Library

Collect, unify and route customer data across sources. In practice this covers independent execution, quality assurance of one's own results, and close alignment with adjacent roles and interfaces.

Collect and analyse feedback and NPS data and derive improvements.

Elicit and document customer needs through structured questioning. Includes thorough preparation, structured execution and follow-up of conversations, and consistent documentation of outcomes and next steps in the CRM.

Onboard new customers and shorten time to value. Includes thorough preparation, structured execution and follow-up of conversations, and consistent documentation of outcomes and next steps in the CRM.

Leverage customer data and manage retention programmes. Covers confident day-to-day use: data maintenance and quality, configuration of processes and workflows, analyses and reports, and onboarding of new users.

Resolve requests kindly and de-escalate escalations. Covers forward-looking planning and prioritisation, clear alignment with all stakeholders, consistent follow-up on results and continuous improvement of processes.

Handle customs procedures and comply with export controls. This includes assessing specific cases, applying current requirements and case law, legally sound documentation, and advising business units in plain language.

File imports and exports electronically via ATLAS. Covers confident day-to-day use: data maintenance and quality, configuration of processes and workflows, analyses and reports, and onboarding of new users. Applies within the German regulatory and administrative context.

Handle bookkeeping and closings with DATEV software. Covers confident day-to-day use: data maintenance and quality, configuration of processes and workflows, analyses and reports, and onboarding of new users.

Declare and transport dangerous goods in compliance. In practice this covers independent execution, quality assurance of one's own results, and close alignment with adjacent roles and interfaces.

Create, check and keep master data current in systems. Performed according to professional and hygiene standards – including patient observation, complete documentation and structured handovers within the interprofessional team.

Data Analysis is the process of examining data using statistical and logical reasoning to draw conclusions and find insights. It involves the collection, cleaning, organization, and analysis of data, along with the interpretation and communication of findings. It can be used to identify patterns, trends, and correlations in order to better understand a particular set of information. Data Analysis can also be used to develop models and algorithms for predictive analytics.

Data Analysis is the process of examining, cleaning, transforming, and modeling data to identify useful information, suggest conclusions, and support decision-making. It involves a variety of techniques, from statistical and mathematical modeling to machine learning algorithms. Data Analysis is an important skill for many disciplines, including business analytics, market research, and data science. It requires knowledge of the data, an understanding of the business context, and an ability to interpret and communicate the results.

Data Analysis is a set of skills and techniques used to extract valuable insights from data. This includes the ability to clean and organize data, analyze data using various techniques, such as descriptive, predictive, and prescriptive analytics, interpret results, and communicate findings. Data Analysis is a valuable skill for businesses to have in order to make informed decisions and optimize performance.

Data Analysis is the process of cleaning, organizing, and analyzing data in order to discover patterns and insights, drawing conclusions, and making data-driven decisions. It is a critical component of the data science process and is used to make informed decisions about products, services, and processes. Data Analysis involves collecting, organizing, and summarizing data, as well as using advanced analytics tools to interpret the data. It can be used for a variety of purposes, such as identifying trends, forecasting future behavior, and testing hypotheses.

Clean, merge and prepare raw data for analysis. Covers preparing and quality-assuring the data, methodologically sound analysis, visualising results and presenting them audience-appropriately as a basis for decisions.

Build and operate data pipelines and platforms. In practice this covers independent execution, quality assurance of one's own results, and close alignment with adjacent roles and interfaces.

Data Migration is a process of moving data from one platform, system, or application to another. It involves transferring data from a legacy system to a new system, or from one version of a system to a newer version. Data migration is often used to upgrade a system or to make a system more efficient. It is also used to move data from one location to another, such as from on-premise systems to cloud-based systems. Data migration is a complex process that requires planning, preparation, and execution to ensure that data is migrated properly and accurately.

Data Mining is a process of extracting patterns from large amounts of data. It involves the use of various techniques such as statistical analysis, machine learning, and artificial intelligence to uncover hidden insights and knowledge from structured and unstructured data sources. Data Mining is used in a variety of fields such as marketing, healthcare, finance, and science. It can help organizations to make better decisions, uncover trends, and identify new opportunities.

Design business and technical data models. Covers preparing and quality-assuring the data, methodologically sound analysis, visualising results and presenting them audience-appropriately as a basis for decisions.

Know and apply data privacy rules in daily work. This includes assessing specific cases, applying current requirements and case law, legally sound documentation, and advising business units in plain language.

Interpret data protection law and guide implementation. This includes assessing specific cases, applying current requirements and case law, legally sound documentation, and advising business units in plain language.

Define data standards and ensure data quality. Covers preparing and quality-assuring the data, methodologically sound analysis, visualising results and presenting them audience-appropriately as a basis for decisions.

Data Structures is a skill that focuses on the use of data structures to store and organize data. Data Structures involves the use of algorithms, data structures, and software engineering principles to create efficient solutions to complex data problems. Data Structures are used in many different areas of computing, including databases, networking, artificial intelligence, and computer graphics. Data Structures help organize data in an efficient and effective manner, allowing for the manipulation of large amounts of data in real-time. Knowing how to effectively use Data Structures is an invaluable skill for any computer scientist or software engineer.