Data Analyst

PRATIKSHA SHETTY

** ****** ******, ******, ** 617-***-**** ******.**@*****.***.*** www.linkedin.com/in/pratikshashetty9207 EDUCATION

Master of Science in Computer Systems Engineering Jan 16 – May 18 Northeastern University, Boston, MA

Data Warehousing and Business Intelligence, Big Data Systems and Intelligence Analytics, Probability and Statistics, Advances in Data Science, Network Structures & Cloud Computing, Database Management Systems, Web Design Bachelor of Engineering, Electronics Sep 10 – May 14 University of Mumbai, India

TECHNICAL SKILLS

Programming Languages: Java, PL/SQL, Python, JavaScript, SAS, R Programing, Linux, VBA Web Technologies: HTML5, CSS3, jQuery, Bootstrap, Angular.js, Apache Tomcat 8 Cloud Technologies: Amazon Web Services, Google Cloud Platform, Microsoft Azure ETL / BI: Power BI, Tableau, Qlik Sense, Talend, SSIS, Google Analytics Databases: Oracle 11g & 12c, MySQL, SQL Server, NoSQL (Amazon DynamoDB), MongoDB, PostgreSQL, Azure SQL Tools: Microsoft Excel, NetBeans, GIT, RStudio, Orange, IntelliJ IDEA, Junit, JMeter, Travis CI, TOAD Data Modeler WORK EXPERIENCE

Data Analyst, Co-op Jan 17 – Aug 17

The Norfolk and Dedham Group

• Undertook the company-wide Oracle Upgrade Project by writing several complex SQL scripts and Daily Stat Functions to ensure data consistency post upgrade.

• Saved 25% of allotted project completion time by using rigorous and timely Data Profiling techniques, considered cardinality, data types, functional dependencies and KPIs to identify the root cause for performance errors.

• Assisted the Server Migration Project, wrote batch files and shell scripts for data-related scheduled tasks. Tracked and monitored data after every Daily Cycle Load along with documentation and corrections, incase discrepancies occurred.

• Collaborated with BI team and stakeholders to create operational and analytical dashboards using Power BI and Tableau to display the company’s performance over the past 4 years.

• Used PL/SQL to create several stored procedures, triggers, packages and functions to meet various ongoing business requirements. PROJECTS

Data Warehousing and Business Intelligence Jan 18 – April 18

• Sourced data from 4 different Operational Sources and used Data Profiling, Staging, Auditing and Cleaning techniques to ensure data consistency. Data Integration was done on two ETL platforms - Talend and SSIS, to populate the targeted Dimensional Model (MySQL database).

• Significantly optimized Integration/Job runtime(45 million records in 19 minutes) and data quality by allotting run-time memory based on data volume, removed clutter by eliminating redundant attributes and implemented parallelization/serialization techniques as required.

• Used the newly created target as a Data Mart for BI Reporting, created insightful dashboards using SAS, Qlik Sense, Power BI and Tableau. Big Data Analytics, Correlating Twitter Data and Stock Market Trends Sept 17 – Dec 17

• Performed Sentiment Analysis on 250,000+ tweets from 2017 related to the company ‘Apple’, collected using the Twitter API.

• Implemented Natural Language Processing techniques like Word2Vec, N-Gram transformation and One-Hot Encoding for feature extraction and trained the data with SVM, Random Forest and Naïve Bayes classification algorithms. Used 5,10 and 15-fold cross-validation and recorded the best results.

• Compared the graphs against Apple’s stock market value collected from the Yahoo Finance API for the same year, to conclude if the two had an appreciable correlation.

Network Structures & Cloud Computing Sept 17 – Dec 17

• Built the complete Cloudformation Infrastructure, with each resource specifically customized to support the deployment of a Spring Boot Application on AWS cloud platform. Secured the application and protected user data using SSL encryption and SALT cryptography.

• Extensively used AWS storage options such a file storage (S3 Bucket), CDNs, RDS, NoSQL databases, etc. Data Science Project in R Sept 16 – Dec 16

• Performed predictive analysis on a raw data set by cleaning the data and using predictive algorithms like Linear Regression, Decision Trees and SVM on RStudio.

• Implemented Random Forest, Naïve Bayes to compare the accuracy of the analysis and performed data visualization using clustering on Orange Data Mining Toolbox.

Database and BI Jan 16 – April 16

• Created a Dimensional Data Model for a School Dataset using TOAD Data Modeler and build OLAP cubes in SQL Server Analysis Services for each subset of this model.

• Performed intricate data analysis using Oracle, MySQL, SQL Server and NoSQL and generated dashboards to show analysis based on query results using an interactive data visualization tool - MS Power BI.

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