Qualifications of data scientists

To satisfy the above position requirements, data scientist candidates need to have certain

qualifications in addition to the analytic skills that are the foundation of this role.

These qualifications and abilities include:

  • Thinking, mindset and ability to think analytically, creatively, critically and inquisitively;
  • Methodologies and knowledge of complex systems and approaches for conducting both top-down and bottom-up problem-solving;
  • Master’s or PhD degree in computer science, statistics, mathematics, analytics, data science, informatics, engineering, physics, operations research, pattern recognition, artificial intelligence, visualization, information retrieval or related fields;
  • A deep understanding of common statistics, data mining and machine learning methodologies and models;
  • Ability to implement, maintain, and troubleshoot big data infrastructure, such as cloud computing, high performance computing infrastructure, distributed processing paradigms, stream processing and databases;
  • Knowledge of human-computer interactions, visualization and knowledge representation and management;
  • Background in software engineering (including systems design and analysis), quality assurance;
  • Experience working with large datasets, and mixed data types and sources in a networked and distributed environment;
  • Experience in data extraction and processing, feature understanding and relation analysis;
  • Active interest and knowledge in multi-disciplinary and trans-disciplinary studies and methods in scientific, technical, and social and life sciences;
  • Substantial experience with state-of-the-art analytics-oriented scripting, data structures, programming languages, and development platforms in a Linux, cloud or distributed environment;
  • Theoretical background and domain knowledge for the evaluation of the technical and business merits of analytic findings;
  • Excellent written and verbal communication [Matsudaira 2015] and organizational skills, ability to write and edit analytical materials and reports for different audiences, and capacity to transform analytical concepts and outcomes into business-friendly interpretations; ability to communicate actionable insights to non-technical audiences, and experience in data-driven decision making.

 

Note: Excerpted from “Longbing Cao. Data Science: A Comprehensive Overview

 

References:

1. Kate Matsudaira. 2015. The science of managing data science. Commun. ACM 58, 6 (2015), 44–47

Email:contacts@datasciences.org