Maria Clara Soares
Data Scientist | Machine Learning Engineer
Data Scientist | Machine Learning Engineer
Hello, my name is Maria Clara Soares. I am a Computer Science undergraduate with a strong focus on Data Science and Machine Learning. My academic background and practical experience have allowed me to develop solid skills in Python, SQL, and predictive modeling. I am passionate about transforming complex datasets into actionable insights and building intelligent systems that contribute to decision-making and innovation. My professional goal is to continue developing advanced technical expertise while applying data-driven solutions to create measurable impact within organizations.
Click on the cards to see the repositories on GitHub
Coming soon!
Programming Languages • Frameworks • Libraries • Tools • Techniques
Supervised & unsupervised models (MLP, SVM, KNN, Random Forest).
Techniques for missing data, outliers, encoding, and feature scaling.
Classification metrics: Accuracy, F1-Score, Confusion Matrix, ROC Curve.
General-purpose language for data analysis, ML, and automation.
High-performance data manipulation and tabular analysis.
Linear algebra, numerical computation, multidimensional arrays.
ML algorithms, preprocessing utilities, and pipelines.
2D plotting for scientific computing and visualization.
Statistical data visualization built on Matplotlib.
Deep learning framework for neural network training/deployment.
Python scripting for workflow automation and scheduling.
Extracting structured data from websites at scale.
Managing local files and batch processing workflows.
Interactive notebooks for EDA, ML experiments, and docs.
Cloud notebooks with GPU/TPU acceleration.
REST integration for external data/services consumption.
Lightweight, hierarchical data-interchange format.
Structured tabular format for datasets and exchange.
Markup language for structuring web content.
Styling, responsive layouts, and design systems.
Dynamic logic and interactivity for web apps.
Component-based frontend library for SPAs.
JavaScript runtime for backend services and APIs.
Relational DBMS for structured data management.
Lightweight embedded relational database engine.
Backend-as-a-Service for scalable applications.
Object-Oriented Programming and system modeling.
Low-level programming, memory, and algorithms.
Version control, branching strategies, collaboration.
Agile methodologies for project/workflow management.
Analytical reasoning and solution-driven mindset.
Collaboration across multidisciplinary teams.
Clear articulation of technical concepts.
Ongoing professional development in data & AI.