Mapping the Data Analytics and Data Science Profession

Our Certification is mapped to the EDISON Data Science Framework (ESDF) which has been developed to support, guide and ultimately accelerate the education process of fit-for-purpose Data Science Professionals.

The EU-funded EDISON Project has put in place foundation mechanisms that will speed-up the Increase in the number of competent and qualified Data Scientists across Europe and beyond. The EDSF is a collection of documents that define the Data Science profession. Freely available, these documents have been developed to guide educators and trainers, employers and managers, and Data Scientist themselves.

The collection of documents break down the complexity of the skills and competencies needed to define Data Science as a professional practice.

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Defining the Profession

In a rapidly developing profession like Data Analytics and data Science, it is important for students, professionals and employers to be able to map how each role is defined, where they interface and how they interact.

The Professional Skills Framework brings together these elements into one cohesive model and defining the skills, knowledge items and competences required for each role.

Business Analytics Lifecycle

Each professional role defined in the Framework plays an important part in the overall Analytics Lifecycle. At different stages in the lifecycle, some roles become more prominent while others support. This can change and evolve as the project progresses.

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Skills

Knowledge

Data
Applications Engineer

Data
Engineer

Business/
Data Analysis

Data
Scientist

Data Management

At least one of the following:
Informatica
Abinitio
Talend
IBM Data Studio
SAS Data Integration
Dataflux

At least one of the following:
Informatica
Abinitio
Talend
IBM Data Studio
SAS Data Integration
Dataflux

Analytics

At least one of the following:
R
IBM SPSS
SAS

Alteryx

At least one of the following:
R
IBM SPSS
SAS
Alteryx

Visualisation

At least one of the following:
Tableau
Spotfire
Qlik
PowerBI
SAS Visual Analytics

At least one of the following:
Tableau
Spotfire
Qlik
PowerBI
SAS Visual Analytics

At least one of the following:
Tableau
Spotfire
Qlik
PowerBI
SAS Visual Analytics

Languages

Python

At least one of the following:
SQL
T-SQL (Stored procedures and functions)
PL/SQL
pgPL/Sql
Pig
Hive
Impala

At least one of the following:
Python
Java
JavaScript (or similar)
Go
Ruby
C
C#
VBA

At least one of the following:
Python
SQL
T-SQL (Stored procedures and functions)
PL/SQL
pgPL/Sql
Pig
Hive
Impala
Java
JavaScript (or similar)
Go
Ruby
C
C#
VBA

Databases

At least one of the following:
SQL Server
PostgreSQL
Teradata
Oracle
IBM DB2
MySql
SAP HANA
Mongo DB

At least one of the following:
SQL Server
PostgreSQL
Teradata
Oracle
IBM DB2
MySql
SAP HANA
Mongo DB

Shell Scripting

At least one of the following:
DOS batch
Power Shell
BASH (UNIX/LINUX utilities)

At least one of the following:
DOS batch
Power Shell
BASH (UNIX/LINUX utilities)

Other Tools/Software

At least one of the following:
JIRA
MOVEit
SVN (Subversion)
Git
Monarch

At least one of the following:
JIRA
MOVEit
SVN (Subversion)
Git

At least one of the following:
JIRA
MOVEit
SVN (Subversion)
Git
Monarch

Techniques/Methods

Machine Learning
Predictive Modelling

Big data concepts
Data Warehousing

Big data concepts
Data Warehousing

Machine Learning
Predictive Modelling
Big data concepts
Data Warehousing

Data
Applications Engineer

Data
Engineer

Business/
Data Analysis

Data
Scientist

Machine Learning (supervised): Decision trees, Naïve Bayes classification, Ordinary least square regression, Logistic regression, Neural Networks, SVM (Support Vector Machine), Ensemble methods, others

Systems Engineering and Software Engineering principles, methods and models, distributed systems design and organisation

Data management and enterprise data infrastructure, private and public data storage systems and services

Machine Learning (supervised): Decision trees, Naïve Bayes classification, Ordinary least square regression, Logistic regression, Neural Networks, SVM (Support Vector Machine), Ensemble methods. Systems Engineering and Software Engineering principles, methods and models, distributed systems design and organisation. Data management and enterprise data infrastructure, private and public data storage systems and services

