IT consulting
Data science - the science of gaining insights from complex data
The backbone of successful companies are products, services and data. However, without intelligent analysis, this data has no value – like an unused raw material with an expiry date. The intelligent analysis of data is the core of any digitalization strategy.Using data science, patterns can be comprehensibly mapped in rules and continuously developed using learning processes – for example with the following objectives:
- Increasing sales or reducing costs
- Develop a better understanding of the customer
- Optimize internal processes or products
- Improve planning and forecasts
- Develop or refine business models
Data Science
Data science involves the acquisition, analysis and use of data to solve complex problems. This includes the following core activities:
- Collecting and extracting data from various sources (e.g. databases, APIs, sensors, websites).
- Processing unstructured data (such as text, images, videos).
- Dealing with incomplete, incorrect or contradictory data.
- Cleansing and transformation of the data into a format that is suitable for analysis.
- Application of statistical techniques to identify relationships and trends in the data.
- Visualization of data to present results in an easily understandable way.
Modeling & AI
- Use of AI algorithms to make predictions or automate decisions.
- Optimization and validation of models to improve their accuracy and reliability.
- Presentation of the results through reports, dashboards or presentations.
- Translating complex analyses into understandable and actionable recommendations for decision-makers.
- Implementation of models in production systems so that they can be used continuously.
- Automation of processes such as forecasting or anomaly detection.
Procedure model
- Conception, definition of business and project goals
- Cost estimation and project planning
- Description and exploration of the data
- Data cleansing, data selection and anonymization / pseudonymization if necessary
- Selection of suitable methods and tools
- Modelling and parameterization
- Evaluation of the models
- Report preparation and presentation of the results
- Application and training of the model in the operational process
- Consolidation and migration of your data science and IT systems
- Integration of data warehouse, big data and third-party systems
- Integration of forecasts in planning solutions, operational and strategic processes
Methods
- Selection of the relevant attributes
- Development of data models
- Visual Analytics
- Authorization concepts, data protection-compliant analyses
- Performance tuning of the algorithms
- IT service management
- System architecture
- Project management
- Python, Angular, Vue.js, C/C++, and much more.
- Database systems, such as Oracle, DB2, SQL Server and PostgreSQL
- Linux, Azure, Google and AWS Cloud, Docker
- Data lake architectures, such as MinIO and Apache Iceberg
- Reporting and dasbhoarding solutions, such as Apache Superset
- Design and development of reports.
- Development of ETL / ELT processes
- Administration, performance tuning and operation of your data science systems
- Automation of administrative tasks
ML and AI procedures
The methods and processes we use in the field of machine learning and artificial intelligence include, for example
Discover your potential
Our senior consultants in the field of data science will be happy to support you in making your company and your processes more efficient – effectively, efficiently and future-oriented.
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