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Guided learning journeys that build knowledge step by step.
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This learning path introduces beginners to the core concepts and lifecycle of data analytics, emphasizing its role in supporting decision-making. Learners will explore data types, descriptive analytics, basic statistics, data quality issues, and common analytics tools, building a solid foundation for further study.
This learning path guides data scientists in charting a strategic career path and developing professional skills. It covers self-assessment, skill gap analysis, certification and continuing education options, networking, professional organizations, resume building, and mentorship.
This advanced learning path guides graduate students through the entire process of conducting original data science research, from identifying a meaningful problem and reviewing literature to developing novel methodologies, running rigorous experiments, and producing a publishable manuscript. It emphasizes the integration of statistical rigor, computational practice, and academic communication skills.
This learning path equips data science leads with the knowledge and skills to implement ethical data practices and robust governance frameworks. It covers foundational concepts in data ethics and governance, regulatory compliance, data quality management, internal policy development, and audit trails, culminating in a capstone project to apply these principles.
This path equips data scientists with the skills to apply machine learning and data analytics to manufacturing challenges, including predictive maintenance, quality prediction, and process optimization. It covers essential data science foundations, sensor data analysis, anomaly detection, and integration with IIoT and supply chain systems, culminating in practical deployment and monitoring strategies.
A professional learning path for data scientists entering the sports industry. It covers the full data science workflow applied to sports, from data acquisition and statistical analysis to predictive modeling and strategic decision-making, with a focus on player tracking data, performance analytics, and team management.
This path equips data scientists with the principles and practices needed to write production-quality Python code. It covers code structure, documentation, testing, error handling, performance optimization, and code review, culminating in a capstone project that applies all concepts to a realistic data science workflow.
This learning path equips journalists and data scientists with the skills to investigate and report on important stories using data science. It covers data acquisition via FOIA, cleaning, statistical analysis, visualization, and interactive publishing, emphasizing ethical and transparent reporting.
This learning path equips data scientists with the knowledge to apply Automated Machine Learning (AutoML) frameworks effectively. It covers the core components of AutoML—hyperparameter optimization, neural architecture search, and automated feature engineering—and provides hands-on experience with popular frameworks like Auto-sklearn, H2O, and TPOT. By the end, learners will be able to integrate AutoML into their workflows to accelerate model development while understanding its limitations and best practices.
This learning path equips data scientists with the knowledge to apply data science techniques across supply chain functions, including demand forecasting, inventory optimization, route optimization, supplier analytics, logistics network design, risk management, and real-time tracking. It covers essential prerequisites in data science and supply chain concepts, ensuring a solid foundation for advanced applications.