Effectively use a variety of data analytics techniques, such as Prescriptive and Predictive Analytics, for complex data analysis through the whole data lifecycle.

Analytical techniques

Apply designated quantitative techniques, including statistics, time series analysis, optimization and simulation, to deploy appropriate models for analysis and prediction.

Quantitative techniques

Understand and use different performance and accuracy metrics for model validation in analytics projects, hypothesis testing and information retrieval.

Performance and accuracy metrics

Visualise results of data analysis, design dashboard and use storytelling methods.

Visualisation and Presentation

Apply machine learning techniques to analyze and interpret complex data, create predictive models and solve problems across various domains. 

Machine Learning

Use appropriate data analytics and statistical techniques on available data to discover new relations and deliver insights into research problems or organizational processes and support decision-making.

Data Analytics

Design and implement data storage solutions that effectively manage the lifecycle of data, from creation to retirement.

Data storage and application

Develop and execute strategies for data collection, ensuring the acquisition of high-quality, relevant data from various sources. This includes data cleaning, transformation and preparation activities to facilitate analysis.

Data collection and preperation

Develop and apply computational and data driven solutions to domain related problems using wide range of data analytics platforms, with the special focus on Big Data technologies for large datasets and cloud based data analytics platforms

Big Data

Develop and prototype specialised data analysis applications, tools and supporting infrastructures for data driven scientific, business or organisational workflow.

Data analysis applications, tools and supporting infrastructures

Develop, deploy and operate large scale data storage and processing solutions using different distributed and cloud based platforms for storing data (Data Lakes, Hadoop, HBase, Cassandra, MongoDB, Accumulo, DynamoDB, etc.).

Large scale data storage and processing

Consistently apply data security mechanisms and controls at each stage of the data processing. The focus of this competence is the technical implementation of security mechanisms.

Data security mechanisms

Design, build, operate relational and nonrelational databases (SQL and NoSQL), integrate them with the modern Data Warehouse solutions and ensure effective ETL (Extract, Transform, Load), OLTP, OLAP processes for large datasets.

Database management

Use engineering principles and modern computer technologies to research, design, implement new data analytics applications; develop experiments, processes, instruments, systems, infrastructures to support data handling during the whole data lifecycle.

Data Science Engineering

Develop and implement data strategy, in particular, in the form of data management policy and Data Management Plan (DMP).

Data strategy and management

Develop and implement relevant data models, define metadata using common standards and practices, for different data sources in variety of scientific and industry domains.

Data models

Integrate heterogeneous data from multiple sources and provide them for further analysis and use.

Data sources and integration

Maintain historical information on data handling, including reference to published data and corresponding data sources (data provenance).

Data handling

Ensure data quality, accessibility, interoperability, compliance to standards and publication (data curation).

Data quality

Develop and manage/supervise policies on data protection, privacy, IPR and ethical issues in data management.

Data protection and privacy

Develop and implement data management strategy for data collection, storage, preservation, and availability for further processing.

Data Management

Create new understandings by using research methods (including hypothesis, artefact/experiment, evaluation) or similar engineering research and modern development methods.

Research methods

Direct systematic study toward understanding of the observable facts and discovers new approaches to achieve research or organisational goals.

Understanding observable facts

Analyse domain related research process model, identify and analyse available data to identify research questions and/or organisational objectives and formulate sound hypothesis.

Data Science and research process models

Undertake creative work, making systematic use of investigation or experimentation, to discover or revise knowledge of reality and uses this knowledge to devise new applications, contribute to the development of organizational objectives.

Investigation and experimentation

Develop and guide data driven projects, including project planning, experiment design, data collection and handling.

Data driven projects

Create new understandings and capabilities by using the scientific method (hypothesis, test/artefact, evaluation) or similar engineering methods to discover new approaches to create new knowledge and achieve research or organisational goals

Research Methods and Project Management

Analyse information needs, assess existing data and suggest/identify new data required for specific business context to achieve organizational goal, including using social networks and open data sources.

Information needs

Operationalise fuzzy concepts to enable key performance indicators measurement to validate the business analysis, identify and assess potential challenges.

Fuzzy concepts

Deliver business focused analysis using appropriate BA/BI methods and tools, identify business impact from trends, make business case as a result of organisational data analysis and identified trends.

Business Analytics (BA) and Business Intelligence (BI)

Analyse opportunity and suggest use of historical data available at organisation for organizational processes optimization.

Process optimization

Analyse customer relations data to optimise/improve interacting with the specific user groups or in the specific business sectors.

Customer relationship management

Analyse multiple data sources for marketing purposes and identify effective marketing actions.

Data-driven Marketing

Develop required data analytics for organizational tasks, integrate data analytics and processing applications into organization workflow and business processes to enable agile decision making, apply different Data Science process model (CRISP-DM, ASUM, TDSP).

Business and Organization

Apply data science principles in related domains like biology, finances or statistics.

Domain knowledge 

Use domain knowledge (scientific or business) to develop relevant data analytics applications; adopt general Data Science methods to domain specific data types and presentations, data and process models, organisational roles and relations

Domain related Competences: Applied to Business Analytics

Understand and apply fundamental algorithms and data structures to solve computational problems efficiently.

Algorithms and data structures

Employ advanced mathematical principles and techniques to model and solve complex problems in data science. 

Mathematics

Grasp the core principles of computer science, including computational thinking, problem-solving and the theoretical underpinnings of hardware and software.

Computer science basics

Analyze the complexity of algorithms and understand the trade-offs involved in designing and selecting algorithms based on time and space requirements.

Complexity

Theoretical fundamentals

Design, write, test and debug code in various programming languages. Achieve a deep understanding of syntax, semantics and best practices in software development.

Programming

Leverage existing libraries and development tools to build applications more efficiently. This includes familiarity with frameworks, IDEs and code management tools that speed up development and enhance collaboration.

Libraries and tools

Apply software engineering methodologies to design, develop, maintain and evaluate software systems. 

Software engineering

Create and manage development pipelines for continuous integration and continuous deployment (CI/CD). This involves setting up automated workflows for testing, building and deploying software, ensuring streamlined and reliable software delivery.

Development pipelines

Application development

Inspire and guide individuals and teams towards achieving organizational goals.

Leadership

Adapt to and effectively manage various workplace scenarios and environments.

Workplace competencies

Convey information clearly and effectively through both verbal and written forms. 

Competency Grid