“A Data Scientist is a practitioner who has sufficient knowledge in the overlapping regimes of expertise in business needs, domain knowledge, analytical skills, and programming and systems engineering expertise to manage the end-to-end scientific method process through each stage in the big data lifecycle, till the delivery of an expected scientific and business value to science or industry.”
The qualified Data Scientist should be capable of working in different roles in different projects and organisations such as Data Engineer, Data Analyst or Data Architect, Data Steward, etc., and possess the necessary skills to effectively operate components of the complex data infrastructure and processing applications through all stages of the Data lifecycle till the delivery of expected scientific and business values to science and/or industry.
The Data Science Competence Framework defined the following main Data Science competence groups that must be possessed by Data Science practitioners to be able to work at different roles in the data driven organisations.
Core Data Science competences/skills groups defining profile of the Data Science related professional profiles
• Data Science Analytics (including Statistical Analysis, Machine Learning, Data Mining, Business Analytics, others)
• Data Science Engineering (including Software and Applications Engineering, Data Warehousing, Big Data Infrastructure and Tools)
• Domain Knowledge and Expertise (Subject/Scientific domain related) Additional common competence groups demanded by organisations
• Data Management and Governance (including data stewardship, curation, and preservation)
• Research Methods for research related professions and Business Process Management for business related professions
Data Science Professional or Attitude skills (Thinking and acting like Data Scientist)
General group definition: Thinking and acting like a Data Scientist
Accept/be ready for iterative development, know when to stop, comfortable with failure, accept the symmetry of outcome (both positive and negative results are valuable)
Ask the right questions
Recognise what things are important and what things are not important
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
Recognise value of data, work with raw data, exercise good data intuition
Good sense of metrics, understand importance of the results validation, never stop looking at individual examples
Be aware about power and limitations of the main machine learning and data analytics algorithms and tools
Understand that most of data analytics algorithms are statistics and probability based, so any answer or solution has some degree of probability and represent an optimal solution for a number variables and factors
Working in agile environment and coordinate with other roles and team members
Work in multi-disciplinary team, ability to communicate with the domain and subject matter experts
Embrace online learning, continuously improve your knowledge, use professional networks and communities
Storytelling: Deliver actionable result of your analysis
Attitude: Creativity, curiosity (willingness to challenge status quo), commitment in finding new knowledge and progress to completion
Ethics and responsible use of data and insight delivered, awareness of dependability (data scientist is a feedback loop in data driven companies)
Are you envisioning to become Data Scientist? take the baby steps! Learn and prove your rigor by developing skills that matter :)
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