959-***-**** email@example.com Hartford, CT
GitHub: github.com/Lakshmiprasanna999 LinkedIn: linkedin.com/in/lakshmiprasanna-palla SUMMARY
Business Analytics graduate student with over 3 years of professional analytics experience at Oracle utilizing a wide variety of data transformation tools with an excellent understanding of business operations for effective data processing. EDUCATION
University of Connecticut School of Business Hartford, CT Master of Science in Business Analytics and Project Management, GPA: 4.0/4.0 December 2020 Coursework: Predictive Modeling, Statistics using R, Data Management, Big Data Analytics, Intro to Deep Learning National Institute of Technology Calicut Kerala, India Bachelor of Technology in Electronics and Communication Engineering, GPA: 8.32/10.0 August 2016 TECHNICAL SKILLS
Programming: Python, R, SQL
Databases: Oracle, MySQL, MS SQL Server
Tools: Tableau, SAS Suite (SAS Enterprise Guide, SAS JMP), MS-Excel, R Studio, MS Project, MS Visio Big Data Tools: Hadoop, HDFS, Apache Sqoop, Apache Pig, AWS Libraries: NumPy, SciPy, Pandas, Scikit-learn, matplotlib, Seaborn, BeautifulSoup, Keras, TensorFlow, NLTK Statistical Analysis: Linear Regression, Logistic Regression, Ridge and Lasso regression, K-Nearest Neighbors, Decision Trees, Random Forests, Bagging, Gradient Boosting, Support Vector Machines, Neural Networks, Principal Component Analysis, Clustering (K-Means, Hierarchical), Time Series Forecasting, Data Mining, Exploratory Data Analysis, Data Visualization, Inferential Statistics, Hypothesis Testing, A/B Testing, Google Analytics, Web Scraping, Natural Language Processing PROFESSIONAL EXPERIENCE
Graduate Teaching Assistant, University of Connecticut, Hartford, CT Course: Statistics using R Jan 2020 – Present
• Help professor with curation of course contents and assist 45 graduate students learning programming in R. Graduate Analytics Consultant, Trackso, University of Connecticut, Hartford, CT Technologies: Python, Tableau, R, SQL Oct 2019 – Present
• Responsible for predictive analysis of more than 20 solar plants by exploring energy production data of various sites, weather data from monitoring stations.
• Forecasting energy production for each solar PV system in future based on historical data of a site using Time Series Analysis. Data Analyst, Oracle India Private Limited, Bangalore, India Technologies: SQL, Tableau, HR Analytics, Oracle Transactional Business Intelligence Jul 2016 – Jul 2019
• Served as a Lead for Oracle’s largest client, helped them understand the impact of workforce turnover on employee performance and analyzed key HR metrics, thereby improving retention rate by 23%.
• Implemented end-to-end Fusion HCM application for multiple clients, engaged with more than 300 clients to provide analytical solutions that align with business needs and boosted Oracle customers’ satisfaction by 12%.
• Developed highly interactive OTBI reports and analyses using real-time transactional data to answer key business questions, analyzed historical trends and other KPIs which reduced customer’s resource expenditure by 15%.
• Examined data from across multiple workflows, applications and created visualizations to gain actionable insights for efficient data-driven decision making, resulting in higher productivity.
• Generated and validated custom extract definitions to assist clients in data retrieval from database, configured delivery options to ensure successful outbound integration.
• Developed complex SQL queries to analyze existing data and provided critical business insights and recommendations to clients by creating dashboards in Tableau.
• Responsible for workflows to extract, transform and load - ETL client data in application. ACADEMIC PROJECTS Aug 2019 - Present
• HR Analytics Employee Turnover: Predicted the Employee turnover by developing classification models like Logistic Regression, Random Forest, Decision Tree and Ada boost. Achieved AUC of 0.98 with Random Forest Classifier. Code on Github
• Credit Card Segmentation: Implemented K-Means clustering to identify credit card customer segmentation and to define marketing strategy. Created 4 clusters based on transacting behavior of credit card holders. Performed data standardization and applied PCA technique for dimensionality reduction. Code on Github
• Bike Sharing Demand: Predicted hourly count of rental bikes by performing exploratory analysis and building regression models like Linear Regression, Decision Tree, Random Forest Tree. Random Forest model was identified as best model with low RMSE. Code on Github
• Loan Data Visualization: Built a Tableau story on data with 1 M loans by exploring the relationship between loans and people acquiring them, reasons for taking out a loan and who defaults most based on occupation and income.