Our Data Science curriculum is designed to develop analytical fluency and applied machine learning skills across every level of experience. From introductory Python and SQL to advanced neural networks and time series modeling, building full-stack data science capability grounded in real-world business challenges.
Use cloud platforms and tools like PySpark to process large-scale datasets, and create interactive dashboards to communicate insights. Work with structured and semi-structured data using industry-standard Python and SQL.
Learn foundational and advanced statistical techniques including probability, confidence intervals, and hypothesis testing to extract insights and support data-driven decision-making.
Train and evaluate a range of supervised and unsupervised models—logistic regression, decision trees, SVMs, k-means clustering, and recommender systems—using Scikit-learn and other libraries.
Advance into deep learning with Keras and TensorFlow, building models for natural language, time series, and image data. Apply transformers, RNNs, and CNNs to modern AI problems.
Curriculum modules designed for integration into bachelor's and master's degree programs. Delivered in partnership with accredited institutions to bring industry-relevant tech education into traditional academic pathways.
Modular, stackable programs that build critical skills in 1–6 weeks. Ideal for professional education or enterprise partners seeking targeted upskilling or reskilling solutions.
Project-based, outcome-driven programs designed for adult learners shifting into tech careers. Available full-time or part-time through universities or employers, supporting everything from public enrollment to internal mobility.
A new model blending hands-on apprenticeships with formal education. Students work while they learn, graduate with experience, and build real-world confidence—designed for workforce-aligned universities and employers alike.
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