SISHIR SREEKUMAR WELCOMES YOU
BIO
Data Analyst
A data enthusiast with 2 years of experience in interpreting, analyzing and visualizing data for driving business solutions. Proficient knowledge in Statistics, Mathematics and Analytics backed with a strong foudation in programming and an inquisitive mind to facilitate effective analyses of data.
EDUCATION
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UNIVERSITY OF MUMBAI
Bachelors in Engineering (2010-2014)
Major: Electronics and Telecommunication
GPA: 3.6
UNIVERSITY OF TEXAS AT AUSTIN
Masters in Science (2016-2018)
Major: Information Sciences
GPA: 3.93
MY WORK
I have over 2 years of experience in the consulting world as a Software Engineer, primarily working with different databases. In addition to this, I have also gained valuable experience in the field of data analytics through a combination of Academic courses, On-campus work, and my Capstone project.
DATA VISUALIZATION DEVELOPER
Jan 2018
With this project, I helped the IRRIS department by developing Tableau visualizations that answer strategic business questions posed forth by key decision makers of the University . I created interactive dashboards using live data fetched from the data warehouse that helped identify key trends and gather relevant insights from the historical data.
DATA ANALYST
June 2017
My role at the UT Austin Libraries entitles me to help with data gathering and analysis for the continuing resource renewals as well as data entry and processing of ebook and serial data into the electronic resource management system, Intota. Some of the tasks include automating the cost upload process of the Reconciliation project using Python Scripting and Excel Macros, creating visualizations and scraping of records from the online databases.
SR. SOFTWARE ENGINEER
July 2014 - July 2016
As a Senior Software Engineer at Capgemini, I got the opportunity to work in different roles with multiple clients in the span of 2 years. I worked in the role of a Database Administrator, performing duties such as Database Installations and Maintenance activities such as Table Space creation, Alert Log Monitoring, and Database Patching. I also got to work as a SOA developer working on integrating different technologies and enabling communication between them.
ACADEMIC PROJECTS
While pursuing my Master's degree in Information Sciences, I have had the chance to work on different academic projecs focussing on data analytics. Through the projects done as part of the course curriculum, I also gained an opportunity to learn and work with different statistical tools such as R, Python, SAS and Stata in addition to applying the knowledge imparted by the course.
BRAND ANALYSIS ON LUXURY SEDANS
Technologies used: Python Scripting, Gephi, Microsoft Excel
In this project, I got to analyze on unstructured data scraped from popular car discussion forums. Lift Analysis was performed to reveal clusters of brand association, which was visualized using a Multi Dimensional Scaling plot. Relevant attributes associated with each brand were identified and the lift scores calculated to measure the magnitude of association. I also perfromed a sentiment Analysis on the posts, based on which I created a bi-directional Product Comparison network. Page Rank score was calculated based on the network to observe a correlation between the scores and the sales figure.
SOCCER RESULT PREDICTOR
Technologies used: R, Machine Learning Algorithms
The English premiere league data for the 2014/2015 season was analyzed using R. The goal being to identify certain predictors that most strongly affect the probability of winning a match and construct a model based on the most predictive variables. To reduce the dimensionality of the predictor set, PCA was applied. Different models and machine learning algorithms (Random Forest, K‐Nearest Neighbor, and Multinomial Logit regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis) were applied and a maximum accuracy of 68% was obtained.
ONLINE ADVERTISING CTR PREDICTION
Technologies Used: R, Weka, Machine Learning Algorithms, Model Evaluation Technique
Worked on dataset obtained from a kaggle competition hosted by Avazu to predict the click through rate (CTR) of a customer given certain predictor variables. I carried out exploratory analysis to determine the relevant predictors, and applied various machine learning algorithms to the data. Different model evaluation techniques were implemented to evaluate the performance of the various model.
ANALYSIS OF UN VOTING PATTERNS
Technologies used: R, Gephi, Node XL, Microsoft Excel
The UN votes from the year 1946-2015 was collected from kaggle.com to study the voting behavior of different countries across different issues and timelines. Different metrics like betweenness, centrality was used to gain more insights into the voting cluster behavior of the countries. Gephi was used to visulize this behavior.
MOVIE SUCCESS PREDICTOR
Technologies used: Microsoft Excel, Python, SQL, Web Scraper
This project involved wrangling data from multiple sites. Using python, the data was cleaned and stored in the database server. SQL queries were run to get the required data to analyze the relationship between the movie’s success and various predictors. A multi‐variable regression model was applied. Visualizations in the form of graphs were plotted using Excel.
CERTIFICATIONS
I have always believed in the idea of broadening my horizons by adding to my skill sets. In that effort, I have pursued various certification courses to get acquainted to newer technologies as well as getting them validated
CONTACT ME
Have a particular challenge you’re trying to deal with? Contact me today and see what I can do for you.
Email:
Phone:
5128253879