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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 379 / 782
This learning path equips data scientists with the skills to leverage cloud platforms for scalable data science workloads. It covers cloud fundamentals, storage, compute, databases, serverless options, cost management, and security, with a focus on practical application.
This learning path guides data scientists through essential Git and version control skills for collaborative projects. Starting with core concepts, it progresses through branching, merging, and collaboration workflows, including practical conflict resolution and data versioning considerations.
This advanced graduate-level path equips data scientists with the knowledge and skills to analyze graph-structured data using network science and graph algorithms. Starting with foundational graph theory and Python tools, it progresses through network metrics, centrality, community detection, graph embeddings, graph neural networks, and knowledge graphs, culminating in practical application with NetworkX and Neo4j. The path emphasizes hands-on learning and real-world data analysis.
This learning path equips data scientists with the knowledge and skills to design rigorous experiments, from foundational statistical concepts to advanced experimental designs and analysis. It covers hypothesis testing, randomization, sample size and power analysis, common pitfalls, quasi-experimental designs, and multi-armed bandits, enabling learners to make valid causal inferences and evaluate interventions effectively.
This advanced graduate-level path equips data scientists with the skills to apply machine learning and statistical methods to healthcare data, including EHR, medical imaging, and pharmaceutical applications, while navigating regulatory constraints such as HIPAA. It covers clinical prediction models, survival analysis, and outcome analysis, with a strong emphasis on ethical and compliant data handling.
This learning path equips data scientists with the core data science techniques applied to marketing analytics, including customer segmentation, RFM analysis, marketing mix modeling, customer lifetime value, A/B testing, attribution modeling, and churn prediction. It bridges the gap between data science fundamentals and practical marketing applications, enabling learners to derive actionable customer insights and optimize marketing strategies.
A comprehensive learning path for data scientists aiming to apply their skills to finance. It covers essential financial concepts, time series analysis, risk modeling, portfolio optimization, algorithmic trading, and credit/fraud analytics, with a strong emphasis on Python implementation.
This learning path equips data scientists with the knowledge and skills to design, build, and automate reproducible data processing pipelines. It covers essential data engineering concepts, workflow orchestration with Airflow and Prefect, data versioning with DVC, pipeline testing, monitoring, and dependency management. The path emphasizes practical application and best practices for creating robust, maintainable data workflows.
This learning path equips data scientists with a solid understanding of database architectures and data storage solutions. Starting with SQL basics, it progresses through relational database design, NoSQL systems, and modern data platforms, enabling informed decisions for data science workflows.
This path equips data scientists with the knowledge and skills to analyze spatial data and geographic information. It covers foundational concepts in GIS, coordinate systems, and data structures, then progresses to practical techniques such as geocoding, spatial joins, and spatial statistics. The path culminates in advanced applications like clustering and visualization using Python mapping libraries.