SUMMARY

Anirudh Parsi

860-***-**** ad1u1k@r.postjobfree.com Hartford, CT

Experienced Data Analyst with 3+ years of expertise in research and analysis to identify business opportunities, seeking a position as a Business Analyst. Detail-oriented, critical thinker excelling in managing multiple projects with strong communication skills. Proficient in Tableau, Power BI, and Python.

TECHNICAL SKILLS

Programming: MySQL, Python, JavaScript, PySpark, HTML, Oracle PL/SQL Data Visualization: Tableau, Microsoft Power BI, PowerPoint Data Analysis Tools: Microsoft Excel,Microsoft Business Intelligence Other tools and technologies: Microsoft Office suite, Git, Github, Deep Learning, Machine Learning(Linear Regression, Logistic Regression), Yolo.Microsoft azure(Cloud technology )

EDUCATION

New England College, Henniker NH, New Hampshire December 2024 Master of Science in Data Science.

GPA – 3.5

KL University, Vijayawada, Andra Pradesh December 2020 Bachelor of Science in Computer Science

GPA - 3.5

RELEVANT EXPERIENCE

Replicon, Bangalore, Karnataka March 2021-August 2022

• Software Engineer

• Analyzed user requirements to develop software solutions and created technical specifications.

• Developed, tested, debugged, and documented software programs using python.

• Conducted unit tests on code modules to verify the accuracy and functionality of program logic.

. ADDITIONAL EXPERIENCE

KL University, Vijayawada, Andra Pradesh August 2019 Teaching Assistant,

• Conducted lab classes for undergraduate students for the subject Data Science (Python Language, Basic Statistics, and Advanced Excel)

• Assisted professors in grading the assignments and examinations. ACADEMIC PROJECTS

Face Detection and Recognition using MTCNN and PyTorch.

• This project utilizes advanced technologies, such as Multi-task Cascade Convolutional Networks (MTCNN) and PyTorch, for face detection and recognition, with applications spanning surveillance, security, and human-computer.

• The MTCNN architecture, a multi-stage deep learning model, is employed for robust face detection. A labeled dataset is crafted, incorporating essential data augmentation techniques M Faical Expression Recogniation Using Landmarks Distance

• The growing prominence of visual media has spurred interest in emotion recognition through facial analysis, with a focus on isolating facial features to predict sentiments from images and video frames, encompassing six primary emotions and neutrality.

• This involves initial facial detection, followed by feature extraction, with key landmarks such as eyebrows, eyes, nose, and lips identified using shape prediction algorithms like shape_predictor_68_face_landmarks.dat, with the Euclidean Distance Algorithm employed for facial expression analysis among various available algorithms

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