Application Developer
Resume:
Sharath Kumar Parameshwar Bhat
Amherst, MA – 01002.
*****.*******@*****.***
Linkedin Profile: linkedin.com/in/sharathparameshw ar
Portfolio: https://sharathkumarpblog.wordpress.co m/
Technical Skills:
•Languages – Python, R, Scala, Core Java, HTML, CSS, JavaScript(D3.js), Shell Scripting, SQL (MySQL, Oracle, Postgresql, PL-SQL)
•Tools – Git, IBM SPSS, Tableau, MS Office Suite, Anaconda (Spyder, IPython, Jupyter Notebook).
•OS – Windows, Linux – Ubuntu, CentOS, Virtualization Tools like Vmware, Virtualbox
Big Data Technologies – Hadoop, HDFS, MapReduce, Spark (MLlib, Streaming, SparkSQL), Hive, Ambari, Pig
Others – AWS services like S3, EC2, Redshift etc, TensorFlow, Keras, NLTK, PySpark, scikit-learn, Cloudera
Other Accomplishments:
Udemy Certificates: AWS Certified Solutions Architect, Machine Learning, Scala & Spark for Machine Learning.
IBM Big Data Foundations Certificate
Retail Newsletter SPOC at IBM
Got a rating of 4.0/4.0 in the first year at IBM and a rating of 3.5 in the second
3rd place in Inter School Essay Writing Competition.
•Head of the Organization Committee organizing School Annual Day and Sports Day events.
PROFESSIONAL EXPERIENCE:
IBM, Bangalore, KA, India
Application Developer (PC Bank) August 2014 – June 2016
•Providing Level 1-2 support and code fixes for PC Bank web applications built on Java/J2EE framework in production environment.
•Providing both production support and data analysis support within the technology platform management organization.
•Developed automated solutions of support tasks & created real time batch processing using Bash Scripting & automated logger to generate reports.
•Developed and took ownership of dashboards of KPI’s built using Tableau.
•Performing SQL performance tuning, query optimization, indexing.
•Analyzing malfunctions occurring in the existing database systems and providing recovery solutions for the data corrupted in the process.
•Created SQL modules to integrate the existing data on to the database.
•Application Deployment using IBM Websphere Application Server.
•Writing SQL Queries for fault diagnostics, resolution or Change controls.
EDUCATION:
University of North Carolina at Charlotte
Master’s in Computer Science(Data Science) Aug 2016 – Dec 2017
GPA 3.7/4.0
University Visvesvaraya College of Engineering
Bachelor’s in Computer Science and Engineering Sept 2010 – Jun 2014
GPA 4.0/4.0
PROJECTS:
University Predictor Using Existing Result Data [R, R Shiny] Description: A system that uses the available dataset of admission results and analyzes this data and the student’s profile to extensively and accurately predict the admission possibility of the
student into a University’s Master’s program.
Analysis and Visualization of Climatological Data [R, Weka, Tableau]
Description: Discover the trends and variations in the average temperature and GHG emissions over time in the US and compare it with that of the world by following the six steps of CRISP-DM methodology and build Machine Learning models and calculate their accuracy to evaluate our model predictions.
Twitter Query Search and Sentiment Analysis [Python, HTML, CSS, Flask, REST API, Postgresql]
Description: Built a webpage that allows a user to login to Twitter and search for tweets based on a certain query using the Twitter API. Also used the Natural Language Processing API to perform Sentiment Analysis on the tweets obtained from the query.
Classification of Loan Data using Machine Learning[PySpark]
Description: Implemented Naïve Bayes, kNN and Logistic Regression algorithm from scratch without using any existing libraries to classify/predict whether a particular loan would default or not. Used the Banking data from Kaggle which is about 1GB in size with > 1M records.
Text Mining and Machine Learning Based Prediction of Drug-Drug Interactions [Python, BeautifulSoup, Tableau, R]:
Description: Extracting the textual facts of drug metabolism to define the major drug-drug interactions and using this data to build models such as Linear Regression, SVM, Decision Trees to explain the known DDIs and uncover new DDIs.
Analogy prediction using Word Embeddings [Python, NLTK, Glove, word2vec]:
Description: Implemented an analogy prediction method using pretrained word embeddings and compared the accuracies for several analogy tasks.
Convolutional Neural Network for Image Classification [Python (Keras library, scikit-learn)]
Description: Implemented a Convolutional Neural Network that differentiates between the pictures of cats and dogs.
Contact this candidate