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

A method of training machine learning models using labeled data where both input and desired output are provided (Decision trees, Naïve Bayes classification, Ordinary least square regression, Logistic regression, Neural Networks, SVM (Support Vector Machine), Ensemble methods, others).

Machine Learning (supervised)

An approach where machine learning models are trained without any labeled data, aiming to identify underlying patterns or structures within (Clustering algorithms, Principal Components Analysis (PCA), Singular Value Decomposition (SVD), Independent Components Analysis (ICA)).

Machine Learning (unsupervised)

A type of machine learning where an agent learns how to behave in an environment by taking actions and receiving rewards or penalties (Q-Learning, TD-Learning, Genetic Algorithms).

Machine Learning (reinforcement)

A learning technique that utilizes a small amount of labeled data combined with a larger set of unlabeled data.

Machine Learning (semi-supervised)

Possess a comprehensive understanding of deep learning frameworks, neural network architectures, and learning algorithms.

Deep Learning

A subset of data mining focused specifically on extracting valuable insights from textual data sources (Statistical methods, NLP, feature selection, apriori algorithm, etc.).

Text Mining

An approach in analytics that focuses on summarizing and interpreting historical data to identify trends, analyze the impact of decisions or events and establish a clear understanding of past behaviors.

Descriptive Analytics

The process of analyzing numerical data to derive meaningful conclusions (interpret and visualize data, often resulting in measurable insights).

Quantitative Analytics

Involves analyzing non-numerical data such as text, images, or audio. Goal is to understand underlying themes, patterns, or reasons in the data, often relying on interpretation and judgment.

Qualitative Analytics

Leverages statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.

Predictive Analytics

Not just about predicting outcomes (predictive analytics) but also recommending specific actions to achieve desired results.

Prescriptive Analytics

Understanding the techniques and methods used in the initial phase of data analysis. It includes mastering data visualization tools, statistical summaries, and various approaches to uncover insights from data sets without making prior assumptions.

Exploratory Data Analytics

Maintain an in-depth understanding of multivariate statistical techniques, including regression models, factor analysis, cluster analysis, and MANOVA. Interpret complex datasets that contain multiple variables and the interrelationships between them.

Multivariate Analytics

The study of relationships and patterns within data represented as a graph, consisting of nodes (entities) and edges (relationships). Important topics are path analysis, connectivity analysis and community analysis.

Graph Data Analytics

Machine Learning Model Formats refer to the ways in which machine learning models are structured, stored, and utilized (PMML, PFA, ONNX, TensorFlow, etc.).

Machine Learning Model Formats

Application of computational techniques to the analysis and synthesis of natural language and speech (sentiment analysis, machine translation, speech recognition, text generation, etc.).

Natural language processing

Performance and Accuracy Metrics are used to evaluate and compare the effectiveness of statistical models and machine learning algorithms (accuracy, precision, recall, F1 score for classification tasks, etc.).

The practice of examining vast sets of data to discover patterns, correlations, and anomalies (Text mining, Anomaly detection, regression, time series, classification, feature selection, association, clustering).

Data Mining

Principles and techniques for converting data into graphical representations.

Visualisation fundamentals

Operations Research is the application of mathematical models, statistical analysis, simulation and optimization techniques to make complex decisions in business, engineering and science.

Operations Research

Process of creating a digital twin or model of a real-world system to study its behavior under various conditions.

Simulation

Finding the most efficient or effective solution from all feasible solutions.

Optimisation

Understand the methods and skills used to effectively communicate information and ideas to an audience.

Presentation techniques

Conveying the insights from data in a compelling and understandable manner.

Storytelling

Understand the techniques and theories behind sampling methods used in data analysis. This encompasses knowledge of random, stratified, and cluster sampling processes, as well as understanding the importance of sample size and representativeness for statistical inference.

Sampling

Data Science Process Models encompass the systematic approaches used to guide data professionals through the various stages of data analysis and decision-making (CRISP-DM, ASUM, TDSP).

