Data Science Intern

Phalguna Ravuri

Dallas, Texas ********.******@*****.*** 813-***-****

https://www.linkedin.com/in/phalguna-rjs/

http://www.phalgunaravuri.com

EDUCATION

The University of Texas at Dallas Expected Aug 2021 M.S., Information Technology Management (Dean’s Excellence Scholarship) Coursework: Programming for Data Science (Python), Advanced Business Analytics (R), Predictive Analytics (SAS), Data Visualization, Statistics Software Analysis & Project Management, Organizational Behavior, Data Management, Operations Management Gayatri College of Engineering, Visakhapatnam Jun- 2013 B. Tech., Information Technology

Coursework: Data Structures & Algorithms, Data Communications, Web Technologies, Cloud Computing & Computer Networks TECHNICAL SKILLS

Machine Learning: Regression, classification, clustering, random forest, KNN, time series forecasting, Deep Learning (CNN, RNN) using TensorFlow (Keras) Analysis Tools: Tableau, Power BI, advanced Microsoft Excel, Google Analytics Technologies: Java, PL/SQL, Python, R, SQL, Spark, Hadoop, SAS Statistical tools: Statistical testing, Hypothesis testing, inferential Statistics Data Science tools: TensorFlow, Scikit-Learn, Pandas, NumPy, NLTK, OpenCV Skills: Linear & Logistic regression, Random forest, XGBoost, ARIMA, ETS, LDA, RNN, NLP PROFESSIONAL EXPERIENCE

SkillBanc, VA

Intern, Data Science

• Predicting stock price using sentiment analysis model, Finding the stock market daily patterns, creating a dashboard to identify patterns.

• Identifying Multi bagger stocks using machine learning and AI

• Developed Artificial intelligent stock recommender tool using Deep Neural Networks and classification Algorithms

• Used the AWS Sage Maker, EC2, ELB, S3, IAM to quickly build, train and deploy the machine learning models

• Used Text Mining and NLP techniques find the sentiment about the stocks, Applying NLP and text-based extraction techniques Qualcomm, Hyderabad, India (PBM)

Senior Systems Engineer, June 2015 – Mar 2018

• Led a team of 5 members to develop statistical models and implement analytics, & analysis on feedbacks and other text inputs for use in internal audit

• Created financial reporting and analysis to provide insights into 3rd party expenses and track and monitor costs per call and other financial data

• Lead, supported and contributed to global initiatives and projects

• Developed smart, compelling dashboards and reports to track key performance indicators (KPI’s), identify trends and monitored company initiatives

• Segmented device complaints using techniques to extract features reducing 50% of manual work of audit team

• Defined, prepared & executed data loading tasks to prepare for regularly scheduled audits and root cause analysis as required by the Engineering team Systems Engineer Dec 2013 – June 2015

• Devised bench tests for evaluation of parametric performance of cellular based SOC’s, implemented bench level screening procedures by using programmable test equipment. Monitored and managed website modifications, performed administrative tasks such as completing service tickets, provided direct assistance & training to users in operations of systems, customize SharePoint site

• Supported cloud & hybrid products Cisco WebEx, Jabber IM (cloud), CMR, WebEx teams Services along with conference endpoints connected to the cloud, provided highest level technical support to help resolve sophisticated user problems from on prem to cisco cloud ACADEMIC PROJECTS

Data Model in SQL and Mongo Db (Data Management project) Sept 2019- Dec 2019

• Conceptualized & developed data model to provide key statistics to predict which states were most affected by forest fires in the US by cleansing spatial data and creation of entity-relationship diagrams Conjoint Analysis for a brand endorsement company (Predictive Analytics project) Jan 2020 – May 2020

• Received customers reviews from 1000 people with over 50 questions each. Extracted useful insights from the data by dividing into groups for personalized campaigns

• Performed various EDA techniques & conjoint analysis to model actual market decisions for predicting respondent preferences Olympic Medal Prediction using Python (Data Science project) Sept 2020 – Dec 2020

• Scraped data from various websites to gather 35,000 rows of data about Olympics wins, GDP rate, athlete details of various countries to predict number of medals each country will earn in the following Summer and Winter Olympics

• A separate dataset was extracted from World Bank that included country development indicators to use in regression analysis

• Performed feature selection, data operations, logistic regression, K nearest Neighbors, Random Forest Regression and cross validation to get a model with 76% accuracy.

• Feature importance was performed to hypothesize important indicators for quality analysis and prediction Text Sentiment Analysis (NLP project) Sept 2020 – Dec 2020

• Performed Sentiment Analysis on Amazon customer reviews. Used Beautiful soap to remove all HTML tags and special characters, numbers using re,

• Tokenized reviews and performed stemming using Porter Stemmer and removed all stop words

• Applied various featurization techniques like TFIDF vectorizer to convert texts into vectors and was able to retain 90% of the data there by extracting top 10 keywords from word cloud

• Finally, modelled data using Hyperparameter Tuning to get a weighted score of 89 and used confusion matrix to find out the optimum model

Contact this candidate