and structured processing of company data from CSV files with pandas
- Calculating statistical key figures such as mean, standard deviation, and correlation
- Visualizing business data with matplotlib (line, bar, and scatter plots)
- Analysis of cost structures and key figures of individual company departments
- Comparison of several companies based on relative key figures such as profit margin and cost ratio
- Processing and storage of company data via REST APIs and JSON structures
- Development of simple forecast models for sales developments using linear regression
Structure of the modules
Analysis of company key figures from CSV
Introduction to reading, evaluating, and visualizing monthly company key figures from CSV files using pandas and matplotlib.
Analyze the cost structure of a company
Statistical analysis of department costs and visualization of the company's cost structure using pandas, numpy, and matplotlib.
Store company data in User-DB
Building a simple data pipeline from CSV to JSON to storing company data in a database via a REST API.
Compare companies from the database
Analysis and comparison of multiple companies by retrieving stored data sets from a database and calculating central key figures.
Visualization of company key figures
Representation of business economic key figures with various diagram types for analysis, comparison, and relationships.
Analyze cost-revenue relationship
Investigation of statistical relationships between marketing costs and revenue through correlation analysis and visualization with scatter plots.
Revenue forecast using linear regression
Development of a simple predictive model to predict future revenues based on historical data using linear regression.
The clearly defined modules enable a step-by-step introduction to data analysis, visualization, and forecasting procedures and can be flexibly integrated into existing lesson series.
Test the template in the demo access and adapt individual analysis or visualization tasks to your group and teaching goals.