Data quality specialist to data scientist
A data quality specialist already meets 27% of what the data scientist role asks for. The move turns on 47 required skills not yet in the profile.
83.1/ 100
1 How much of what a data scientist is expected to do is already part of a data quality specialist’s work. Directional: the figure for the reverse move differs, because moving into a speciality is not the same problem as moving out of one. 2 Every skill the data scientist role is recorded as needing, required and optional together. What these numbers mean.
Why this move works
A data scientist role treats 16 of its required skills as things a data quality specialist already does. You would walk in with these on day one.
- data engineering
- data ethics
- design database scheme
- establish data processes
- execute analytical mathematical calculations
- handle data samples
What stands in the way is 47 required skills the profile does not yet cover. Table 3 groups them; Table 4 says which to take first.
Table 2 · What you already bring
Of the 27 skills that carry over, these are the ones fewest other occupations ask for. A data scientist role needs them, and most people applying for one will not have them already. This is the part of a data quality specialist background worth leading with.
- already held handle data samples information skills
- already held data ethics arts and humanities
- already held normalise data working with computers
- already held healthcare analytics health and welfare
- already held implement data quality processes working with computers
- already held perform data cleansing working with computers
All 27 carried skills, including the 16 the data scientist role treats as required.
Table 3 · What you may need to learn
The 70 missing skills fall into 10 areas of the ESCO skill hierarchy, numbered in the order worth working in. Areas carrying the most required skills come first, and inside each one the required skills sit above the optional ones. Nothing here is a verdict on you: it is a list of what this particular job asks for that your current one does not.
- still to learn manage intellectual property rights required
- still to learn communicate with a non-scientific audience required
- still to learn deliver visual presentation of data required
- still to learn develop professional network with researchers and scientists required
- still to learn disseminate results to the scientific community required
- still to learn draft scientific or academic papers and technical documentation required
- still to learn evaluate research activities required
- still to learn increase the impact of science on policy and society required
- still to learn interact professionally in research and professional environments required
- still to learn mentor individuals required
- still to learn promote the transfer of knowledge required
- still to learn publish academic research required
- still to learn speak different languages required
- still to learn think abstractly required
- still to learn write scientific publications required
- optional, not held manage ICT data architecture optional
- optional, not held apply blended learning optional
- optional, not held teach in academic or vocational contexts optional
- still to learn manage data collection systems required
- still to learn apply for research funding required
- still to learn apply research ethics and scientific integrity principles in research activities required
- still to learn collect ICT data required
- still to learn conduct research across disciplines required
- still to learn demonstrate disciplinary expertise required
- still to learn integrate gender dimension in research required
- still to learn interpret current data required
- still to learn manage findable accessible interoperable and reusable data required
- still to learn manage research data required
- still to learn perform scientific research required
- still to learn promote open innovation in research required
- still to learn promote the participation of citizens in scientific and research activities required
- still to learn synthesise information required
- optional, not held create data models optional
- optional, not held make data-driven decisions optional
- still to learn data mining required
- still to learn data models required
- still to learn information extraction required
- still to learn online analytical processing required
- still to learn data science required
- still to learn data visualisation software required
- optional, not held Hadoop optional
- optional, not held unstructured data optional
- optional, not held computer simulation optional
- optional, not held image recognition optional
- optional, not held scientific computing optional
- still to learn build recommender systems required
- still to learn develop data processing applications required
- still to learn operate open source software required
- still to learn manage open publications required
- still to learn use databases required
- optional, not held integrate ICT data optional
- optional, not held manage ICT data classification optional
- optional, not held perform data mining optional
- optional, not held use spreadsheets software optional
- still to learn empirical analysis required
- still to learn scientific literature required
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- still to learn statistical modeling techniques required
- still to learn mathematical modelling required
- optional, not held business analytics optional
- optional, not held computational biology optional
4 further areas in the appendix
Table 4 · Where to start
The 3 entries a data scientist role is least likely to hire without. The ordering is computed from the skill data, not from what pays.
- 01 build recommender systems practise this
- 02 develop data processing applications practise this
- 03 data mining study this
Each entry opens a course search for that skill. Career Overlap earns nothing from these links.
