Java Data

SUNISHA CHALASANI

*** ****** ***** ***** #*,Charlotte,NC-28262 ********@****.*** 704-***-**** linkedin.com/in/sunisha-chalasani-47695126

EDUCATION

Master of Science: Computer Science, Concentration in Data Science

University of North Carolina at Charlotte: GPA: 3.7/4.0 Aug 2016- Dec 2017

Bachelor of Technology: Computer Science Engineering

Gitam University, Visakhapatnam, India: GPA 8.68/10.0 Jul 2012- May 2016

EXPERIENCE

Instructor: DUKE TALENT IDENTIFICATION PROGRAM, DURHAM, US Jun 2017- Jul 2017

Instructor for ‘Computer Skills’ teaching the topics from Software Engineering, Python, Database Systems, Tableau and R programming to academically talented students.

Full Stack Developer: PEOPLE TECH GROUP, Hyderabad, India May 2015- Jun 2015

Developed Library Management system using Java, PHP, HTML, CSS, JSP and worked on MYSQL database. Tools: Eclipse IDE.

Data reporting Analyst: RELIANCE CORPORATION LIMITED, Visakhapatnam, India Mar 2015- Aug 2016

Business data is managed by performing data sorting, computing and reporting the results using Excel. The information of each vehicle is stored in database using MySQL and various queries are performed on this database.

TECHNICAL SKILLSS

Programming languages- C, C++, Java, Python, R

Big Data Ecosystems- Hadoop, MapReduce, HDFS, PySpark

Scripting languages- JSP and Servlets, PHP, JavaScript, XML, HTML, CSS

Databases- MYSQL Workbench, Oracle, MongoDB

Analytical Tools- SAS Enterprise Guide, Tableau, Weka

Web Server- Tomcat

Tools- Eclipse, PyCharm, NetBeans, Visual Studio, Android Studio, Excel, Matlab

PROJECTS(https://github.com/sunishac)

Cebordine: A web application is developed for user to order food from multiple restaurants at the same time.

Tools and Technologies: HTML, CSS, Java Script, Node JS and MySQL Workbench.

venObuy: A web application is developed where the customers can buy or sell their products from the store and the admin can accept the items and can add or delete items from the store.

Tools and Technologies: Java, HTML, CSS, JSP, Servlets, MySQL workbench and AWS.

Make a trip: A mobile application is developed in android studio implementing intents, threads, JSON Parsing, Firebase and Google Maps API where a user can plan a trip with other users, can accept or delete friends; can discuss about the trip in a chat, share images and also can show the round trip map in google maps. Tools and Technologies: Java, xml, Android Studio.

The Games DB: A mobile application is developed implementing intents and XML Pull parsing on games API where a user can search and find similar games based on the games API. Tools and Technologies: Java, xml, Android Studio.

Trivia quiz: A trivia quiz mobile application is developed which allows user to take the quiz and shows results, this is implemented using intents, Async task and JSON Parsing. Tools and Technologies: Java, xml, Android Studio.

iTunes Top paid Apps: The iTunes API is parsed using JSON parsing and the top paid apps are displayed using a recycler view. These top paid apps are selected as favorites, refreshed and displayed in increasing and decreasing order using menu.

Tools and Technologies: Java, xml, Android Studio.

Weather App: A weather app is developed by parsing data from the API’s of the accuweather. Current climate details are displayed in all the required intents using shared preferences. Firebase database is used to save the cities and these saved cities are displayed using recycler view. All the required details in the intents are displayed taking data from the firebase database. Tools and Technologies: Java, xml, Android Studio.

Search querying using Hadoop MapReduce: A set of documents are taken, each terms term frequency; each documents document frequency and their term frequency- inverse document frequency (TF-IDF) are calculated. A query is taken, tokenized and the words of the query which match the words in the documents are given with respect to their TF-IDF scores.

Tools and Technologies: Java, Hadoop – HDFS, MapReduce.

PageRank on Wikipedia using Map Reduce: PageRanks of an input set of hyperlinked Wikipedia documents using Hadoop MapReduce is computed. This is implemented in several steps by creating link graph, processing PageRank, cleaning and sorting; each and every step is implemented using MapReduce paradigm. Tools and Technologies: Java, Hadoop – HDFS, MapReduce.

Hospital Readmissions project: After initial analysis through visualizations and statistical analysis of Hospital Readmission data of Diabetic patients, various predictive models like Radom Forest, KNN and Logistics regression were implemented to attain 89% accuracy. Tools and Technologies: R, Tableau, Microsoft Excel.

Analyzing Data with Spark: Two datasets are taken and multiple linear regression is implemented using python interface to Spark where the input data is in HDFS. Tools and Technologies: python, spark, Hadoop- HDFS.

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