Certified Data Scientist

Applicants must have a minimum of two years experience in analytics or a related area in addition to the following criteria:

Skills

Knowledge

Applicants must have confidence in at least one skillset under each heading:

Data Management


Informatica
Abinitio
Talend
IBM Data Studio
SAS Data Integration
Dataflux

Analytics


Alteryx
R
IBM SPSS
SAS

Visualisation


Tableau
Spotfire
Qlik
PowerBl
SAS Visualisation Analytics

Databases


SQL Server
Postgre SQL
Teredata
Oracle
IMB DB2
MySwl
SAP HANA
Mongo DB

Languages


Python
Java
Javascript (or similar)
Go
Ruby
C
C#
VBA
SQL
T-SQUL (Stored Procedures and Functions)
PL/SQL
phPL/Sql
Pig
Hive
Impala

Techniques & Methods


Machine Learning
Predictive Modelling
Big Data Concepts
Data Warehousing

Shell Scripting


DOS Batch
Power Shell
BASH (UNIX/LINUX utilities)

Data Management


Informatica
Abinitio
Talend
IBM Data Studio
SAS Data Integration
Dataflux

Analytics


Alteryx
R
IBM SPSS
SAS

Visualisation


Tableau
Spotfire
Qlik
PowerBl
SAS Visualisation Analytics

Databases


SQL Server
Postgre SQL
Teredata
Oracle
IMB DB2
MySwl
SAP HANA
Mongo DB

Languages


Python
Java
Javascript (or similar)
Go
Ruby
C
C#
VBA
SQL
T-SQUL (Stored Procedures and Functions)
PL/SQL
phPL/Sql
Pig
Hive
Impala

Techniques & Methods


Machine Learning
Predictive Modelling
Big Data Concepts
Data Warehousing

Shell Scripting


DOS Batch
Power Shell
BASH (UNIX/LINUX utilities)


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Applicants must be able to demonstrate some capability in the following areas:

Develop and implement data engineering strategy for data collection, integration, quality, lineage, security, storage, preservation, and availability for further processing.Develop and implement data strategy, in particular, in a form of data management policy and Data Management Plan and path to execution of the plan – tooling & steps.

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

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

Maintain historical information on data handling, including reference to published data and corresponding data sources – Data Lineage and Data DictionaryEnsure data quality, accessibility, interoperability, compliance to standards, and publication.

Design, build, operate relational and non-relational databases (SQL and NoSQL), integrate them with the modern Data Solutions, ensure effective ETL (Extract, Transform, Load), OLTP, OLAP processes as appropriate such that they can be both implemented and scaled into target environments.

Visualise results of data analysis, design dashboard and use storytelling methodsUse appropriate data analytics and statistical techniques on available data to discover new relations and deliver insights into research problem or organisational processes and support decision-making.

Effectively use variety of data analytics techniques, such as Machine Learning (including supervised, unsupervised, semisupervised learning), Data Mining, Prescriptive and Predictive Analytics, for complex data analysis through the whole Business Analytics lifecycle.

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

Identify, extract, and pull together available and pertinent heterogeneous data, including modern data sources such as social media data, open data, governmental data.

Understand and use different performance and accuracy metrics for model validation in analytics projects, hypothesis testing, and information retrieval in line with the Business Analytics Lifecycle

Develop required data analytics for organisational tasks, integrate data analytics and processing applications into organisation workflow and business processes to enable agile decision making (Stage 5&6 of the Business Analytics Lifecycle).

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.

Use engineering principles (general and software) to research, design, develop and implement new instruments and applications for data collection, storage, analysis and visualisation

Develop and apply computational and data driven solutions to domain related problems using wide range of data analytics platforms, including Big Data technologies for large datasets and cloud based data analytics platforms.

Develop and prototype specialised data analysis applications, tools and supporting infrastructures for data driven scientific, business or organisational workflow; use distributed, parallel, batch and streaming processing platforms, including online and cloud based solutions for on-demand provisioned and scalable services.

Develop, deploy and operate large scale data storage and processing solutions using different distributed and cloud based platforms for storing data.

Consistently apply data security mechanisms and controls at each stage of the data processing, including data anonymisation, privacy and IPR protection.

Develop and implement data engineering strategy for data collection, integration, quality, lineage, security, storage, preservation, and availability for further processing.Develop and implement data strategy, in particular, in a form of data management policy and Data Management Plan and path to execution of the plan – tooling & steps.

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

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

Maintain historical information on data handling, including reference to published data and corresponding data sources – Data Lineage and Data DictionaryEnsure data quality, accessibility, interoperability, compliance to standards, and publication.

Design, build, operate relational and non-relational databases (SQL and NoSQL), integrate them with the modern Data Solutions, ensure effective ETL (Extract, Transform, Load), OLTP, OLAP processes as appropriate such that they can be both implemented and scaled into target environments.

Visualise results of data analysis, design dashboard and use storytelling methodsUse appropriate data analytics and statistical techniques on available data to discover new relations and deliver insights into research problem or organisational processes and support decision-making.

