As a Data Scientist, you will need to master following:

• build recommender systems
• data mining
• data models
• design database scheme
• develop data processing applications
• establish data processes
• information categorisation
• information extraction
• manage data collection systems
• manage intellectual property rights
• normalise data
• online analytical processing
• operate open source software
• query languages
• resource description framework query language
• visual presentation techniques

More generally, a data scientist is also versed in:

• apply for research funding
• apply research ethics and scientific integrity principles in research activities
• collect ICT data
• communicate with a non-scientific audience
• conduct research across disciplines
• deliver visual presentation of data
• demonstrate disciplinary expertise
• develop professional network with researchers and scientists
• disseminate results to the scientific community
• draft scientific or academic papers and technical documentation
• evaluate research activities
• execute analytical mathematical calculations
• handle data samples
• implement data quality processes
• increase the impact of science on policy and society
• interact professionally in research and professional environments
• interpret current data
• manage findable accessible interoperable and reusable data
• manage open publications
• manage personal professional development
• manage research data
• mentor individuals
• perform data cleansing
• perform project management
• perform scientific research
• promote inclusion in research
• promote open innovation in research
• promote the participation of citizens in scientific and research activities
• promote the transfer of knowledge
• publish academic research
• report analysis results
• speak different languages
• statistics
• synthesise information
• think abstractly
• think analytically
• use data processing techniques
• use databases
• write scientific publications

It also helps to know the following:

• LDAP
• LINQ
• MDX
• N1QL
• SPARQL
• XQuery
• create data models
• data quality assessment
• define data quality criteria
• design database in the cloud
• integrate ICT data
• manage ICT data architecture
• manage ICT data classification
• manage data
• perform data mining
• unstructured data
• apply blended learning
• business intelligence
• teach in academic or vocational contexts
• use spreadsheets software