Machine Learning (unsupervised): clustering algorithms, Principal Components Analysis (PCA), Singular Value Decomposition (SVD), Independent Components Analysis (ICA)

Cloud Computing, cloud based services and cloud powered services design

Data storage systems, data archive services, digital libraries, and their operational models

Machine Learning (unsupervised): clustering algorithms, Principal Components Analysis (PCA), Singular Value Decomposition (SVD), Independent Components Analysis (ICA). Cloud Computing, cloud based services and cloud powered services design. Data storage systems, data archive services, digital libraries, and their operational models.

Machine Learning (reinforced): Q-Learning, TD-Learning, Genetic Algorithms)

Big Data technologies for large datasets processing: batch, parallel, streaming systems, in particular cloud based

Data governance, data governance strategy, Data Management Plan (DMP)

Machine Learning (reinforced): Q-Learning, TD-Learning, Genetic Algorithms). Big Data technologies for large datasets processing: batch, parallel, streaming systems, in particular cloud based. Data governance, data governance strategy, Data Management Plan (DMP).

Data Mining (Text mining, Anomaly detection, regression, time series, classification, feature selection, association, clustering)

Applications software requirements and design, agile development technologies, DevOps and continuous improvement cycle

Data Architecture, data types and data formats, data modeling and design, including related technologies (ETL, OLAP, OLTP, etc.)

Data Mining (Text mining, Anomaly detection, regression, time series, classification, feature selection, association, clustering). Applications software requirements and design, agile development technologies, DevOps and continuous improvement cycle. Data Architecture, data types and data formats, data modeling and design, including related technologies (ETL, OLAP, OLTP, etc.)

Text Data Mining: statistical methods, NLP, feature selection, apriori algorithm, etc.

Systems and data security, data access, including data anonymisation, federated access control systems

Data lifecycle and organisational workflow, data provenance and linked data

Text Data Mining: statistical methods, NLP, feature selection, apriori algorithm, etc. Systems and data security, data access, including data anonymisation, federated access control systems. Data lifecycle and organisational workflow, data provenance and linked data

Prescriptive Analytics

Compliance based security models, privacy and IPR protection

Data curation and data quality, data integration and interoperability

Prescriptive Analytics. Compliance based security models, privacy and IPR protection. Data curation and data quality, data integration and interoperability

Prescriptive Analytics

Relational, nonrelational databases (SQL and NoSQL), Data Warehouse solutions, ETL (Extract, Transform, Load), OLTP, OLAP processes for large datasets

Data protection, backup, privacy, IPR, ethics and responsible data use

Prescriptive Analytics. Relational, nonrelational databases (SQL and NoSQL), Data Warehouse solutions, ETL (Extract, Transform, Load), OLTP, OLAP processes for large datasets. Data protection, backup, privacy, IPR, ethics and responsible data usu

Graph Data Analytics: path analysis, connectivity analysis, community analysis, centrality analysis, subgraph isomorphism, etc.

Big Data infrastructures, high-performance networks, infrastructure and services management and operation

Metadata, PID, data registries, data factories, standards and compliance

Graph Data Analytics: path analysis, connectivity analysis, community analysis, centrality analysis, subgraph isomorphism, etc. Big Data infrastructures, high-performance networks, infrastructure and services management and operation. Metadata, PID, data registries, data factories, standards and compliance

Qualitative analytics

Modeling and simulation, theory and systems

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

Qualitative analytics. Modeling and simulation, theory and systems. Open Data, Open Science, research data archives/repositories, Open Access, ORCID

Natural language processing

Information systems, collaborative systems

Data preparation and pre-processing

Natural language processing. Information systems, collaborative systems. Data preparation and pre-processing

Business Analytics (BA) and Business Intelligence (BI); methods and data analysis; cognitive technologies

Optimisation

Business Analytics (BA) and Business Intelligence (BI); methods and data analysis; cognitive technologies. Optimisation

Data Warehouses technologies, data integration and analytics

Data driven User Experience (UX) requirements and design

Data Warehouses technologies, data integration and analytics
Data driven User Experience (UX) requirements and design

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