Certified Data Scientist

Certification Requirements

Applicants must have a minimum of two years experience in analytics or a related area.
Applicants must have confidence in at least one skillset under each heading:

Data Management:
  • Informatica
  • Abinitio
  • Talend
  • IBM Data Studio
  • SAS Data Integration
  • Dataflux
  • Alteryx
  • R
  • SAS
  • Tableau
  • Spotfire
  • Qlik
  • PowerBl
  • SAS Visualisation Analytics
  • Python
  • Java
  • Javascript (or similar)
  • Go
  • Ruby
  • C
  • C#
  • VBA
  • SQL
  • T-SQUL (Stored Procedures and Functions)
  • PL/SQL
  • phPL/Sql
  • Pig
  • Hive
  • Impala
  • SQL Server
  • Postgre SQL
  • Teredata
  • Oracle
  • IMB DB2
  • MySwl
  • Mongo DB
Shell Scripting
  • DOS Batch
  • Power Shell
  • BASH (UNIX/LINUX utilities)
Techniques & Methods
  • Machine Learning
  • Predictive Modelling
  • Big Data Concepts
  • Data Warehousing

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 LifecycleDevelop 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.


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.
Certification Fee:
€200 + VAT (€246)

Not Ready for Certification Yet?

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.

4 weeks, on demand video and reading

4 weeks of study, 1-3 hours/week

3 weeks of study, 5-6 hours/week

4 weeks, on-demand video and reading

6 weeks of study, 2-3 hours/week

5 weeks of study, 5 - 10 hours/week

5 weeks of study, 5 - 10 hours/week

4 weeks of study, 2-3 hours/week

7 weeks of study, 5-8 hours/week

6 weeks of study, 5-8 hours/week

5 weeks, on-demand video and reading.

4 weeks, on-demand video and reading.

5 weeks of study, 1-2 hours/week

4 weeks, video lectures and reading.

5.5 hours of on-demand video

4 weeks of study, 5-7 hours/week

4 weeks, 3 -5 hours per week

4 weeks of study, 5-6 hours/week

4 weeks of study, 2-3 hours/week

Industry Advisory Council Certification Group
How does the certification process work?

If you believe you already have the requisite skills and experience to qualify as a professional in your chosen field, you can register for certification and upload evidence of your experience and qualifications (Degrees, Diplomas, Curriculum Vitae, LinkedIn profile, Employers References) to our Certifications Committee for review.

If you’re not yet ready for certification, we have identified a learning path of online courses with Coursera®, which is mapped to our requirements for qualification.

Should you choose to expand your skills and knowledge with Coursera® or with alternative methods, you can return to us, once your training is complete and submit your application to our Certification Committee for review.

How long does it take?

Our certification committee sit at the end of each month. Fully complete applications for certifications will be reviewed at that meeting. Candidates will be informed shortly thereafter. You can expect to hear back from us within six weeks.

Is the Certification an Academic Qualification?

No. Analytics Institute qualifications are based on the Edison Data Science Framework and are Professional Qualifications. They are not designed either to be considered as academic qualifications or to replace them. Analytics Institute qualifications are industry supported.

Does my the Certification Fee include the online training?

No. The Certification Fee includes assessment, the award of the Qualification and listing on our Professional Register, once qualified.

What is the Edison Framework?

The Edison Consortium includes industry and academic organisations from across Europe. The core consortium includes members from the University of Southampton, the University of Stavanger, the University of Amsterdam, the EGI Foundation, the Research Institute for Telecommunication and Cooperation and Inmark Europa.

The EDISON Data Science Framework 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 Scientists themselves. This collection of documents collectively breakdown the complexity of the skills and competences need to define Data Science as a professional practice.

The Analytics Institute has based our qualifications on the Edison Framework.

Who is on the Qualifications Committee?

Our Qualification Committee includes senior experts from our Industry Advisory Council including Pfizer, Aon, EY, Microsoft, IBM and Novartis.

What is the Analytics Institute?

The Analytics Institute exists to support the Data Science and Analytics sector. We do this by providing a platform for our members to share insights and expertise, to collaborate and build networks in the industry and to foster innovation in the sector. We are fully supported in our mission by leading organisations in the public and private sectors.

Corporate member organisations can ensure their staff are kept fully up to date on new developments in the sector by attending our monthly Continuous Professional Development events. They also have access to analytics training programmes designed around members specific training requirements. We host quarterly networking events and user groups so that analytics professionals can make new friends and build contacts in the industry. Our annual National Analytics Conference is the key industry event of the year.