Table 1Transition record

computer vision engineertodata quality specialist

A computer vision engineer already meets 37% of what the data quality specialist role asks for. The move turns on 11 required skills not yet in the profile.

From computer vision engineer

37%

Overlap1

To data quality specialist

12 Skills carried over

11 Required, not held

66.1 Difficulty2

adjacent 0–30

moderate 30–55

substantial 55–75

career change 75–100

this move, 66.1

This move: 66.1 of 100 · substantial

1 Share of the data quality specialist role’s weighted skill requirement already met by the computer vision engineer 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 12 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 computer vision engineer background worth leading with.

Skill the target role also needs Area

already held handle data samples information skills
already held normalise data working with computers
already held implement data quality processes working with computers
already held perform data cleansing working with computers
already held establish data processes working with computers
already held data engineering information and communication technologies (icts)

All 12 carried skills, including the 9 the data quality specialist role treats as required.

Table 3

Table 3 · What you would need to learn

The 28 missing skills fall into 8 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 3 required, 1 supplementary

required, not held manage data required
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, 6 supplementary

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

optional, not held LDAP optional
optional, not held LINQ optional
optional, not held MDX optional
optional, not held N1QL optional
optional, not held SPARQL optional
optional, not held XQuery optional

03 communication, collaboration and creativity 2 required, 3 supplementary

required, not held design database scheme required
required, not held address problems critically required

optional, not held design database in the cloud optional
optional, not held build business relationships optional
optional, not held train employees optional

04 working with computers 2 required, 1 supplementary

required, not held utilise regular expressions required
required, not held use data processing techniques required

optional, not held perform data analysis optional

05 management skills 1 required, 2 supplementary

required, not held manage standards for data exchange required

optional, not held manage schedule of tasks optional
optional, not held perform project management optional

06 arts and humanities 1 required, 1 supplementary

required, not held data ethics required

optional, not held visual presentation techniques optional

2 further areas in the appendix

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 12 skills that carry over

Skill Type

already held define data quality criteria skill
already held establish data processes 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 query languages knowledge
already held report analysis results skill
already held resource description framework query language knowledge
already held data engineering knowledge
already held execute analytical mathematical calculations skill
already held statistics knowledge

The 2 learning areas not shown above

Skill to acquire Tier

07 business, administration and law 2 supplementary

optional, not held data quality assessment optional
optional, not held business processes optional

08 health and welfare 1 supplementary

optional, not held healthcare analytics optional

17 supplementary skills, helpful but not required

Skill to acquire Tier

optional, not held LDAP optional
optional, not held LINQ optional
optional, not held MDX optional
optional, not held N1QL optional
optional, not held SPARQL optional
optional, not held XQuery optional
optional, not held data quality assessment optional
optional, not held design database in the cloud optional
optional, not held execute ICT audits optional
optional, not held healthcare analytics optional
optional, not held visual presentation techniques 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 perform project management optional
optional, not held train employees optional

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

Skill Type

not needed by the target role Python (computer programming) knowledge
not needed by the target role apply statistical analysis techniques skill
not needed by the target role cognitive computing knowledge
not needed by the target role computer graphics knowledge
not needed by the target role computer programming knowledge
not needed by the target role computer simulation knowledge
not needed by the target role conduct literature research skill
not needed by the target role conduct qualitative research skill
not needed by the target role conduct quantitative research skill
not needed by the target role conduct scholarly research skill
not needed by the target role create data models skill
not needed by the target role data science knowledge
not needed by the target role debug software skill
not needed by the target role deep learning knowledge
not needed by the target role define technical requirements skill
not needed by the target role deliver visual presentation of data skill
not needed by the target role design user interface skill
not needed by the target role develop computer vision system skill
not needed by the target role develop data processing applications skill
not needed by the target role develop software prototype skill
not needed by the target role digital image processing knowledge
not needed by the target role digital systems knowledge
not needed by the target role digital twin technology knowledge
not needed by the target role image formation knowledge
not needed by the target role image recognition knowledge
not needed by the target role integrated development environment software knowledge
not needed by the target role interpret current data skill
not needed by the target role machine learning knowledge
not needed by the target role manage data collection systems skill
not needed by the target role mathematical modelling knowledge
not needed by the target role perform data mining skill
not needed by the target role perform dimensionality reduction skill
not needed by the target role principles of artificial intelligence knowledge
not needed by the target role quantum computing knowledge
not needed by the target role scientific computing knowledge
not needed by the target role signal processing knowledge
not needed by the target role state estimation knowledge
not needed by the target role use markup languages skill
not needed by the target role use software libraries skill
not needed by the target role utilise computer-aided software engineering tools skill

13 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 data ethics required
required, not held database required
optional, not held LDAP optional
optional, not held LINQ optional
optional, not held MDX optional
optional, not held N1QL optional
optional, not held SPARQL optional
optional, not held XQuery optional
optional, not held data quality assessment optional
optional, not held healthcare analytics optional
optional, not held visual presentation techniques optional
optional, not held business processes optional

Index

Other moves recorded from computer vision engineer

Full index of moves from computer vision engineer

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.