Srinidhi A E
About Srinidhi A E
Srinidhi A E is a Data Analytics Virtual Intern currently working at KPMG India and has previous experience as a Data Science Intern at Yoshops.com. He holds a Bachelor of Engineering in Civil Engineering and a Post Graduate Program in Data Science and Business Analytics from Great Lakes Institute of Management.
Work at KPMG India
Currently, Srinidhi A E serves as a Data Analytics Virtual Intern at KPMG India. This role involves engaging in data analysis projects that support decision-making processes within the organization. The internship provides an opportunity to apply data science skills in a professional environment, contributing to various analytical tasks.
Current Role at Yoshops.com
Srinidhi A E is also working as a Data Science Intern at Yoshops.com. In this position, he is involved in data-driven projects that aim to enhance business operations and customer engagement. The internship allows him to further develop his skills in data science and analytics.
Education and Expertise
Srinidhi A E completed a Post Graduate Program in Data Science and Business Analytics from Great Lakes Institute of Management, achieving a GPA of 3.42 (81.44%). He also holds a Bachelor of Engineering in Civil Engineering from Visvesvaraya Technological University. His educational background equips him with a solid foundation in data science and analytical methodologies.
Professional Experience at Fastlane Information Technologies
Prior to his current internships, Srinidhi A E worked as an Associate Engineer at Fastlane Information Technologies Pvt. Ltd. from 2018 to 2022. During this four-year tenure, he gained practical experience in engineering and technology, contributing to various projects in Davangere, Karnataka, India.
Technical Skills in Data Science
Srinidhi A E possesses proficiency in various data science tools and techniques. He is skilled in using libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn for data analysis and visualization. He is also capable of implementing machine learning techniques, including Linear and Logistic Regression, Classification Models, Bagging, Boosting, and Clustering.