About Me

Masters in Data Science, Texas A&M University

I completed my Master’s in Data Science from Texas A&M University. Yes, I’m an Aggie.

Through the curriculum at the university, I have gained strong foundations in statistical concepts – random variables, different types of distributions, probability theory, descriptive statistics, inferential statistics, statistical modeling, hypothesis testing, mathematics for data science – linear algebra, optimization techniques. Apart from these foundations, by taking data science electives I enhanced my ability to use various data science algorithms related to – Regression Analysis, Classification, Clustering, Neural Networks, Time Series Forecasting, Data Mining Algorithms, Association Rule Mining, Natural Language Processing, Distributed Computing using Pyspark, Dask.

Bachelors in Electronics and Communication Engineering, SRM University

My Bachelor’s was in Electronics and Communication Engineering from SRM University. The engineering foundation laid a proper structure to develop problem solving skills, mathematical and logical reasoning abilities along with learning programming, digital circuits, computer networks and architecture, network security.

Graduate Student Assistant

Senior Technical Associate

Technical Associate

Graduate Student Assistant

Senior Technical Associate

Technical Associate

SKILLS

Data Mining
Data Cleaning and Preprocessing
Data Analysis
Data Visualization
Hypothesis Testing
Statistical Analysis
Time Series and Geospatial Analysis
Predictive Modeling
Machine Learning
Deep Learning
Natural Language Processing
Relational Database Management Systems
End to End ML/DL deployment on AWS cloud using AutoML and custom algorithms

TOOLS

Programming Languages: Python, R, SQL.
Data Visualization Libraries: Power BI, Tableau, Matplotlib, Seaborn, Plotly, Folium, ggplot2.
Data Science Libraries: NumPy, Pandas, Sklearn, TensorFlow, SciPy, Dplyr, Caret, Statsmodels, Pyspark, Pytorch, NLTK, Spacy, Dask, Jupyter notebooks, Databricks.
Data Science Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Recommendation systems, Neural Networks, BERT, Roberta, CNN, RNN, LSTM, Clustering (KMeans, DBSCAN, Hierarchical), ARIMA, SARIMAX.

Deep Learning

Built neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learned how to make them better with strategies such as Dropout, BatchNorm, and Xavier/He initialization. Learned the industry applications using Python and TensorFlow, and tackled real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing.

Certificate

IBM Data Science Professional Certificate

Developed and honed hands-on skills in Data Science and Machine Learning. Started with an orientation of Data Science and its Methodology, became familiar and used a variety of data science tools, learned Python and SQL, performed Data Visualization and Analysis, and created Machine Learning models. In the process completed several labs and assignments on the cloud including a Capstone Project at the end to apply and demonstrate knowledge and skills.

Certificate

Data Scientist with Python career track

With python as a programming language, worked with real-world datasets to learn the statistical and machine learning techniques and solved the business problems using the machine learning algorithms. Learned the visualization and performed data analysis on many projects that involved during the course. Performed hypothesis testing and built predictive models.

Certificate

Python for Everybody Specialization

Learned the fundamental programming concepts including data structures, networked application program interfaces,and databases, using the Python programming language. In the Capstone Project, used the technologies learned throughout the Specialization and designed applications for data retrieval, processing, and visualization.

Certificate

Statistics with Python Specialization

Learned statistical analysis, and use of the Python programming language to conduct data analyses, where data comes from, what types of data can be collected, how to effectively summarize and visualize data, how to utilize data for estimation and assessing theories, proper interpretations of inferential results, and how to apply more advanced statistical modeling procedures.

Certificate

Mathematics for Machine Learning Specialization

Gained a strong foundation in the mathematical concepts that are essential for data science and machine learning. Learned how to represent data in a linear algebra context and to manipulate these objects mathematically. Summarized the properties of data sets and map them onto lower dimensional spaces using principal component analysis. Solved optimization problems and use this skill to train models for describing data such as simple neural networks.

Certificate

Sales Insights - KPI Dashboard Design Using Tableau

Developed a Tableau dashboard using US superstores data to answer questions like sales, quantity, profits, their distribution in terms of state, type of segment and other questions. Dashboards offer interactive features and present a comprehensive overview or detailed information on various topics of interest. By analyzing sales data, companies can evaluate and enhance their performance where it is needed.

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Image Classification on the Fashion Dataset

Designed a convolution neural network model for Image Classification on the Fashion dataset and enhanced the prediction capability by 5% when constrained with 100000 parameters.

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End to End Machine Learning Deployment

Developed an end to end machine learning development cycle and created a streamlined workflow using CICD pipeline to deploy the model to AWS Cloud even though the initial analysis, model building were build using Jupyter notebooks.

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Restaurant Location Recommendation in NY state

Extracted 100 venues present within a 30km radius of any location in New York city and identified best potential locations to open a restaurant which can increase their revenue and profits. Reduced the risk of opening in a location with low customer demand or high competition by modeling based on the factors such as competitor presence, population density.

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Student Grade Prediction

Improved the accuracy of the student grade predictor model from 60% up to 90% by using different algorithms such as Decision-Trees, Random Forests, Support Vector Machines so that educational institutions can provide appropriate interventions for students to improve their academic performance, graduation rates.

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Criminal Analysis in the Neighborhoods of Toronto

Created an unsupervised learning model that segments the neighborhoods of Toronto based on similar crime activities and helps the law enforcement agencies to allocate their resources more effectively to reduce crime rate, response time and improve public safety.

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Marketing Insights for Product Development

Performed market data analysis on survey data to extract insights on demographics, consumer preferences, competitor presence, purchase behavior, and brand preference. Utilized findings are used to enhance brand awareness, increase market share, and improve product quality.

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Sentiment Analysis on Low Resource African Languages

Performed the sentiment analysis on low resource African languages and enhanced the F1-score evaluation metric of the text classifier by at least 2 folds using deep learning transformer models such as BERT, RoBERTa.

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Pandemic Flight Analysis

Analyzed the US domestic flight data and proposed a solution to decrease the number of flights to a minimum per state utilizing only those airports with most recurring and frequent flights so that travelers can reach their desired destinations, while lowering the spread of Covid-19.

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Water Usage Prediction

Designed a forecasting model that can predict the water consumption a week ahead that helps the policymakers to address the water shortage in certain regions. Improved the water usage prediction performance of a regression model and identified the top 5 important modeling factors affecting water usage from the given data.

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