Data scientist
A data scientist is a professional who applies statistical, computational, analytical, and domain methods to analyze data, derive insights, and build data-driven models or systems.
Delivering Machine Learning Projects: From design to deployment
A data scientist is a specialist who conditions data, creates models, and tests and evaluates their effectiveness.
- How it is used
- Frames data scientists as specialists addressing data, modelling, and domain-alignment challenges in ML project teams.
- How deep it goes
- Defines core responsibilities and team boundaries without detailing modelling techniques, testing procedures, or implementation steps.
- What you can do after
- Understand how data-science responsibilities fit alongside data engineering, ML engineering, and business translation.
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Machine Learning System Design
A professional role involving analysis of data, development of data-driven models or insights, and communication of findings to support decisions.
- How it is used
- Frames ML system design as a responsibility commonly carried by data scientists despite the role's variable scope.
- How deep it goes
- Defines the role through contrasting responsibilities and examples, but stops short of organizing a complete responsibility framework.
- Examples
- Uses pivot tables, SQL, dashboards, A/B tests, recommender systems, chatbots, computer vision, clusters, and management questions.
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Designing Deep Learning Systems: A guide for software engineers
A data scientist is a practitioner who formulates business problems as machine learning problems and develops, evaluates, and operationalizes data-driven solutions.
- How it is used
- Translates product needs into machine learning solutions and carries them from data exploration through production monitoring.
- How deep it goes
- Details responsibilities from preprocessing and prototyping to workflow automation, deployment, monitoring, and model-degradation troubleshooting.
- Examples
- Uses intent classification, labeled datasets, data-ingestion APIs, dataset-fetching APIs, PyTorch, and TensorFlow as practical artifacts.
- What you can do after
- Builds and operates machine learning solutions from business formulation through production deployment and performance troubleshooting.
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The Art of AI Product Development
A data scientist is a practitioner who applies statistics, mathematics, programming, and machine learning to analyze data and develop data-driven models.
- How it is used
- Supports user needs by collecting data and training machine learning models within an AI product team.
- How deep it goes
- Defines responsibilities and neighboring roles but stops short of detailing statistical methods, algorithms, or implementation procedures.
- Examples
- Uses a lean AI team in which one data scientist collects data and trains models for user needs.
- What you can do after
- Evaluate how data collection and model training fit within a broader AI development team.
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Related concepts
- conversational aiuses · Conversational AI is artificial intelligence that enables software systems to interact with users through natural-language dialogue.
- cloud engineerused by · A cloud engineer specializes in configuring and managing cloud infrastructure and cloud-managed services.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.