Nakul Sharma

Background

About Nakul Sharma

A hardworking and aspiring result oriented professional having done MBA in Big data analytics from Goa institute of management with a background in Aeronautical Engineering and over 3 years of profound experience working in Data analytics and Educational services industry. A strong believer in self motivating and developing skills in accordance with the latest industry standards to maximum extent. In short a proven ability to work with all levels of personnel. I have learnt to apply my technical, analytical and soft skills gained throughout my Career to create machine learning models including predictive analysis to solve various industrial problems. Not only do I bring strong mathematical and Data science acumen but other soft skills to the table such good presentation abilities and public speaking as well. I’ve taken on challenging work that has allowed me to apply my knowledge at a professional level. I’ve learned to work well under pressure, focus on detail, and work as an individual as well as part of a team to succeed. I have exceeded the goals for all positions I’ve held and am looking for an opportunity to demonstrate my abilities in a company that focuses on applying Technical skills in Data science to a whole new level and is focused on building and maintaining relationships within the Data science realm and beyond. Highlighted Skills: • Data Analysis: R (dplyr, tidyr, lubridate, stringr, ggplot2), Python (Pandas, NumPy, SciPy, Matplotlib, scikit-learn), Hive, Pyspark, Advanced SQL, Tableau, Power BI, Advanced Excel, NoSQL(MongoDB, Neo4j), Sawtooth • Statistics: Statistical learning: Decision Tree, Random Forest, KNN, Classification, Supervised/Unsupervised Learning, Cluster Analysis, Resampling Regression Analysis: Multiple regression, Logistic Regression, Regression smoothers; Penalty methods Inferential Statistics: A/B testing, Bayes' theory, Parametric and Non-Parametric Methods, Sampling Distribution Theory, Large Sample Theory, Hypothesis Testing, Maximum Likelihood Estimation, Likelihood Ratio Testing, Confidence Interval Construction Time Series: Stochastic models, Exponential Smoothing, Holt-Winters Methods, Box-Jenkins approach, Frequency Domain Analysis Experimental Design Sample Surveys: Random Sampling, Unequal Probability Sampling, Systematic, Multistage and Stratified Sampling