MrMimic/data-scientist-roadmap

Features

• Educational Curriculum Repositories - Provides a comprehensive repository of tutorials and structured collections of learning materials for data science.
• Data Science Fundamentals - Provides a structured curriculum covering data science fundamentals, statistics, and professional analytical workflows.
• Machine Learning Knowledge Bases - Acts as a knowledge base for acquiring the theoretical and practical skills needed for machine learning model deployment.
• Curriculum Mappings - Organizes educational resources into structured sequences of technical topics to guide progressive mastery.
• Curated Learning Paths - Organizes educational resources into sequential dependency graphs to guide learners from basics to advanced data science topics.
• Learning Path Organization - Structures educational content into a linear sequence where prerequisites must be completed before advancing.
• Machine Learning Study Paths - Implements a structured sequence of learning activities to build proficiency in machine learning and predictive modeling.
• AI & Machine Learning Education - Provides educational content on neural network theory and machine learning algorithms to build predictive models.
• AI and Data Science Education - Delivers pedagogical resources for mastering statistics, programming, and machine learning fundamentals.
• Data Engineering Pipelines - Includes educational tracks on collecting, cleaning, and managing data pipelines within an analytical workflow.
• Data Science Tutorials - Provides a modular structure of tutorials and lessons specifically for data science domains.
• Data Engineering Training - Provides training programs focused on building pipelines and processing large-scale datasets for data engineering.
• Career Guidance - Offers guidance on the specific skill requirements and milestones needed to transition into professional data science roles.
• Technical Career Roadmaps - Provides a structured guide for skill acquisition and professional milestones required for technical roles in data science.
• Technical Skill Curations - Provides curated learning materials and structured paths for acquiring professional data science and technical skills.
• Knowledge Taxonomies - Implements a hierarchical taxonomy to categorize technical subjects and facilitate systematic navigation of the knowledge domain.
• Instructional Modules - Breaks down complex data science domains into discrete, manageable lessons that can be consumed independently.
• AI and Data Science - Tutorials and notes for data science learning.
• Data Science Learning - Tutorials for the data science roadmap.

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