I am a Business Analyst student at The University of Texas at Dallas with expertise in the field of data analytics, business intelligence, and machine learning. I am highly skilled at Python, SQL, Machine Learning and Tableau.
I combine technical ability with business acumen to provide insights and recommendations and generate added value to businesses and stakeholders. I am an effective communicator, highly adaptable and committed to continuous development.
Key Competencies:
- Leadership and Management
- Communication and Negotiation
- Adaptability and Flexibility
- Creativity and Innovation
Key Technologies and Methodologies:
- Python, Advanced SQL, Microsoft Excel, Tableau, Apache Spark, Adobe Analytics, Agile, Scrum, DevOps
- ML Packages: Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, SciPy, Keras, TensorFlow
Key Achievements:
Summer Internship at Austin Energy
- Managed a cross-functional team of 10 – 15 staff
- Automated the data acquisition team resulting in a 70% reduction in time
- Provided insight and recommendations to improve analytics the culture and enhance the mission to
excellent customer service
Software Engineer at Micro Focus
- Developed an application to monitor the performance of cloud management software
- Achieved 30% reduction on the number of defects logged in by the customers
Please contact me at namratha3012@gmail.com to find out how I might contribute and add value to your organization.
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Experience
Business Analyst Intern at Austin Energy
• Reduced the time spent in the data acquisition process by 70% through automation and scripting SQL queries to retrieve cross-functional data from different data sources like Oracle, Powerplan, and Excel
• Performed data modeling and explanatory data analysis and designed an interactive KPI dashboard with a balanced scorecard in Tableau to visualize the trends and insights of the company’s 14 high-level ISO metrics
Software Engineer at Micro Focus
• Developed an application with REST APIs to monitor the performance of a cloud management software using Java, Spring Hibernate framework, NodeJS, and MySQL server
• Helped the customers to gain insights on the uptime and availability of each node in a cluster environment and achieved a 32% reduction on the number of defects logged in by the customers
Software Engineer at Hewlett Packard Enterprise
• Developed 3 content packs for a release automation tool using the concepts of Continuous Integration and Continuous Development, REST API and DevOps to simplify the management of any cloud platform or infrastructure used by the client
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Projects
Analysis of Bitcoin Market Price
• Implemented regression models like Linear, KNN, Ridge, Lasso, SVM, Ensemble models with bagging and boosting technique, and built an artificial neural network model using Keras and TensorFlow to predict the market price of bitcoins in Python
• Optimized the performance of these algorithms using hyperparameter tuning to select the best parameters, studied the effect of dimensionality reduction using PCA and evaluated the model of Lasso Regression with the best accuracy of 95.63%
Informac, Data Science Challenge - Winner
• Predicted the health scores of restaurants in LA county dataset using random forest regression with an accuracy of 94.32%
• Identified the key factors impacting the health scores of the restaurant using random forest classification and analyzed the relationship between health code violations and the scores using the chi-squared test
Movie Box office predictions
• Feature engineered data such as genre, plot & reviews using Naïve Bayes Classifier for text mining and sentiment analysis
• Predicted the box office collection of movies for the year 2019 based on 20 years of historical data using random forest regressor with an accuracy of 86.3% in Python
Tourist pattern analysis and hotel recommendation
• Analyzed the migration patterns of customers based on their travel history and built a system to provide personalized hotel recommendation using random forest classification and association rules with an accuracy of 92.45%
• Studied the impact of advertising mediums on hotel bookings and forecasted the seasonal booking traffic for each country using ARIMA model with an accuracy of 88.20% in R programming language
Web Scraping of US Census Data
• Used the BeautifulSoup Python library to extract unconventional census data for top 8 populated cities in the USA
• Applied ETL process to sequence dataset with each city’s supplemental data like Government & racial composition information from their respective Wikipedia sites and structured the dataset to be uploaded to BigQuery table for interactive analysis
LINKViz 1.0, Data Visualization Challenge – Finalist
• Inspected the production of vanilla to identify trends & relationship in the yield of vanilla with regional weather conditions and average price over a span of 56 years
• Created a deep dive model to allow new businesses and consumers to interact with Tableau dashboard and get insights on vanilla harvest, cultivation and revenue trends around the world