RAMYA YEDDLA

adyj9i@r.postjobfree.com 573-***-**** Cerritos, California https://www.linkedin.com/in/ramya-reddy-yeddla EDUCATION

Master of Science in Information Science and Technology May 2023 Missouri University of Science and Technology, Rolla, MO, US GPA: 3.8/4.0 Certification: Business Analytics and Data Science Coursework: Data Science and Machine learning with Python, Business Analytics and Data Science, Machine Learning Algorithms and Applications, Information Visualization, Foundations of Data Management, Information Retrieval and Analysis. Bachelor of Technology in Computer Science and Engineering Sep 2020 JNTU, Hyderabad, India GPA: 3.26/4.0

Coursework: Data Analytics, Python, DBMS, Java, Web Technologies, Data Structures, Mathematics, Cloud Computing. TECHNICAL SKILLS

Programming: Python, PHP, R, C, C++, Java, C#.

Web Technologies: HTML, CSS.

Database: MySQL, Oracle RDBMS, MS SQl, PostgreSQL. Libraries: ggplot2, dplyr, NumPy, Pandas, Matplotlib, Scikit-learn, Caret, Seaborn. Tools, Frameworks & Cloud: Jupyter, Anaconda, Django, Visual Studio Code, RStudio, Microsoft Excel, SSMS, SSRS. Visualization Tools: Power BI, Tableau, SAS.

WORK EXPERIENCE

Graduate Teaching Assistant - Missouri University of Science and Technology Aug 2022 – May 2023 Web and Digital Media Development

• Guided 3 cross-functional sections in the correction of errors in code and co-taught course concepts.

• Evaluated lab assignments and checked for syntax errors in code for a class size of 48.

• Managed proctoring and grading services for laboratory examinations and presentations. Commonwealth Rolled Products – Lewisport, Kentucky, USA (remote) May 2022 – Aug 2022 Business Analyst Intern

• Collaborated with cross-functional teams to identify critical KPIs for the business, leading to the creation of targeted reports that drove actionable decision-making.

• Created and optimized DAX queries to analyze daily statistical data for "CALP Production" and "Warm Data" during the production years of 2019-2021, resulting in a more efficient and accurate analysis process that saved 2 hours per day.

• Implemented data visualization techniques using Power BI to present complex statistical analysis findings to management, resulting in a better understanding of trends and potential areas for improvement, increasing productivity by 15%.

• Developed and executed testing plans to identify and fix bugs across 15 Power BI reports, resulting in a 70% reduction in report downtime. Validated SSRS reports, made necessary changes in query and delivered information to the deployment team.

• Created and maintained SQL scripts and stored procedures for accurate and consistent data extraction from SSMS. ACADEMIC PROJECTS & ACTIVITIES

Political Tweet Sentiment Analysis of 2020 US Elections

• Implemented data validation techniques that reduced the error rate by 20%, resulting in cleaner datasets that were easier to analyze, maintain data quality and interpret the data.

• Conducted extensive testing of two and three cluster methods in SAS Enterprise Miner, ultimately selecting the most efficient method which led to a 20% increase in model accuracy.

• Contributed to a team project analyzing social media engagement and sentiment, identifying key patterns and trends that informed the development of effective communication strategies. Predicting Parkinson’s Disease using ML Algorithms

• Collaborated with 3 people resulting in successful implementation of project to predict the motor and total UPDRS scores.

• Underwent through data preparation to handle outliers, negative values, dimension reduction resulted in 100% cleaned data and analyzed Normalized data based on principal component analysis producing 8 principal components.

• Evaluation of supervised, unsupervised and ensemble learning models achieved R squared value of 99%. Data Analysis of Crimes in Los Angeles

• Performed ETL process and feature engineered the data by cleansing data set of size 2,429,662. Rectified null values using “. Fillna ”, along with dropping duplicates, removing redundant variables, reduced dataset size to 711,396.

• Performed predictive analysis using Machine Learning models concerning performance metrics to obtain the most accurate possible machine learning model with an AUC score of 80% and visualized the data using Tableau. Modeling and Predicting Cyber Hacking Breaches

• Built a web application using modules namely Upload data, Access details, User Permissions and Data Analysis.

• Anatomized breach size, type of attack on URL, count of attack and developed a program for the project using Django, python for back-end and Wamp Server for Web Connectivity.

• Accomplished the positive dependencies between the incident inter-arrival time and breach sizes to estimate the frequency of the breach resulted in 71% of malware attacks in the year 2011.

Contact this candidate