Data Systems and Artificial Intelligence
Data has become not only an integral part of most digital technologies and products, it has also evolved into an important production factor for business. With data becoming more and more relevant businesses increasingly rely on technologies such as databases, ETL processes, machine learning models, big data analysis, simulations, visualizations or automated reporting systems.
Middle-sized or large companies often use standardized software for their data management such as Oracle, SAP or Kafka, which usually comes with the problem that these systems only provide solutions for relatively straight forward data management processes. What I offer are high quality data systems that are specifically designed for individual needs and business requirements, which can be more complex and sophisticated.
For my past projects I built a variety data systems and technologies, which can be integrated and used through different types of applications.
Databases and APIs
Databases are an important technology that serve as the foundation of digital applications and business processes. Databases store information in a structured manner and usually consist of rows and columns. In contrast to Excel spread sheets databases allow the scaling of accessing, managing, editing and organizing large amounts of data. In most cases I implement relational databases using SQL and build a data management system on top of that, but I also gained experience with cloud databases or NoSQL databases.
Data crawling
Data crawling is the process of extracting data from a specific data source, most commonly from websites, but also from other sources such as PDFs. Data crawling can be useful in a variety of situations. For example, it allows you to monitor prices or products from competitors, to extract information from news sources or social media, to extract contact details from websites, to generate quantitative datasets for statistical analysis or track other data.
ETL-processes
ETL stands for Extract, Transform and Load and describes a system that integrates data from different data sources, processes and organizes the data and stores the data in a central database such as a data warehouse. Data integrations often are a precondition for business analytics, automated reporting, machine learning pipelines or other automated business processes.
Machine learning and AI
Machine Learning (ML) is a new computing paradigm, as Peter Norvig argued, in which the structure of the software is not designed by the developer, but rather by extracting patterns from empirical data. ML-models can be used for different kinds of applications. My past projects included classification algorithms for a business data company, a recommendation system for a streaming service or a WhatsApp chatbot for a headhunting agency.
Big data analytics
Today data analytics is an important part of measuring and monitoring business performance and other trends and developments. For my clients I frequently build software that helps them analyze their data. This can include statistical algorithms, ranking algorithms, clustering algorithms, data mining, causal analysis, visualization or automated reporting.
Data systems are one of my favourite type of project because they provide an opportunity to utilize my skills and experience that I gained from my past background as data scientist and statistician. Past projects included algorithms for the banking and insurance industry, ETL processes for marketing or manufacturing businesses, crawling bots, recommendation systems, classification systems, chatbots, MS Azure applications, API integrations, dashboards or the automation of data reporting processes.