Data Analyst Python
Resume:
Dhanashri Jadhav
Aspiring Data Scientist
Aspiring Data Scientist with 2+ years of experience in data analysis and data modelling. Skilled in Data Analytics, Machine Learning Algorithms, Statistics, Problem solving, and Web & Database Programming. Currently seeking internship in the field of Data Science.
******.*@************.***
Boston, United States
https://www.slideshare.net/DhanashriJadhav5
linkedin.com/in/dhanashrij
https://github.com/Dhanashrij11
EDUCATION
09/2019 – Present
M.S in Analytics
Northeastern University
Boston, USA
Big Data Predictive Analytics
Artificial Intelligence Machine Learning
Data Visualisation Statistical Analysis
07/2011 – 05/2015
B. E in Information Technology
Gujarat Technological University
Gujarat, India
WORK EXPERIENCE
08/2016 – 06/2019
Data Analyst
E-tech Global Services
Vadodara, India
Led team of enthusiastic and new Data Analysts
Supported the Knowledge Discovery process using the Data Mining Tools
Performed ETL using Python, Pandas, Matplotlib, and Jupyter Notebook
Analyzed client/customer datasets to provide strategic directions to the company using Data Analytics
Performed various statistical analysis using Excel
(VLOOKUP, PivotTable, Charts, VBA, Macro and
Regression Analysis)
07/2014 – 05/2015
IT intern
L&T Technology Services
Vadodara, India
Developed Web App for managing company assets
(HVACS, Elevators, PC’s, Security Access Machines, Vending Machines, and more) in ASP.Net and MySQL
Created an emergency alert system, used to convey
messages to the management in case of critical
condition/failure of the assets.
The application also provided predictive maintenance reports, thereby reducing failures and increasing
efficiency of assets by up to 8%
SKILLS
Python R Tableau PowerBI SQL Pandas
Matlab ETL AWS Keras Scikit TensorFlow
Regression Data Mining A/B Testing Git SAS
EDA Neural Networks Machine Learning Spark
Time Series Scala Deep Learning Google Analytic
ACADEMIC PROJECTS
Superstore Sales Prediction Analysis R Studio, Machine Learning
Performed data profiling, cleaning and linear regression to identify the predictor variables
Analyzed the data (EDA) and implemented XGBoost and Random Forest algorithm for identifying correlation and significant variables Developed generalised linear model and predicted the Superstore's Sales and Profit that could serve as insights to stakeholders Music Recommender System Python, Jupyter Notebook Used subset of “Million Song Dataset’ to create recommendations using Machine Learning Library (MLlib), and Pandas
Created Popularity -based and Personalized Recommender System FIFA Player 2020 Best Team Position Prediction R
Studio,KNN, Naïve Bayes, Random Forest, CNN, Linear Regression
Developed predictive models to predict the team position for the player based on their skills and characteristics
Identified the strongest positive correlation between the position of team and predictor variables
Performed EDA, Linear Regression, KNN Classification, Naïve Bayes, Random Forest, and Neural Networks, achieving accuracy of ~ 80% Fashion MNIST Image Classification R Studio, Kera’s, TensorFlow, CNN
Performed multi-class classification on 70,000 images distributed under 9 fashion labels categorized as Top, Shoes, Bag etc. Performed EDA, KNN Classification, Naïve Bayes, Random Forest, and Neural Networks, achieving accuracy of ~ 90%
Boston Property Assessment 2020 Python, Seaborn, Matplotlib, Sklearn, Spyder
Performed ETL, EDA, removed outliers using skewness-kurtosis, correlations, and heatmap, and extracted most dominant features from dataset of ~175K rows
Developed models to predict the key factors in assessing the land value of Boston using Linear Regression, Lasso Regression, and Ordinary LeastSquare that yielded the adjusted R2 as 90%
Courses
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Achievements/Tasks
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