Effectively use variety of data analytics techniques, such as Machine Learning (including supervised, unsupervised, semisupervised learning), Data Mining, Prescriptive and Predictive Analytics, for complex data analysis through the whole Business Analytics lifecycle.

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

Identify, extract, and pull together available and pertinent heterogeneous data, including modern data sources such as social media data, open data, governmental data.

Understand and use different performance and accuracy metrics for model validation in analytics projects, hypothesis testing, and information retrieval in line with the Business Analytics Lifecycle

Develop required data analytics for organisational tasks, integrate data analytics and processing applications into organisation workflow and business processes to enable agile decision making (Stage 5&6 of the Business Analytics Lifecycle).

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.

Use engineering principles (general and software) to research, design, develop and implement new instruments and applications for data collection, storage, analysis and visualisation

Develop and apply computational and data driven solutions to domain related problems using wide range of data analytics platforms, including Big Data technologies for large datasets and cloud based data analytics platforms.

Develop and prototype specialised data analysis applications, tools and supporting infrastructures for data driven scientific, business or organisational workflow; use distributed, parallel, batch and streaming processing platforms, including online and cloud based solutions for on-demand provisioned and scalable services.

Develop, deploy and operate large scale data storage and processing solutions using different distributed and cloud based platforms for storing data.

Consistently apply data security mechanisms and controls at each stage of the data processing, including data anonymisation, privacy and IPR protection.


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Apply for Certification

If your skills, knowledge and experience meet the minimum requirements as set out in the framework, you can apply today to be assessed for certification.

Our certification committee meets to assess applicants on a regular basis. You should expect to hear from us within six weeks of applying.

Click Register Now and complete an online application form, uploading the required evidence:

  • CV.
  • LinkedIn Profile.
  • Certificates of Education (Degrees/Diploma).
  • Any Project Work details you wish to include to support your application.

Not yet ready for Certification?

If you’ve decided you’re not ready for certification, we’ve outlined a programme of online learning with Coursera© to get you there. Mapped to our framework, these courses will give you the necessary skills to complete certification in your chosen area of expertise. Coursera© courses can be completed in your own time and typically cost €49.

Simply choose the courses that fill the gaps in your knowledge. Once completed, you can return and register for certification, uploading your Coursera© certificates to support your application.

European Data Science Framework

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Our certification is mapped to the Edison Framework (EDSF).

A Europe-wide project to establish the necessary skills and define Data Science as a professional practice across the continent.

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Not yet ready for Certification?

If you’ve decided you’re not ready for certification, we’ve outlined a programme of online learning with Coursera© to get you there. Mapped to our framework, these courses will give you the necessary skills to complete certification in your chosen area of expertise. Coursera© courses can be completed in your own time and typically cost €49.

Simply choose the courses that fill the gaps in your knowledge. Once completed, you can return and register for certification, uploading your Coursera© certificates to support your application.

Coursera

Introduction to Data Analytics for Business

Commitment:

4 weeks, on demand video and reading.

Coursera

Business and Financial Modelling – Fundamentals

Commitment:

4 weeks of study, 1-3 hours/week.

Coursera

Introduction to Big Data

Commitment:

3 weeks of study, 5-6 hours/week.

Coursera

Predictive Modeling and Analytics

Commitment:

4-weeks, on demand video and reading.

Coursera

Big Data Modeling and Management Systems

Commitment:

6 weeks of study, 2-3 hours/week.

Coursera

Cloud Computing Applications, Part 1

Commitment:

5 weeks of study, 5 - 10 hours/week.

Coursera

Cloud Computing Applications, Part 2

Commitment:

5 weeks of study, 5 - 10 hours/week.

Coursera

Foundations of strategic business analytics

Commitment:

4 weeks of study, 2-3 hours/week.

Coursera

Machine Learning: Classification

Commitment:

7 weeks of study, 5-8 hours/week.

Coursera

Machine Learning: Clustering & Retrieval

Commitment:

6 weeks of study, 5-8 hours/week.

Coursera

Python for Data Science

Commitment:

5 weeks, on-demand video and reading.

Coursera

R Programming

Commitment:

4 weeks, on-demand video and reading.

Coursera

Hadoop Platform and Application Framework

Commitment:

5 weeks of study, 1-2 hours/week.

Coursera

Big Data Analysis with Scala and Spark

Commitment:

4 weeks, video lectures and reading.

Coursera

Become QlikView Designer from Scratch

Commitment:

5.5 hours of on-demand video.

Coursera

Visual Analytics with Tableau

Commitment:

4 weeks of study, 5-7 hours/week.

Coursera

Accounting Analytics

Commitment:

4 weeks, 3-5 hours per week.

Coursera

Customer Analytics

Commitment:

4 weeks of study, 5-6 hours/week.

Coursera

Operations Analytics

Commitment:

4 weeks of study, 2-3 hours/week.

Industry Advisory Council: Certification Group

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