Sudha Sura

Enthusiastic Data Science student with a solid foundation in statistical analysis, machine learning, and data visualization. Skilled in programming languages such as Python and R, and experienced with tools like SQL, Tableau, and TensorFlow. Adept at conducting data preprocessing, building predictive models, and deriving actionable insights from complex datasets. Known for strong analytical abilities, attention to detail, and a collaborative approach to problem-solving. Eager to apply academic knowledge and hands-on skills to real-world projects in a dynamic internship setting to drive data-driven decision-making and innovation.

Capstone Project: Face Recognition,

      Role: AI developer

• In this project, I was responsible for developing a robust face recognition system using deep learning techniques. The goal was to create a solution capable of accurately identifying and verifying individuals in real-time from video feeds and images.
• Responsibilities included data collection, model training with CNNs, and integration into existing applications. I conducted extensive testing to optimize performance, achieving over 95% accuracy. The project enhanced security and user experience in various applications.

Research Project: Breast Cancer Detection,

• In this project, I was responsible for developing an AI-based breast cancer detection system using deep learning techniques. The objective was to create a model capable of accurately identifying potential cancerous tissues from mammogram images, aiding early diagnosis and improving patient outcomes.
• My responsibilities included collecting and preprocessing medical imaging data, designing and training convolutional neural networks (CNNs), and optimizing the model's performance to minimize false positives and false negatives. I integrated the system into healthcare applications for real-time screening, achieving over 90% accuracy. The project contributed to enhanced diagnostic capabilities and supported medical professionals in making timely decisions.

Data Engineering

• Through the Data Engineering coursework, I acquired a solid foundation in designing and building scalable data pipelines and architectures. I gained hands-on experience with tools like SQL, Hadoop, and Spark to process and manage large datasets efficiently. I developed strong skills in implementing ETL (Extract, Transform, Load) processes and data warehousing techniques to ensure data reliability and accessibility.
• Additionally, I learned to work with cloud-based data infrastructures and real-time data streaming technologies, optimizing workflows for high-performance data processing. This coursework enhanced my ability to handle complex data engineering challenges in real-world scenarios.

Information Visualization

• Through the Information Visualization coursework, I gained expertise in transforming complex datasets into clear, interactive visual representations. I developed proficiency in using tools like Tableau, D3.js, and Python libraries (Matplotlib, Seaborn) to create meaningful and intuitive visualizations. I learned the principles of effective data storytelling, focusing on clarity, aesthetics, and audience engagement.
• Additionally, I acquired skills in handling large datasets, creating dashboards, and applying visualization techniques to support data-driven decision-making. This coursework enhanced my ability to communicate insights through visuals, making complex information accessible to diverse stakeholders.

Statistical Computing

• In the Statistical Computing course, I developed a deep understanding of statistical methods and their practical application in data analysis. I worked extensively with programming languages like R and Python, applying libraries such as NumPy, SciPy, and R’s statistical packages to explore and manipulate data.
• The course emphasized key concepts such as hypothesis testing, regression models, and probability distributions, allowing me to solve complex data problems. I also became proficient in automating statistical workflows and visualizing results, equipping me with the skills to interpret data insights effectively and support data-driven decision-making.

Knowledge Discovery and Data Mining

• In the Knowledge Discovery and Data Mining course, I gained expertise in extracting valuable insights from large and complex datasets. I learned to apply various data mining techniques such as classification, clustering, association rule mining, and anomaly detection using tools like Python (Scikit-learn), R, and Weka.
• The course focused on the entire knowledge discovery process, including data preprocessing, pattern recognition, and model evaluation. I also acquired practical experience in working with real-world datasets to uncover hidden patterns and trends, enhancing my ability to make data-driven predictions and decisions. This course sharpened my problem-solving skills by combining theory with hands-on projects.