E: ac8gm7@r.postjobfree.com

M: 916-***-**** /

888-***-****

LinkedIn: https://bit.ly/2Cvkmfi

Kaggle : http://bit.ly/2yrVUZt

GitHub : http://bit.ly/2OCizML

EDUCATION

PGP IN DATA SCIENCE 2019

Praxis Business School

B. Tech, IT 2015

Maulana Abul Kalam Azad University Of Technology 7.92/10

XII (CBSE) 2010

Julien Day School 77%

X (ICSE) 2008

Julien Day School 76%

SKILLS

SQL 3.5/5

Time Series Analysis 3.0/5

Python 4.0/5

Machine Learning 4.0/5

Data Warehousing 3/5

MS Excel 3/5

Tableau 3.5/5

PySpark 2/5

R 3/5

Project Management 4/5

Project Reporting 4.5/5

Predictive Modelling 4.5/5

Data Visualization 4/5

Mongodb 3/5

NoSQL 3/5

CERTIFICATIONS

CAPM

ITIL V3 Foundation Certified

Information Storage and Management Cloud Infrastructure Service

J2EE with Oracle

EXPERIENCE

DELL EMC DATA ENGINEER

March 2016– June 2018

I did an Internship of 6 months at Dell’s Data Engineering Team, where I learnt how to use Python to process the data. I was also introduced to advanced SQL, and received some hands-on experience in Informatica, and some exposure to Data Warehousing.

Pre-processed the data and applied different machine learning

modelling techniques like K-fold cross validation, Random forests, XGBoost

techniques etc. Programming language- Python

DELL EMC BUSINESS ANALYST, DATA CENTER MIGRATION SPECIALIST

March 2017 – April 2018

This role required me to plan & execute migration events. Planning and gathering of information on the migrating servers was done over a span of 6 weeks. Once the list was confirmed I had to prepare the Locklist and the Runbook. For Preparing the Locklist I used Python and MySQL. Once the Locklist was ready I would schedule and conduct Table Top and Go-NoGo Meetings. During migration window, I had to keep a close eye on different teams and resolve different issues as they come. Once the window closes, I needed to look over and resolve issues, by interacting with different teams, in the Hypercare Week.

DELL EMC SR .BUSINESS ANALYST, PROJECT MANAGER

March 2016 – June 2018

I was part of a team of Consulting Project Managers, who managed Services-Led Projects. Apart from managing and delivering technical projects that involved data center migration, I have managed projects on Azure-installation and Implementation, and Workforce Modernization. My tasks included, understanding the SOW, creating the project charter, followed by the project plan, and manage, and report, project financials and other deliverables.

ANALYTICS PROJECTS

Time Series Analysis (ARIMA)

Tools Used: Python

This is my most recent project. Even though the project was a simple one, but since I never before did a project on Time Series Analysis it helped me learn a lot; stationarity of a series, interpretation of ACF and PACF plots, and how to tune the parameters of ARIMA model to best analyze and forecast time series data.

Adult Census Income

Predict whether income exceeds $50K/yr based on census data (KAGGLE.COM)

Tools Used: Python

The given dataset might seem simple, to many, at first glance. But on careful exploration, we could see that, merely dropping the missing values will reduce the overall accuracy of the model. So I used imputation to fill the missing values for both numerical and categorical variables. Standardized and normalized the dataset, and then I used Random Forest, Logistic Regression, and Bagged Tree Classifier and compared the scores of each to get to the best model

Soft Skills- Communication, Collaboration, Creative Thinking, Critical Thinking, Problem Solving, Generating Hypothesis.

Tools/Library- Python, Matplotlib, Seaborn, Numpy, Scikit learn, Pandas, nltk,

Hard Skills - Data Analysis, Predictive Modelling, Statistics, Data Visualization, Programming, Debugging, BI reports

Pokemon Challenge (WWW.KAGGLE.COM)

Tools Used: Python

Since my first project had much to do with Regression, for my second project I decided to go with Classification. The data comprises of three files; one contains the Pokemon’s characteristics, the second one contains information about previous combats, and the third is ‘test’ dataset. Cleaning the data was challenging, I used Logistic Regression, Decision Tree and Random Forest, and calculated the accuracy of each to get to the best model.

PUBG Finish Placement Prediction (WWW.KAGGLE.COM)

Tools Used: Python

The project had a fairly large dataset and we were asked to come up with the most promising strategy to win the game. I used EDA to deduce the best strategy and followed it with the application of Decision Tree and Random Forest to calculate the winning chance of any player. In my EDA I also found some possible hackers/cheaters as well. As my first project, it sure helped me understand analytics better

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