Forecasting probabilities and recognising correlations with Data Science
We create competitive advantages for our clients by extracting knowledge from data
Our tasks include the processing, analysis and visualisation of your data. As a result, new insights and correlations can be gained. In addition, we develop predictions about important company key figures with the help of machine learning algorithms.
Our services in the field of Data Science:
Data Science aims to make predictions of activities, behaviour and trends using new and historical data . It involves the application of Big Data, statistical analysis techniques, analytical queries and automated machine learning algorithms to build predictive models.
Data management
Ensuring data quality, data availability and data transfer processes
Big Data Analytics and Data Engineering
Procurement, preparation and analysis of new and existing data
Model development
Development of mathematical models with statistical methods and machine learning
Model provision
Results are integrated into the decision-making process
Model monitoring
Monitoring performance to improve models at intervals
Reporting
Documentation and visualisation of the results
Data science comprises various technologies with which data is cleaned, analysed and new information is obtained
Application of Data Science
- Information retrieval:
The analysis of data from heterogeneous sources becomes possible with the support of Big Data techniques. Summarising the information provides new insights and supports strategic and operational decisions.
- Discovery of similarity structures:
Cluster analysis is used to identify groups that have similar structures from large data sets. No prior knowledge of the data is necessary, customers or products can simply be segmented.
- Forecasting corporate key figures:
The prediction of key figures through the application of forecasting models provides an insight into future developments. Thus, corporate decisions can be improved, and competitive advantages can be created.
- Detection of dependencies:
Exploratory data analysis uses statistical methods so that dependencies and correlations in the data are recognised and visualised. With the recognition of these correlations, the data can be better assessed and evaluated.
- Automated data processes:
The use of data pipelines facilitates the work with data by automating the collection, preparation and processing. The quality of the data has an enormous influence on the handling and use in the area of "Business Intelligence" and "Predictive Analytics".