Data Science Process Models

Involves the process of transforming raw data into a set of representative features that are essential for machine learning algorithms. It helps by simplifying the complexity of data, enhancing computational efficiency and improving the performance of predictive models.

Feature Extraction

Data Analytics Knowledge

An understanding of the interdisciplinary processes required to develop and manage complex systems throughout their life cycles. Important topics are Systems Engineering principles, methods and models, distributed systems design and organisation.

Systems Engineering principles

Familiarity with the delivery of computing services over the internet, including storage, processing power and networking capabilities. Important topics are Cloud Computing, cloud based services and cloud powered services design.

Cloud Computing

Knowledge of tools and techniques to handle, analyze and visualize very large datasets that traditional data processing software cannot manage (batch, parallel, streaming systems, in particular cloud based).

Big Data technologies for large datasets processing

Knowledge of software that manages computer hardware and provides services for computer programs, serving as an intermediary between users and the computer hardware.

Operating systems

Systems and data security, data access, inclusive data anonymisation, federated access control systems.

Systems and Data Security

Understanding of security standards and regulations, and the methods to ensure data privacy and protect intellectual property rights.

Compliance

Proficiency in the administration of databases (Relational, nonrelational databases [SQL and NoSQL], Data Warehouse solutions, ETL/ELT, OLTP/OLAP processes)

Knowledge of the underlying infrastructure and networking solutions required to support the processing and analysis of large datasets.

Big Data infrastructure services, highperformance networks

Familiarity with theoretical and practical approaches to replicate the behavior of systems for analysis and prediction.

Modeling and simulation, theory and systems

An understanding of integrated systems used to collect, process, store and distribute information, as well as platforms that facilitate collaborative work.

Information systems and collaborative systems 

Demonstrate expertise in the principles and best practices of data modeling, including logical and physical model design. Understand the methods for creating data models that accurately represent complex data structures and relationships within a database or data architecture.

Data models

Possess a thorough understanding of model tuning techniques to enhance the performance of predictive models. This includes knowledge of parameter tuning, cross-validation, feature selection and the use of performance metrics to evaluate and improve model accuracy.

Model tuning

Be knowledgeable about various data storage solutions, including databases, file systems, cloud storage services, and data warehouses, along with their respective performance, scalability, and security aspects.

Data storage

Describes crucial steps in the data science workflow involving cleaning, transforming and normalizing data to make it suitable for analysis.

Data wrangling (preparation and pre-processing)

Understand the methodologies and tools for collecting data from various sources.

Data collection (acquisition)

Data Science Engineering Knowledge

The practice of collecting, storing and using data securely, efficiently and cost-effectively. Including the Data Management Plan (DMP) describing the operational part of the Data Strategy.

Data management

Oversight and management responsibilities for ensuring the proper handling and use of data, ensuring its quality, privacy, and security (Research Data Management, FAIR data principles, Data Stewardship, Data storage systems, data archive services, data libraries, and their operational models).

Data stewardship

A comprehensive approach to managing an organization's data, ensuring its quality, availability, accessibility and security through policies, procedures and standards (Data governance, data governance strategy, Data Management Plan [DMP]).

Data governance

The design and specification of data systems, including the blueprint for how data is stored, integrated, and maintained (Data Architecture, data types and data formats, data modeling and design, including related technologies [ETL, ELT, etc.]).

Data architecture

Understand the structured sequence of processes through which a piece of work passes from initiation to completion within an organization, including the roles, responsibilities and procedures involved.

Organisational workflow

Activities related to organizing, enhancing and ensuring the accuracy and usability of data and the ability to combine and utilize different datasets together.

Data curation (data integration and interoperability)

Practices to ensure that data is guarded against unauthorized access, use, disclosure, disruption, modification, or destruction (Data protection, backup, RAID).

Data protection

Understanding the descriptive information about datasets, unique identifiers for data objects, principles for making data findable, accessible, interoperable and reusable (Metadata, PID, FDO [FAIR Digital Object], data registries, data factories, standards and compliance).

