Data analyst to data scientist
A data analyst already meets 47% of what the data scientist role asks for. The move turns on 36 required skills not yet in the profile.
68.5/ 100
1 How much of what a data scientist is expected to do is already part of a data analyst’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 27 of its required skills as things a data analyst already does. You would walk in with these on day one.
- collect ICT data
- data engineering
- data ethics
- data mining
- data models
- data science
What stands in the way is 36 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 51 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 analyst background worth leading with.
- already held collect ICT data information skills
- already held Hadoop information and communication technologies (icts)
- already held handle data samples information skills
- already held data ethics arts and humanities
- already held normalise data working with computers
- already held statistical modeling techniques natural sciences, mathematics and statistics
All 51 carried skills, including the 27 the data scientist role treats as required.
Table 3 · What you may need to learn
The 46 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 design database scheme required
- still to learn manage intellectual property rights required
- still to learn communicate with a non-scientific audience 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 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
- 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 conduct research across disciplines required
- still to learn demonstrate disciplinary expertise required
- still to learn integrate gender dimension in research 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
- 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
- optional, not held manage ICT data classification optional
- still to learn empirical analysis required
- still to learn scientific literature required
- still to learn manage personal professional development required
- still to learn perform project management required
- still to learn mathematical modelling required
- optional, not held computational biology optional
3 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 design database scheme practise this
Each entry opens a course search for that skill. Career Overlap earns nothing from these links.
Appendix · The rest of the record
All 51 skills that carry over
- already held collect ICT data skill
- already held data engineering knowledge
- already held data ethics knowledge
- already held data mining knowledge
- already held data models knowledge
- already held data science knowledge
- already held data visualisation software knowledge
- already held deliver visual presentation of data 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 information categorisation knowledge
- already held information extraction knowledge
- already held interpret current data skill
- already held manage data collection systems skill
- already held normalise data skill
- already held online analytical processing knowledge
- 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 statistical modeling techniques knowledge
- already held statistics knowledge
- already held use data processing techniques skill
- already held use databases skill
- already held visual presentation techniques knowledge
- already held Hadoop 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 business analytics knowledge
- already held business intelligence knowledge
- already held create data models skill
- already held data quality assessment knowledge
- already held define data quality criteria skill
- already held healthcare analytics knowledge
- already held image recognition knowledge
- already held integrate ICT data skill
- already held make data-driven decisions skill
- already held manage data skill
- already held marketing analytics knowledge
- already held multidisciplinary research knowledge
- already held perform data mining skill
- already held research design knowledge
- already held social network analysis knowledge
- already held unstructured data knowledge
- already held use spreadsheets software skill
The 3 learning areas not shown above
- still to learn quantitative analysis required
- optional, not held digital curation optional
- optional, not held computer simulation optional
- optional, not held scientific computing optional
- optional, not held state estimation optional
10 supplementary skills, helpful but not required
- optional, not held digital curation optional
- optional, not held computational biology optional
- optional, not held design database in the cloud optional
- optional, not held manage ICT data architecture optional
- optional, not held manage ICT data classification optional
- optional, not held apply blended learning optional
- optional, not held computer simulation optional
- optional, not held scientific computing optional
- optional, not held state estimation optional
- optional, not held teach in academic or vocational contexts optional
16 held skills the data scientist role does not ask for
- not needed by the target role analyse big data skill
- not needed by the target role apply statistical analysis techniques skill
- not needed by the target role cloud technologies knowledge
- not needed by the target role data storage knowledge
- not needed by the target role database knowledge
- not needed by the target role digital data processing skill
- not needed by the target role documentation types knowledge
- not needed by the target role game theory knowledge
- not needed by the target role gather data for forensic purposes skill
- not needed by the target role information architecture knowledge
- not needed by the target role information confidentiality knowledge
- not needed by the target role information structure knowledge
- not needed by the target role manage cloud data and storage skill
- not needed by the target role manage quantitative data skill
- not needed by the target role store digital data and systems skill
- not needed by the target role web analytics knowledge
9 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 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 computational biology optional
- optional, not held computer simulation optional
- optional, not held scientific computing optional
- optional, not held state estimation optional
Other moves recorded from data analyst
- data quality specialist 67% covered · moderate
- data entry clerk 63% covered · moderate
- chief data officer 56% covered · moderate
- ICT information and knowledge manager 50% covered · substantial
- data entry supervisor 49% covered · substantial
- data engineer 47% covered · substantial
- computer vision engineer 43% covered · substantial
- big data archive librarian 32% covered · career change
Common questions about this move
Can a data analyst become a data scientist?
Yes, and it is a substantial move. A data analyst already covers 47% of what a data scientist is expected to do: 51 skills carry over, 27 of which the data scientist role treats as required.
What skills does a data analyst need to become a data scientist?
36 required skills are missing, plus 10 optional ones. They fall into 9 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 analyst to data scientist?
Its learning distance is 68.5 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.