Highlights:• Experienced Optometric Physician having 5+ years of experience in analyzing health data. • Demonstrated effective Time Management and Project Management aptitude to perform simultaneous research and teaching duties of 60+ undergraduate students each year. • Built-up strong Public Speaking Skills to make confident to present complex scientific information on short notice. Technical Skills: • Languages: Python (pandas, scikit-learn, NumPy), R-studio • Predictive Modeling: Linear/ Logistic Regression, Classification, Clustering, Decision Tree, Random Forest • Machine Learning: Deep Learning, Neural Networks, Image Processing, Keras, TensorFlow • Probability and Statistics: Hypothesis Testing • Databases: SQL (MySQL, SQLite) • Data Visualization: Oracle Analytics Desktop, matplotlib, seaborn • Version Control: Github • Tools: MS Office • Data Science Methods: Mining, Wrangling, Cleaning, Analysis, Visualization, Storytelling • Statistical Software: Excell, SPSS, AMOS • Data Analysis Techniques: PCA, t-test, ?2, ANOVA, etc • Communication: Good written and oral communication Please feel free to reach me out through my email: debarshidatta@outlook.com / cell # + 1561 271 2871 (EST).

Skills:Data Analyst, Data Science, UX Researcher

Goal:I’m a Data Scientist with over 6 years of hands on of experience in doing projects related to Image Processing, Social and Health Data. Currently, enrolled in Springboard's Data Science Certification Program. The program is equipping me with the technical proficiency in and experience working with full data science cycle: problem statement, data wrangling, exploratory data analysis, modeling, evaluation, data storytelling, and mastering skills in Python, Pandas, APIs, SQL, Statistical Inference, Machine Learning (Supervised & Unsupervised).

Honor:¦ Received Graduate Fellowship for Academic Excellence ($5000) for the 2019-2020 academic year, Florida Atlantic University, USA. ¦ NSF Student Travel Support for attending Big Data Neuroscience Workshop 2019, 2018, & 2017, organized by the Advanced Computational Neuroscience Network (ACNN), USA. ¦ The 2018 Young Stars Estes Awards supported by the Association for Psychological Science and the Psychonomic Society to support travel to the Workshop on “Deep, Fast and Shallow Learning in Humans and Machines” at Indiana University, USA.

Data Science Fellow 01/2021 - current
Spring board, Remote, United States
Industry: Data Science
Mastering skills in Python, SQL, Data Analysis, Data Visualization, Hypothesis Testing, and Machine Learning.
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Researcher 08/2014 - 01/2021
Florida Atlantic University, Boca Raton, FL United States
Industry: Research and Education
Conduct Behavioral Research, Analyze data and report the results.
• Face detection: Successfully developed Supervised Machine Learning algorithms using Python ("Face Detection Pipeline") to detect and estimate the percentage of correct recognition of faces by using PCA to reduce the dimensionality and built and fit a multi-class SVM classifier. The accuracy rate is ~80%. • Image Classification: Developed a Deep Learning Classifier using a Convolutional Neural Network model using AlexNet & Classification Learner App in MATLAB (SVM, Logistic Regression, Decision Tree, KNN, a Linear Discriminant) to recognize different species of cats & dogs from 25,000 images from the Kaggle dataset, the model has successfully classified with an accuracy of ~97%. • Image Classification: Developed a Deep Learning Classifier using a Convolutional Neural Network model using PyTorch (Decision Tree Classifier) to recognize different species of flowers, the model has successfully classified flower species with an accuracy of ~95%. Dissertation Projects: • Executed research on Visual Perception by using Psychophysical Techniques. Designed complex 2D and 3D stimuli by using Psychtoolbox— a set of MATLAB functions for Vision and Neuroscience Research, and PsychoPy—a package for the psychological experiments. • Developed research, based on concepts of Perceptual Grouping (Gestalt Psychologist), Amodal Completion, Affinity (Ullman, 1979). Used different experimental paradigms on Apparent Motion Perception such as Dynamic Grouping Motion, Counterchange Specified Motion, and Motion Quartet where motion is perceived by changing the luminance of surfaces. • Developed research projects into various forms of scientific communication resulting in a first-author paper in high-impact peer-reviewed journal (PLoS One), and have participated in several poster presentations in local, national, and international conferences.--
Research Associate 08/2011 - 08/2013
University of Missouri-St.Louis, St. Louis, Missouri United States
Industry: Education and Research
• Conducted research to learn the basics of spatial and temporal vision by using psychophysical techniques. • Learned to work in a multicultural environment with a unique combination of detail-oriented mindset, analytical skills, and a fast-paced work environment.
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University of Missouri-.Louis 08/2011 - 08/2013
St.Louis, Missouri, United States
Degree: Master's Degree
Major:Vision Science
Minor:Data Analyst
Project Description: Spatial Pattern Detection and Discrimination in Normal and Amblyopic Adult Humans Under Monoptic and Dichoptic Viewing Conditions. My involvement in this project was to learn the basics by researching spatial and temporal vision by using psychophysical techniques & Psykinematix.

UX Researcher UX Researcher
Data Science Data Analyst, Data Science