Table 1Transition record

data scientisttodata quality specialist

A data scientist already meets 66% of what the data quality specialist role asks for. The move turns on 7 required skills not yet in the profile.

From data scientist

66%

Overlap1

To data quality specialist

27 Skills carried over

7 Required, not held

39.3 Difficulty2

adjacent 0–30

moderate 30–55

substantial 55–75

career change 75–100

this move, 39.3

This move: 39.3 of 100 · moderate

1 Share of the data quality specialist role’s weighted skill requirement already met by the data scientist profile. Required skills count in full, supplementary skills at 0.35. Directional: the figure for the reverse move differs. 2 Combines what is missing with how specialised it is, so a gap of general skills scores easier than the same number of narrow ones.

Table 2

Table 2 · What you already bring

Of the 27 skills that carry over, these are the ones fewest other occupations ask for. A data quality specialist role needs them, and most people applying for one will not have them already. This is the part of a data scientist background worth leading with.

Skill the target role also needs Area

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 13 the data quality specialist role treats as required.

Table 3

Table 3 · What you would need to learn

The 13 missing skills fall into 6 areas of the ESCO skill hierarchy, numbered below in the order worth working in: the areas carrying the most required skills come first, and inside each one the required skills sit above the supplementary ones.

Skill to acquire Tier

01 information skills 2 required, 1 supplementary

required, not held manage database required
required, not held process data required

optional, not held execute ICT audits optional

02 information and communication technologies (icts) 2 required

required, not held information structure required
required, not held database required

03 communication, collaboration and creativity 1 required, 2 supplementary

required, not held address problems critically required

optional, not held build business relationships optional
optional, not held train employees optional

04 management skills 1 required, 1 supplementary

required, not held manage standards for data exchange required

optional, not held manage schedule of tasks optional

05 working with computers 1 required, 1 supplementary

required, not held utilise regular expressions required

optional, not held perform data analysis optional

06 business, administration and law 1 supplementary

optional, not held business processes optional

Table 4

Table 4 · Where to start

The 3 entries a data quality specialist role is least likely to hire without. The ordering is computed from the skill data, not from what pays.

Each entry opens a course search for that skill. Career Overlap earns nothing from these links.

Appendix

Appendix · The rest of the record

All 27 skills that carry over

Skill Type

already held data ethics knowledge
already held define data quality criteria skill
already held design database scheme skill
already held establish data processes skill
already held handle data samples skill
already held implement data quality processes skill
already held manage data skill
already held normalise data skill
already held perform data cleansing skill
already held query languages knowledge
already held report analysis results skill
already held resource description framework query language knowledge
already held use data processing techniques skill
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 engineering knowledge
already held data quality assessment knowledge
already held design database in the cloud skill
already held execute analytical mathematical calculations skill
already held healthcare analytics knowledge
already held perform project management skill
already held statistics knowledge
already held visual presentation techniques knowledge

6 supplementary skills, helpful but not required

Skill to acquire Tier

optional, not held execute ICT audits optional
optional, not held build business relationships optional
optional, not held business processes optional
optional, not held manage schedule of tasks optional
optional, not held perform data analysis optional
optional, not held train employees optional

70 held skills the data quality specialist role does not ask for

Skill Type

not needed by the target role Hadoop knowledge
not needed by the target role apply blended learning skill
not needed by the target role apply for research funding skill
not needed by the target role apply research ethics and scientific integrity principles in research activities skill
not needed by the target role build recommender systems skill
not needed by the target role business analytics knowledge
not needed by the target role business intelligence knowledge
not needed by the target role collect ICT data skill
not needed by the target role communicate with a non-scientific audience skill
not needed by the target role computational biology knowledge
not needed by the target role computer simulation knowledge
not needed by the target role conduct research across disciplines skill
not needed by the target role create data models skill
not needed by the target role data mining knowledge
not needed by the target role data models knowledge
not needed by the target role data science knowledge
not needed by the target role data visualisation software knowledge
not needed by the target role deliver visual presentation of data skill
not needed by the target role demonstrate disciplinary expertise skill
not needed by the target role develop data processing applications skill
not needed by the target role develop professional network with researchers and scientists skill
not needed by the target role digital curation knowledge
not needed by the target role disseminate results to the scientific community skill
not needed by the target role draft scientific or academic papers and technical documentation skill
not needed by the target role empirical analysis knowledge
not needed by the target role evaluate research activities skill
not needed by the target role image recognition knowledge
not needed by the target role increase the impact of science on policy and society skill
not needed by the target role information categorisation knowledge
not needed by the target role information extraction knowledge
not needed by the target role integrate ICT data skill
not needed by the target role integrate gender dimension in research skill
not needed by the target role interact professionally in research and professional environments skill
not needed by the target role interpret current data skill
not needed by the target role make data-driven decisions skill
not needed by the target role manage ICT data architecture skill
not needed by the target role manage ICT data classification skill
not needed by the target role manage data collection systems skill
not needed by the target role manage findable accessible interoperable and reusable data skill
not needed by the target role manage intellectual property rights skill

30 further entries not listed here

3 gaps that are knowledge rather than practice

Knowledge gaps usually close through study. Practical skill gaps usually need something you can point at.

Skill to acquire Tier

required, not held information structure required
required, not held database required
optional, not held business processes optional

Index

Other moves recorded from data scientist

Full index of moves from data scientist

Note

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.