Metadata

The study of proper business policies and practices regarding potentially controversial subjects such as data privacy, transparency and moral rights and responsibilities.

Ethics

Techniques to protect personal information by removing or modifying personal identifiers, ensuring individuals' rights to data privacy and the rights over creations of the mind (Privacy, IPR and responsible data use).

Anonymisation, Privacy and IPR

Have a firm grasp of the types and sources of bias that can occur in data collection, analysis and interpretation. This includes understanding selection bias, confirmation bias and measurement bias and knowing strategies to mitigate their impact on research and decision-making processes.

Bias

Overarching vision that includes what the organization wants to achieve with its data, how it aligns with business objectives and the benefits it expects to gain.

Data strategy 

Have a comprehensive understanding of the stages through which data passes, from initial generation or acquisition through various phases of processing, distribution, use, maintenance and its final disposition or archival.

Data lifecycle

Possess insights into the origins, custody and transformations data undergoes, providing context and ensuring the integrity and historical record of the data throughout its lifecycle.

Data provenance

Have a thorough understanding of the various data formats (XML, JSON, CSV, etc.) and structures (relational, hierarchical, graph, etc.) used to store, manage and exchange data effectively and efficiently.

Data formats and structures

Knowledge of the principles and practices to ensure high data quality, including accuracy, validity, reliability, timeliness, and relevance of the data used for analysis.

Data quality

Comprehends the diversity and characteristics of various data sources, ranging from traditional databases to real-time data streams and unstructured data. 

Data sources

Data Management Knowledge

Systematic techniques and procedures used to investigate, study and analyze a specific subject or field of study (Research methods, research cycle, hypothesis definition and testing).

Research methods

Practices and principles of designing experiments, constructing models and planning research to ensure consistent results.

Experiment design, modelling and planning

Advocacy and practices for freely accessible research publications and data, databases for storing and sharing research, unique identifiers for researchers, and guidelines for Findable, Accessible, Interoperable, and Reusable data.

Open Access, Open Science, Open Data, research data archives/ repositories, ORCID, FAIR data principles

Oversight and best practices for handling, organizing, storing, and sharing research data to ensure its quality and accessibility (Research Data Management, Data Management Plan (DMP), data stewardship, FAIR data principles).

Research Data Management

Techniques for identifying and articulating specific scenarios or problems that a system or solution should address (research infrastructure and projects).

Use-cases analysis (research and projects)

The practice of initiating, planning, executing, controlling and closing a project to achieve specific goals within certain criteria (scope, planning, assessment, quality, risk and team management).

Project management

The process of recording and detailing the specific aspects, methods, results and other pertinent information related to a project or study.

Documentation

Understand the principles and practices that ensure the consistent replicability of results in experimental research, including the documentation, methodologies and standards required for verification and validation.

Experimental research reproducibility

Research Methods and Project Management Knowledge

Practices and tools to analyze business data to make informed business decisions and gain insights into organizational operations.

Business Analytics (BA) and Business Intelligence (BI)

Systematic approach to improving and automating organizational processes to optimize efficiency and effectiveness (Business Processes Management [BPM], general business processes and operations, organisational processes analysis/modelling).

Business Processes Management (BPM)

Flexible and iterative strategies emphasizing data-driven decision-making in organizational methodologies and processes.

Agile data-driven methodologies, processes and enterprises

Application of statistical methods to economic data to test hypotheses and forecast future trends.

Econometrics: Data analysis and applications

Utilization of data in managing interactions with potential and existing customers and designing user interfaces for optimal user experience.

Data-driven Customer Relations Management (CRM) and User Experience (UX) design

Identification and articulation of specific business scenarios or problems that an enterprise or industry should address.

Use-cases analysis (business and industry)

Technologies and practices for storing large volumes of data from different sources, integrating them and performing analytics.

Data Warehouses technologies, data integration and analytics

Marketing strategies and decisions informed by the analysis of data about or from consumers.

