Professional Summary

Around * years of experience and comprehensive industry knowledge in Business/Data Analysis, Data Integration, Data Mining, Business Intelligence developing performance dashboards/reports.

Have implemented Data Mining concepts on several large data set of structured and unstructured data to discover and unearth meaningful insights.

Ability and experience in applying predictive models and Machine Learning algorithms such as Multiple Linear Regression, Logistic Regression, Decision trees, K Means clustering, and other advanced statistical techniques for analytical insights and reports.

Have extensively used python libraries such as NumPy for mathematical calculations, Pandas for data manipulation and analysis, Matplotlib and Seaborn for data visualization, Sklearn/ Scikit-learn for Machine learning, TensorFlow for deep learning, and NLTK for Natural language processing.

Working knowledge of SDLC and Waterfall, Agile Methodologies.

Have performed Web scraping for pulling out HTML and XML data with the help of python beautiful soup library.

Knowledge of the design and implementation of the Data Warehouse life cycle and familiarity with entity-relationship/dimensional modeling, Star/Snowflake Schema, Facts and Dimension Tables.

Working knowledge of advanced Excel functions such as Sort, Filter, Pivot Tables, SUMIF functions, and VLOOKUP functions.

Have worked on Excel Linear Programming, Time-Series, Seasonal Trends, Forecasting and Network Models.

Skillset

Programming Languages

Python, SQL, R

Libraries

NumPy, Pandas, Matplotlib, Scikit-learn, Seaborn, Beautiful Soup, NLTK, Regex

Statistical Analysis

Anaconda distribution (Jupyter notebook, Spyder), R, RStudio

Visualization Tool

Tableau, Microsoft Power BI

Databases

MYSQL, Oracle 11g

Project Management

JIRA

Others

UML, MS Visio, MS-Office Suite (Word, Excel, XLMiner, Project, Outlook), TOAD, AWS basics

Education

Master of Science in Information Systems, Business Analytics – (GPA-3.57)

California State University, Fullerton, USA January 2018 - January 2020

Bachelor of Engineering in Electronics and Telecommunications Engineering

Maharashtra Institute of Technology, Pune, India August 2011 – May 2015

Professional Experience

1. Data Center, California State University, Fullerton

Student Data Analyst July 2018 – December 2019

Data Center server monitoring via OpManager tool to ensure necessary services of the campus function without any interruption.

Ad-hoc analysis on performance management of student portals, server data to create error logs for complaints registered.

Guided other assistants in raising hardware and software incidents with the help of ServiceNow ticketing tool.

Used Tableau to find trends of the data center device storage capacity and the temperatures of the servers.

Data vis. from the monthly server status reports helped supervisor and higher management to predict when might servers’ temperature and storage capacity deviate from what was considered as normal.

2. CoinGenius, Irvine, CA

Data Science Intern September 2019 – December 2019

The 10 weeks internship program started with: Volatility indexes of the top 10 crypto coins to highlight the prices fluctuations of the coins with the highest market cap.

The data analysis on the crypto price peaks and lows over the past week, month, or a quarter, helped CoinGenius’ clients/ investors in making informed decisions in the future.

Identifies selected keywords and fetched data from the twitter API for selected crypto coins to calculate the sentiment index score using Regex and NLTK libraries.

Natural Language Processing was used to perform sentiment analysis based on the polarity value of the selected tweets and the calculated index score was later integrated into the company’s website.

Identified major investors of the market with the help of Whale Alert Analysis, where visualizations of such whales helped in drawing pattern based on their recent activity.

3. ProIndia Services, Gurgaon, India

Software Engineer September 2015 – September 2016

Working knowledge of all the client interfaces of Informatica like Designer, Workflow Manager, Workflow Monitor and Repository Manager.

Good hands on experience in design and development of Informatica Mappings, Sessions and Workflows.

Responsible for extraction, transformation and loading of data from Database and flat files into Data Warehouse.

Involved in Low level design, development and testing of mappings to assure that data is loaded as per ETL requirement specification.

Developed Informatica mappings for Type 1, Type 2 Slowly Changing Dimensions.

Migration/Deployment of Mappings, Sessions and Workflows to QA (Quality Assurance) and Production environment.

Monitoring the workflow and resolving issues within the SLA.

Root Cause analysis for repetitive failures and maintaining the tracker for the same.

Academic Projects

Instacart Market Basket Analysis and Recommendation System CSUF, Fullerton

The Instacart dataset was a relational set of files consisting of more than 3.4M rows with 206K unique users.

Objective was to use anonymized data on customer orders over time to build a model to help us in predicting what orders are likely to be reordered.

Interpret and summarize results to accurately list overall demand for customers, highest/least selling products, which departments have the highest sales and the product, what days and hours of a week are considered peak hours, etc.

Human Resource Data Survey CSUF, Fullerton

Performed data cleaning, exploratory statistics, and analyzed data using kNN, Classification and Regression trees, and Decision models to predict monthly salary and attrition rate of employees.

Multiple Linear Regression for Prediction and kNN, CART and Logistic Regression models were used for Classification.

World Bank International Debt Statistics CSUF, Fullerton

Objective was to analyze international debt data collected by the World bank to find total debt owed by different countries, countries with the highest debt across different debt indicators.

Used MYSQL RDBMS to pull data, to be later statistically analyzed to provide meaningful insights based on debt indicators.

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