Sesha Sai Tumma
About Sesha Sai Tumma
Sesha Sai Tumma is a Data Scientist at Brillio in Seattle, Washington, with expertise in machine learning algorithms and data visualization tools. He has a background in data analysis and business technology from previous roles at Deloitte and Changing The Present.
Work at Brillio
Sesha Sai Tumma has been employed at Brillio as a Data Scientist since 2020. Based in Seattle, Washington, he has contributed to various projects over the past four years, applying his expertise in machine learning and data analysis to drive insights and solutions for clients.
Previous Experience in Data Analysis
Prior to his role at Brillio, Sesha worked at Changing The Present as a Data Analyst for two months in 2020, located in San Jose, California. He also held multiple positions at Deloitte, starting as a Business Technology Analyst for three months in 2015, followed by a year as a Data Analyst from 2016 to 2017, and then serving as a Senior Data Analyst from 2017 to 2019 in Hyderabad, India.
Education and Expertise
Sesha holds a Bachelor's degree in Computer Science from Osmania University, where he studied from 2012 to 2016. He furthered his education at Purdue University, obtaining a degree in Business Analytics and Information Management from 2019 to 2020. He possesses expertise in machine learning algorithms, data mining, and statistical methods.
Technical Skills and Tools
Sesha is proficient in various programming and data analysis tools. He utilizes Python libraries such as Numpy, Pandas, Matplotlib, Seaborn, and Scikit-Learn for data manipulation and visualization. He is skilled in SQL for database management, employing Oracle and MySQL, and uses R for scripting and creating interactive applications with R-Shiny. Additionally, he has experience with big data technologies like Hadoop and Hive.
Data Visualization and Analysis Techniques
Sesha demonstrates strong capabilities in data visualization using tools like Tableau and Power BI. He is experienced in data cleaning, manipulation, and exploratory data analysis, employing statistical methods such as ANOVA, Linear Regression, and Logistic Regression to derive meaningful insights from data.