Data driven marketing

Specific knowledge about a certain field, industry, or area of expertise.

Domain knowledge

Identification, assessment and prioritization of risks followed by strategies to minimize or manage their impact.

Risk management

Hold a clear understanding of key performance indicators (KPIs) relevant to various business sectors, including the ability to select appropriate KPIs, set targets and interpret results to measure the effectiveness and efficiency of organizational performance.

Key performance indicators

Domain related Competences: Applied to Business Analytics Knowledge

Data Analytics and Statistical languages and tools

Harness spreadsheet software for data analytics, using functions and formulas to perform calculations, analyze trends and create visualizations for data-driven decision-making (Analysis ToolPack, PivotTables, etc. in Excel or Google Sheets).

Spreadsheet Data Analytics

Utilize statistical analysis software (such as R, IBM, SAS, or SPSS) to perform complex data manipulation, conduct statistical tests and generate insights from large datasets.

Statistical analysis software

Employ a variety of analytics, statistical and programming languages and tools such as WEKA, KNIME, Scala, Stata and Orange for specialized data processing, analysis and visualization tasks.

Other analytics, statistical and programming languages

Use data analytics programming languages and their libraries, like Python (pandas, numpy, mathplotlib, scipy, pytorch, scikitlearn, seaborn, etc.) or R (cran, ggplot2, dplyr, reshap2, etc.) to perform data cleaning, analysis and visualization at an advanced level.


Data analytics programming language and libraries

Leverage data mining tools to uncover patterns, correlations and insights from large data sets, facilitating predictive models and strategic decision-making (RapidMiner, Orange, R, WEKA, NLTK, etc.).

Data mining tools

Apply scripting languages for automating repetitive tasks, parsing data and creating data pipelines in analytics workflows (JavaScript, PHP, Octave, Pig, etc.).

Scripting languages

Use Matlab for data analytics, applying its computational environment for algorithm development, data visualization, data analysis and numerical computation.

Identifier	Name
DSDA01	Analytical techniques
DSDA02	Quantitative techniques
DSDA03	Performance and accuracy metrics
DSDA04	Visualisation and Presentation
DSDA05	Machine Learning

Identifier	Name
DSENG01	Data storage and application
DSENG02	Data collection and preperation
DSENG03	Big Data
DSENG04	Data analysis applications, tools and supporting infrastructures
DSENG05	Large scale data storage and processing
DSENG06	Data security mechanisms
DSENG07	Database management

Identifier	Name
DSDM01	Data strategy and management
DSDM02	Data models
DSDM03	Data sources and integration
DSDM04	Data handling
DSDM05	Data quality
DSDM06	Data protection and privacy

Identifier	Name
DSRM01	Research methods
DSRM02	Understanding observable facts
DSRM03	Data Science and research process models
DSRM04	Investigation and experimentation
DSRM05	Data driven projects

Identifier	Name
DSBA01	Information needs
DSBA02	Fuzzy concepts
DSBA03	Business Analytics (BA) and Business Intelligence (BI)
DSBA04	Process optimization
DSBA05	Customer relationship management
DSBA06	Data-driven Marketing
DSBA07	Business and Organization
DSBA08	Domain knowledge

Assets

DSDA - Data Analytics

DSENG - Data Science Engineering

DSDM - Data Management

DSRM - Research Methods and Project Management

DSBA - Domain related Competences: Applied to Business Analytics

DSTF - Theoretical fundamentals

DSAD - Application development

DSPC - Personal Competencies

Identifier	Name
DSTF01	Algorithms and data structures
DSTF02	Mathematics
DSTF03	Computer science basics
DSTF04	Complexity

Identifier	Name
DSAD01	Programming
DSAD02	Libraries and tools
DSAD03	Software engineering
DSAD04	Development pipelines

Identifier	Name
DSPC01	Leadership
DSPC02	Workplace competencies
DSPC03	Communication
DSPC04	Personal improvement
DSPC05	Teamwork
DSPC06	Problem solving
DSPC07	Ethical thinking