Essential Practical Skills a Data Scientist should have
Skill 1: Understanding life cycle analysis (Data Science)
Understands the various phases of product delivery and can plan and perform analysis for them. Can contribute to decision-making throughout the lifecycle. Collaborates with user researchers, developers, and other roles throughout the lifecycle; Understands the value of analytics, how to contribute with impact, and what data sources, analytics techniques, and tools can be used at each point in the lifecycle.
What it means: Understands and can help teams apply a range of techniques to analyze data and gain insights. Is proactive and can present compelling findings that inform broader decisions. Begins to use innovative approaches to solve problems.
Skill 2: Data technology and manipulation
Works with other technologists and analysts to integrate and separate data feeds to map, produce, transform, and test new scalable data products that meet user needs. Has a demonstrated understanding of how to expose data from systems (e.g., via APIs), link data from multiple systems, and deliver streaming services. Collaborates with other technologists and analysts to understand and leverage different types of data models. Understands and can use various data engineering tools for repeatable data processing and can compare different data models. Understands how to build scalable machine learning pipelines and combine feature engineering with optimization methods to improve data product performance.
What it means: May collaborate with data engineers to map, produce, transform, and test new data feeds for data owners and consumers, selecting the most appropriate tools and technologies. May lead ad hoc data exploration in various data serialization and storage formats from across the enterprise for data consumers.
Skill 3: Applied mathematics, statistics, and scientific practices.
Understands how algorithms are designed, optimized, and applied at scale. Can select and use appropriate statistical methods for sampling, distribution evaluation, bias, and error. Understands methods for problem structuring and can judge when which way is proper. Applies scientific methods through experimental design, exploratory data analysis, and hypothesis testing to reach robust conclusions.
What it means: Understands a range of practices and can help teams apply them. Develops more profound expertise in a narrower range of specialties; Begins to apply emerging theory to practical situations.
Skill 3: Data Science Innovation
Identifies and leverages business opportunities to ensure more efficient and effective ways to use Data Science. Explores opportunities to leverage new Data Science tools and techniques to address business and organizational challenges. Demonstrates solid intellectual curiosity with an interdisciplinary approach that draws on innovations in science and industry.
What it means: Demonstrates solid intellectual curiosity and proactively explores areas of innovation in government and industry. Able to identify the business benefits of innovation within the organization.
(Content adaptet and edited from the GOV.UK — Open Government Licence v3.0)
This might be also of interest (my new book on Mindful AI):
THE AI THOUGHT BOOK: Inspirational Thoughts & Quotes on Artificial Intelligence
