Danilo is a student pursuing a Master's degree in Computer Engineering, specializing in Data Engineering, at the University of Naples "Federico II."
Danilo has been passionate about mathematics and computers since childhood. His passion flourished during his Bachelor's degree in Computer Engineering and continues to grow through work, research, and personal study.
Over the years, Danilo has successfully passed several advanced math and computer science related exams and has deepened his expertise in Data thanks to his specialization, which he expects to complete in 2026.
The following coursework highlights my core expertise in mathematics, data engineering, and computer science.
- Calculus I
- Calculus II
- Linear Algebra and Euclidean Geometry
- Mathematical Methods for Engineering (complex analysis, Fourier and Laplace transforms, Generalized functions, ODE and PDE)
- Dynamic Systems and Automatic Control (Modeling of physical systems, Transfer functions, Stability analysis (BIBO, Lyapunov) with time and frequency response, Feedback control, State-space representation, controllability and observability)
- Signals & Digital Signal Transmission (Continuous and discrete signals, Probability, Fourier series and sampling, Digital signal representation, modulation techniques, Nyquist/Shannon criteria, channel capacity, noise analysis, spectral analysis and signal to noise optimization).
Information Systems and Business Intelligence
- Information system architecture: ERP (Odoo), CRM, SOA-based systems
- Business process modeling (BPM, BPR), BONITA workflow tool
- Lifecycle of enterprise systems: planning, feasibility, maintenance
- Decision support systems and Business Intelligence tools
- Applied case studies: logistics, public admin, transport, GIS
Machine Learning
- Core algorithms: decision trees (C4.5), SVM, neural networks (MLP, CNN, RNN), clustering
- Deep learning: autoencoders, GANs, regularization, training strategies
- Model evaluation: CV, LOO, ROC, cost-sensitive classification
- Feature engineering: PCA, discretization, sampling, attribute selection
- Ensemble learning (Bagging, Boosting, Stacking), semi-supervised & multi-instance learning
- Bayesian networks, temporal models, and probabilistic reasoning
Big Data Engineering
- Hadoop ecosystem: HDFS, YARN, Hive, Pig, Spark, GraphX, MLlib
- NoSQL systems: key-value, document, column-family, graph DBs; CAP theorem, BASE vs ACID
- Big Data lifecycle: data prep, modeling, visualization
- Real-time data pipelines: Kafka, Flume, Lambda/Kappa architectures, Spark Streaming
- Emerging applications: cybersecurity, smart cities, social network analysis
- Big Data in the cloud: AWS, Azure services for scalable analytics
- Email: [email protected]
- Linkedin profile: LinkedIn