Computer vision engineer to data analyst
A computer vision engineer already meets 36% of what the data analyst role asks for. The move turns on 21 required skills not yet in the profile.
74.3/ 100
1 How much of what a data analyst is expected to do is already part of a computer vision engineer’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 analyst role is recorded as needing, required and optional together. What these numbers mean.
Why this move works
A data analyst role treats 15 of its required skills as things a computer vision engineer already does. You would walk in with these on day one.
- apply statistical analysis techniques
- data engineering
- data science
- define data quality criteria
- establish data processes
- execute analytical mathematical calculations
What stands in the way is 21 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 20 skills that carry over, these are the ones fewest other occupations ask for. A data analyst 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.
- 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 image recognition information and communication technologies (icts)
- already held establish data processes working with computers
All 20 carried skills, including the 15 the data analyst role treats as required.
Table 3 · What you may need to learn
The 47 missing skills fall into 9 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 data mining required
- still to learn data models required
- still to learn information confidentiality required
- still to learn information extraction required
- still to learn information structure required
- still to learn unstructured data required
- still to learn 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 cloud technologies optional
- optional, not held data storage optional
- optional, not held information architecture optional
- optional, not held online analytical processing optional
- optional, not held database optional
- optional, not held web analytics optional
- still to learn digital data processing required
- still to learn integrate ICT data required
- still to learn use data processing techniques required
- still to learn use databases required
- optional, not held manage quantitative data optional
- optional, not held store digital data and systems optional
- optional, not held use spreadsheets software optional
- still to learn manage data required
- still to learn analyse big data required
- still to learn collect ICT data required
- optional, not held gather data for forensic purposes optional
- optional, not held manage cloud data and storage optional
- optional, not held make data-driven decisions optional
- still to learn data quality assessment required
- still to learn business intelligence required
- optional, not held marketing analytics optional
- still to learn documentation types required
- still to learn information categorisation required
- optional, not held social network analysis optional
- still to learn visual presentation techniques required
- still to learn data ethics required
3 further areas in the appendix
Table 4 · Where to start
The 3 entries a data analyst 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 · The rest of the record
All 20 skills that carry over
- already held apply statistical analysis techniques skill
- already held data engineering knowledge
- already held data science knowledge
- already held define data quality criteria 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 normalise data skill
- already held perform data cleansing skill
- already held perform data mining skill
- already held query languages knowledge
- already held resource description framework query language knowledge
- already held statistics knowledge
- already held create data models skill
- already held deliver visual presentation of data skill
- already held image recognition knowledge
- already held manage data collection systems skill
- already held report analysis results skill
The 3 learning areas not shown above
- still to learn business analytics required
- optional, not held statistical modeling techniques optional
- optional, not held game theory optional
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- optional, not held healthcare analytics optional
26 supplementary skills, helpful but not 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 cloud technologies optional
- optional, not held data storage optional
- optional, not held gather data for forensic purposes optional
- optional, not held healthcare analytics optional
- optional, not held information architecture optional
- optional, not held manage cloud data and storage optional
- optional, not held online analytical processing optional
- optional, not held statistical modeling techniques optional
- optional, not held database optional
- optional, not held game theory optional
- optional, not held make data-driven decisions optional
- optional, not held manage quantitative data 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 store digital data and systems optional
- optional, not held use spreadsheets software optional
- optional, not held web analytics optional
32 held skills the data analyst role does not ask for
- not needed by the target role Python (computer programming) knowledge
- 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 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 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 integrated development environment software knowledge
- not needed by the target role machine learning knowledge
- not needed by the target role mathematical modelling 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 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
34 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 business analytics required
- still to learn data mining required
- still to learn data models required
- still to learn data quality assessment required
- still to learn documentation types required
- still to learn information categorisation required
- still to learn information confidentiality required
- still to learn information extraction required
- still to learn information structure required
- still to learn unstructured data required
- still to learn visual presentation techniques required
- still to learn business intelligence required
- still to learn data ethics required
- still to learn 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 cloud technologies optional
- optional, not held data storage optional
- optional, not held healthcare analytics optional
- optional, not held information architecture optional
- optional, not held online analytical processing optional
- optional, not held statistical modeling techniques optional
- optional, not held database optional
- optional, not held game theory 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 web analytics optional
Common questions about this move
Can a computer vision engineer become a data analyst?
Yes, and it is a substantial move. A computer vision engineer already covers 36% of what a data analyst is expected to do: 20 skills carry over, 15 of which the data analyst role treats as required.
What skills does a computer vision engineer need to become a data analyst?
21 required skills are missing, plus 26 optional ones. They fall into 9 areas of the ESCO skill hierarchy, led by information and communication technologies (icts). The single most important is business analytics.
How hard is the move from computer vision engineer to data analyst?
Its learning distance is 74.3 out of 100, which is the substantial 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.