Table 1Transition record

computer vision engineertodata scientist

A computer vision engineer already meets 26% of what the data scientist role asks for. The move turns on 46 required skills not yet in the profile.

From computer vision engineer

26%

Overlap1

To data scientist

24 Skills carried over

46 Required, not held

83.4 Difficulty2

adjacent 0–30

moderate 30–55

substantial 55–75

career change 75–100

this move, 83.4

This move: 83.4 of 100 · career change

1 Share of the data scientist 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 24 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 computer vision engineer background worth leading with.

Skill the target role also needs Area

already held develop data processing applications working with computers
already held handle data samples information skills
already held normalise data working with computers
already held scientific computing information and communication technologies (icts)
already held implement data quality processes working with computers
already held perform data cleansing working with computers

All 24 carried skills, including the 17 the data scientist role treats as required.

Table 3

Table 3 · What you would need to learn

The 73 missing skills fall into 11 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 communication, collaboration and creativity 15 required, 4 supplementary

required, not held design database scheme required
required, not held manage intellectual property rights required
required, not held communicate with a non-scientific audience required
required, not held develop professional network with researchers and scientists required
required, not held disseminate results to the scientific community required
required, not held draft scientific or academic papers and technical documentation required
required, not held evaluate research activities required
required, not held increase the impact of science on policy and society required
required, not held interact professionally in research and professional environments required
required, not held mentor individuals required
required, not held promote the transfer of knowledge required
required, not held publish academic research required
required, not held speak different languages required
required, not held think abstractly required
required, not held write scientific publications required

optional, not held design database in the cloud optional
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

02 information skills 12 required, 2 supplementary

required, not held apply for research funding required
required, not held apply research ethics and scientific integrity principles in research activities required
required, not held collect ICT data required
required, not held conduct research across disciplines required
required, not held demonstrate disciplinary expertise required
required, not held integrate gender dimension in research required
required, not held manage findable accessible interoperable and reusable data required
required, not held manage research data required
required, not held perform scientific research required
required, not held promote open innovation in research required
required, not held promote the participation of citizens in scientific and research activities required
required, not held synthesise information required

optional, not held manage data optional
optional, not held make data-driven decisions optional

03 information and communication technologies (icts) 5 required, 8 supplementary

required, not held data mining required
required, not held data models required
required, not held information extraction required
required, not held online analytical processing required
required, not held data visualisation software required

optional, not held Hadoop optional
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 unstructured data optional

04 working with computers 5 required, 3 supplementary

required, not held build recommender systems required
required, not held operate open source software required
required, not held manage open publications required
required, not held use data processing techniques required
required, not held use databases required

optional, not held integrate ICT data optional
optional, not held manage ICT data classification optional
optional, not held use spreadsheets software optional

05 generic programmes and qualifications 2 required, 2 supplementary

required, not held empirical analysis required
required, not held scientific literature required

optional, not held multidisciplinary research optional
optional, not held research design optional

06 social sciences, journalism and information 2 required, 2 supplementary

required, not held information categorisation required
required, not held quantitative analysis required

optional, not held digital curation optional
optional, not held social network analysis optional

5 further areas in the appendix

Table 4

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.

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

Appendix

Appendix · The rest of the record

All 24 skills that carry over

Skill Type

already held data engineering knowledge
already held data science knowledge
already held deliver visual presentation of data skill
already held develop data processing applications 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 interpret current data skill
already held manage data collection systems skill
already held mathematical modelling knowledge
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 statistics knowledge
already held computer simulation knowledge
already held create data models skill
already held define data quality criteria skill
already held image recognition knowledge
already held perform data mining skill
already held scientific computing knowledge
already held state estimation knowledge

The 5 learning areas not shown above

Skill to acquire Tier

07 arts and humanities 2 required

required, not held visual presentation techniques required
required, not held data ethics required

08 management skills 2 required

required, not held manage personal professional development required
required, not held perform project management required

09 natural sciences, mathematics and statistics 1 required, 2 supplementary

required, not held statistical modeling techniques required

optional, not held business analytics optional
optional, not held computational biology optional

10 business, administration and law 3 supplementary

optional, not held data quality assessment optional
optional, not held business intelligence optional
optional, not held marketing analytics optional

11 health and welfare 1 supplementary

optional, not held healthcare analytics optional

27 supplementary skills, helpful but not required

Skill to acquire Tier

optional, not held digital curation optional
optional, not held Hadoop optional
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 business analytics optional
optional, not held computational biology optional
optional, not held data quality assessment optional
optional, not held design database in the cloud optional
optional, not held healthcare analytics 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 manage data optional
optional, not held unstructured data optional
optional, not held apply blended learning optional
optional, not held business intelligence 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 social network analysis optional
optional, not held teach in academic or vocational contexts optional
optional, not held use spreadsheets software optional

28 held skills the data scientist 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 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 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 design user interface skill
not needed by the target role develop computer vision system 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 integrated development environment software knowledge
not needed by the target role machine learning knowledge
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 signal processing 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

30 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 data mining required
required, not held data models required
required, not held information categorisation required
required, not held information extraction required
required, not held online analytical processing required
required, not held statistical modeling techniques required
required, not held visual presentation techniques required
required, not held data ethics required
required, not held data visualisation software required
required, not held empirical analysis required
required, not held quantitative analysis required
required, not held scientific literature required
optional, not held digital curation optional
optional, not held Hadoop optional
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 business analytics optional
optional, not held computational biology optional
optional, not held data quality assessment optional
optional, not held healthcare analytics optional
optional, not held unstructured data optional
optional, not held business intelligence optional
optional, not held marketing analytics optional
optional, not held multidisciplinary research optional
optional, not held research design optional
optional, not held social network analysis 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.