TANISHA TOMAR

B.Tech student in Computer Science and Data Science, with expertise in programming, data analytics, and web development. Skilled in utilizing data-driven strategies to address complex challenges and enhance operational efficiency. Proficient in Python and SQL, with practical experience in data visualization, predictive modelling, and software development. Proven leadership as Class Representative, fostering collaboration to improve academic performance and contributing to technical and business development projects. Highly motivated employee with a desire to take on new challenges. Strong work ethic, adaptability, and exceptional interpersonal skills. Adept at working effectively unsupervised and quickly mastering new skills.

• Quickly adapted to new tools and technologies, I enjoy learning on the fly to help push projects along. This passion for continuous learning makes me flexible and beneficial to every team.
• Communication: Excellent communicator with strengths in public speaking and active listening; known for articulating ideas clearly and building productive relationships with clients and colleagues.

• Modern JavaScript From The Beginning 2.0, 2024
• Introduction to Cybersecurity

• Algorithm Visualizer: Designed and developed an interactive JavaScript-based visualization tool to demonstrate fundamental sorting and searching algorithms, including Bubble Sort, Merge Sort, and Quick Sort. Integrated real-time animation to show the step-by-step execution of sorting and searching algorithms. The tool highlights element comparisons, swaps, and search iterations, helping users understand how each algorithm progresses from start to finish. By making intermediate steps visible, it clarifies both the logic and runtime behavior.
• Face Detector: Developed a Python-based face detection tool using OpenCV that processes live webcam input to identify human faces in real time. The program analyzes each video frame, applies detection algorithms, and marks faces with bounding boxes for clear visualization. It demonstrates the working of basic computer vision techniques in an interactive way while maintaining responsiveness. Achieved around 80% detection accuracy with an average response time under 100ms, making it suitable for lightweight AI applications.