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Data Analyst Python

Boston, Massachusetts, United States
March 15, 2019

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Boston, MA. ***** 617-***-**** LinkedIn Github Portfolio 3 YR OPT Available May 2019


Northeastern University, Boston (Concentration: Data Science) May 2019 Master of Science in Engineering Management

Relevant Courses: Probability and Statistics, Data Mining in Engineering, Neural Networks/Deep Learning, Machine Learning in Finance

MIT College of Engineering, Pune, India June 2016

Bachelor of Engineering in Electronics and Telecommunication Relevant Courses: Object Oriented Programming (C++, Java), Digital Image Processing (MATLAB) SKILLS

Programming Languages: R & Python (pandas, numpy, matplotlib, seaborn, statsmodels, sci-kit, scipy), Keras, TensorFlow, PyTorch, R programming, Hadoop, Hive

Databases/ ETL: MySQL, Oracle

Visualization Tools: Tableau, Advanced MS Excel (Pivital table, vlook), MS Office Machine Learning(Supervised and Unsupervised Learning): PCA, LDA, Predictive Modeling, Linear Regression, Logistic Regression, CART, SVM, K-NN, Bayesian Analysis, Clustering, Neural Networks, Deep Learning, Natural Language Processing, Time-Series Econometrics


Collegepond – Mumbai, India Data Analyst Intern Jan – Aug 2017

• Extracted and parsed through large volume raw-data logs of clients to identify high interest in higher education

• Enhanced customer experience by analysing factors impacting the choice of country thereby increasing customers’ view by 45%

• Investigated patterns using Python and built predictive model to identify university selection of the clients SELECTED ACADEMIC PROJECTS Github

Credit Card Fraud Detection Using Machine and Deep Learning in Python Jan-Apr 2019

• Using Machine Learning techniques like Logistic Regression, Classification Tress, Naïve Bayes, Local Outlier and Isolation Trees, created prediction models to predict fraudulent activities

• Trained Deep Learning models like Artificial Neural Networks and Self Organizing maps to observe the increase in fraudulent credit card transactions based on rampant credit card usage

• Created prediction models comparing machine learning and deep learning approach; Obtained accuracy of 95% Portfolio Management and Algorithmic Trading of Cryptocurrency using Python Sept-Dec 2018

• Analyzed cryptocurrency term structure data to understand the distribution of OHLC by plotting kernel density and to identify bull and bear markets

• Forecasted cryptocurrency prices using Bootstrapping, Principal Component Analysis, Bollinger bands, Kalman Filter, Moving Average, Double cross-over, OLS Regression, Random Forest, Long Short Term Memory(RNN) and designed trading strategies using prices, and market oscillators

• Explained the mathematics behind the machine learning techniques by calculating the coefficients using matrix factorization and calculus in Python

Image Classification using CNN in Python (TensorFlow) Sept-Dec 2018

• Using Tensorflow, designed and implemented a feed forward convolutional neural net for images to solve fake face recognition

• Compared ReLU with tanh activation function to find most accurate activation function with minimum validation loss

• Achieved validation accuracy of 93% and realized the accuracy using matplotlib to display the performance NBA Database Design and Database Management Jan - Apr 2018

• Developed a code for JOINS, VIEWS, TRIGGERS, DUMPS, TRANSACTIONS, PRIVILEGES using SQL to check the database functionality

• Using SQL Workbench, created Entity-Entity Relationship diagrams for NBA database

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