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Guided learning journeys that build knowledge step by step.
category · Learning · slug · learning · 7812 Paths
7812 Paths · page 380 / 782
This learning path equips data scientists with the skills to translate complex data insights into compelling narratives for non-technical audiences. It covers audience analysis, narrative structuring, visual storytelling, dashboard design, and effective presentation techniques, culminating in a capstone project and peer review.
This learning path guides data scientists through creating a professional portfolio that demonstrates their skills to employers. It covers project selection, GitHub portfolio building, Kaggle competitions, code quality, documentation, README writing, and presentation skills, each building on the previous.
This advanced graduate-level path guides data scientists from foundational linear algebra and Python through core deep learning concepts to specialized architectures including CNNs, RNNs/LSTMs, autoencoders, and GANs. It emphasizes hands-on implementation with PyTorch/TensorFlow and culminates in practical applications for data science.
This path equips data scientists with essential NLP techniques for analyzing textual data, from preprocessing to advanced transformer-based models. It covers TF-IDF, word embeddings, sentiment analysis, topic modeling, NER, and BERT, with a focus on practical application in data science workflows.
This learning path guides data scientists from Python basics through the principles of visual perception and chart selection, to hands-on practice with matplotlib, Seaborn, and Plotly, culminating in interactive dashboards and storytelling with data. Each node builds on the previous, ensuring a solid foundation for creating effective visualizations.
A structured learning path for aspiring data scientists to master SQL querying, joining, and aggregation. Starting with relational database fundamentals, it progresses through core querying skills, joins, subqueries, CTEs, window functions, and query optimization, with a focus on data analysis applications.
This path prepares job-seeking data scientists for technical interviews by systematically covering SQL, Python, probability, statistics, machine learning, case studies, behavioral questions, and portfolio review. It builds from foundational prerequisites to advanced application, ensuring a comprehensive and structured preparation.
This learning path equips data scientists in non-profit and public sectors with the knowledge and skills to apply data science methods to social and environmental challenges. It covers foundational data science concepts, public data sources, impact measurement, policy analysis, humanitarian applications, environmental data analysis, and ethical considerations, with a focus on practical application and responsible practice.
A graduate-level learning path covering the mathematical foundations and practical algorithms of optimization used in data science. It progresses from core calculus and linear algebra through convex analysis, unconstrained and constrained optimization, to stochastic and hyperparameter optimization methods.
This graduate-level path equips data scientists with a comprehensive toolkit for detecting anomalies and rare events, spanning statistical methods, machine learning models, and specialized techniques for time-series and contextual anomalies. It emphasizes rigorous evaluation under imbalanced data conditions, essential for fraud detection and security applications.