Appendix · The rest of the record
All 27 skills that carry over
- already held data engineering knowledge
- already held data ethics knowledge
- already held design database scheme skill
- already held establish data processes skill
- already held execute analytical mathematical calculations skill
- already held handle data samples skill
- already held implement data quality processes skill
- already held normalise data skill
- already held perform data cleansing skill
- already held perform project management skill
- already held query languages knowledge
- already held report analysis results skill
- already held resource description framework query language knowledge
- already held statistics knowledge
- already held use data processing techniques skill
- already held visual presentation techniques knowledge
- already held LDAP knowledge
- already held LINQ knowledge
- already held MDX knowledge
- already held N1QL knowledge
- already held SPARQL knowledge
- already held XQuery knowledge
- already held data quality assessment knowledge
- already held define data quality criteria skill
- already held design database in the cloud skill
- already held healthcare analytics knowledge
- already held manage data skill
The 4 learning areas not shown above
- still to learn information categorisation required
- still to learn quantitative analysis required
- optional, not held digital curation optional
- optional, not held social network analysis optional
- still to learn manage personal professional development required
- optional, not held business intelligence optional
- optional, not held marketing analytics optional
- optional, not held state estimation optional
23 supplementary skills, helpful but not required
- optional, not held digital curation optional
- optional, not held Hadoop optional
- optional, not held business analytics optional
- optional, not held computational biology optional
- optional, not held create data models optional
- optional, not held integrate ICT data optional
- optional, not held manage ICT data architecture optional
- optional, not held manage ICT data classification optional
- optional, not held perform data mining optional
- optional, not held unstructured data optional
- optional, not held apply blended learning optional
- optional, not held business intelligence optional
- optional, not held computer simulation optional
- optional, not held image recognition optional
- optional, not held make data-driven decisions optional
- optional, not held marketing analytics optional
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- optional, not held scientific computing optional
- optional, not held social network analysis optional
- optional, not held state estimation optional
- optional, not held teach in academic or vocational contexts optional
- optional, not held use spreadsheets software optional
13 held skills the data scientist role does not ask for
- not needed by the target role address problems critically skill
- not needed by the target role build business relationships skill
- not needed by the target role business processes knowledge
- not needed by the target role database knowledge
- not needed by the target role execute ICT audits skill
- not needed by the target role information structure knowledge
- not needed by the target role manage database skill
- not needed by the target role manage schedule of tasks skill
- not needed by the target role manage standards for data exchange skill
- not needed by the target role perform data analysis skill
- not needed by the target role process data skill
- not needed by the target role train employees skill
- not needed by the target role utilise regular expressions skill
26 gaps that are knowledge rather than practice
Knowledge gaps usually close through study. Practical skill gaps usually need something you can point at.
- still to learn data mining required
- still to learn data models required
- still to learn information categorisation required
- still to learn information extraction required
- still to learn online analytical processing required
- still to learn statistical modeling techniques required
- still to learn data science required
- still to learn data visualisation software required
- still to learn empirical analysis required
- still to learn mathematical modelling required
- still to learn quantitative analysis required
- still to learn scientific literature required
- optional, not held digital curation optional
- optional, not held Hadoop optional
- optional, not held business analytics optional
- optional, not held computational biology optional
- optional, not held unstructured data optional
- optional, not held business intelligence optional
- optional, not held computer simulation optional
- optional, not held image recognition optional
- optional, not held marketing analytics optional
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- optional, not held scientific computing optional
- optional, not held social network analysis optional
- optional, not held state estimation optional
Other moves recorded from data quality specialist
Common questions about this move
Can a data quality specialist become a data scientist?
Yes, and it is a career change move. A data quality specialist already covers 27% of what a data scientist is expected to do: 27 skills carry over, 16 of which the data scientist role treats as required.
What skills does a data quality specialist need to become a data scientist?
47 required skills are missing, plus 23 optional ones. They fall into 10 areas of the ESCO skill hierarchy, led by communication, collaboration and creativity. The single most important is build recommender systems.
How hard is the move from data quality specialist to data scientist?
Its learning distance is 83.1 out of 100, which is the career change band. That accounts for both how much is missing and how specialised those skills are.
How this record was compiled
Both occupations are taken from ESCO, which lists the skills and knowledge each occupation is expected to have and marks every one required or optional. Nothing here is a prediction about hiring, and nothing here knows that a particular employer wants a particular certificate. Treat Table 3 as a starting point for your own research rather than a syllabus. The full method states what these figures can and cannot tell you.