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Data Information Technology

Location:
Charlotte, North Carolina, United States
Posted:
November 11, 2017

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Sai Koumudi Kaluvakolanu

ac29jp@r.postjobfree.com Cell: (980)-***-**** 12002, Apt M, Diploma Dr, Charlotte, NC, 28262

https://www.linkedin.com/in/sai-koumudi-kaluvakolanu-56815780

SUMMARY:

Highly motivated software professional with 1 year of experience in data analysis using R, Python, SAS, Tableau.

Provided data driven, action oriented solutions to challenging business problems.

Analyzed and processed complex data sets using advanced querying, visualization and analytical tools.

Provided technical leadership in a team that designed and developed analysis systems to extract meaning from large scale data.

Known for excellent troubleshooting skills, able to analyze code and engineer well researched, cost-efficient and responsive solutions.

Ability to pay attention to detail, self-motivated with good problem-solving skills, easily adaptable to new environments and a wonderful team player.

EDUCATION:

Master of Science in Computer Science, The University of North Carolina at Charlotte GPA: 4.0 Dec 2017

Bachelor of Technology in Information Technology, Sreenidhi Institute of Science and Technology GPA: 4.0 Jun 2016

TECHNICAL SKILLS:

Languages & OS: R, JSP, Php, Python, D3, Bootstrap, Java, Linux, Android, C, C++, HTML, CSS.

Tools & IDE: Spyder, Rstudio, Splunk, Eclipse, NetBeans, Photoshop, Informatica, Android Studio, Tableau, Postgres, Hadoop, Kafka, Zookeeper, Amazon AWS, Weka, Git, Apache tomcat, MySQL, MongoDB, ETL, Microsoft Access and Excel, Pandas, Splunk.

EXPERIENCE:

Spectrum – Technology Gateway Intern: May 2017 – Aug 2017

Contributed in code refactoring and building an internal web application (in JSF, CDI, LDAP) to find the employee details of all three companies (Charter Communications, Legacy Time Warner Cable, Legacy Bright House).

Used lambok, JXplorer and for every request, we hit all 3 active directories, gets a combined result.

Coign Technologies – Business Solutions Intern: Dec 2015 – Apr 2016

Involved in requirement gathering phase of SDLC with Business Analyst and framing them into user stories in the product backlog items, worked with Agile-Scrum methodologies.

Created SQL quires, triggers, views to interact with database. Documented the entire process and developed UML diagrams.

PROJECTS:

Movie Recommendations using MapReduce:

Implemented a MapReduce program (Java) in Hadoop which gives the list of movie recommendations for a given movie, based on movie ratings, cosine similarity and statistical correlation. https://github.com/saikoumudi/MapReduce-MovieRecommendations

Data Extraction & Classifier Identification:

Built our own dataset using JSoup library, considered a decision feature and discretized it to get highest precision, Used Weka tool and classifiers used are Naïve Bayes, Logistic regression, J48, Random forest. https://github.com/saikoumudi/KnowledgeBasedSystems

Data Visualization:

Created various interactive visualizations and Dashboards using D3, Tableau for Daimler Truck Data, tested, cleaned and standardized the data to meet business needs. https://github.com/saikoumudi/DaimlerTruckDataVisualization

Performance Analysis for Clustering techniques:

Modified and tested the code for 3 clustering algorithms, K-means, Spectral and Affinity Propagation clustering.

Analyzed the data related to Iris, Breast Cancer Diagnosis and Year Prediction. https://github.com/saikoumudi/Clustering-Algo

GUI based schema definition for different databases:

Contributed in integrating multiple databases (MySQL, Oracle on Apache Tomcat) so that, importing to and exporting from other databases will become easy. https://github.com/saikoumudi/GUI-for-different-databases-

Image Query Result Clustering

Applied Convolution Neural Networks for deep learning 100, 000 google images and applied AP clustering for optimal partition of returned images.

Twitter data analysis for sentiment analysis for US presidential elections 2016:

Sentiment analysis from twitter data and applying clustering algorithms and text analytics/mining with statistics using R programming.

Big Data Analytics for Workers’ compensation claims processing:

Data cleaning and Wrangling, Merging datasets, Insights, visualization and analysis of optimization of the cost and time involved in processing the compensation claims – SAS, Tableau.

Prediction of Hospital Re-admission:

Analysis of diabetic patient records to analyze the cause of re-admission and design a predictive model to predict the re-admission of a patient to the hospital – R Studio, Tableau.



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