Shreya Verma
Current Position
Data Scientist at Mphasis
Location: New York, NY, United States
Education
- Master's Degree in Statistics from Columbia University
Skills and Expertise
Shreya Verma is highly skilled in various programming and data science tools. Her expertise includes:
- Programming Languages: Python, R, SQL
- Data Science Tools: Extensive knowledge and experience in a wide array of data science tools, methodologies, and technologies.
Professional Highlights
- Data Scientist at Mphasis: Demonstrates strong capabilities in handling data science projects and utilizing advanced statistical methods.
- Student Representative for MA Statistics, Class of 2022: Appointed by Columbia University’s Department of Statistics, highlighting her leadership and commitment to her field.
Online Presence and Contributions
- LinkedIn Profile: [Shreya Verma - LinkedIn](https://www.linkedin.com/in/shreyaverma6)
- Notable Activities:
- Shared insights from "An Introduction to Statistical Learning with Applications in R".
- Published content on unsupervised learning and other data science topics.
- Engaged with her professional network through posts and articles, including notable interactions with peers and thought leaders in the industry.
Additional Information
- Podcaster: Engages in podcasting, sharing insights and discussions pertinent to data science and related fields.
- Community Engagement: Active in professional communities, often participating in discussions and contributing to collective knowledge bases.
Shreya Verma’s profile illustrates a well-rounded data science professional with strong academic credentials and practical experience in the field. Her role at Mphasis positions her as a valuable asset in data-driven decision-making and advanced statistical analysis. Her proactive involvement in the community and content creation underscores her commitment to advancing in her field. This combination of skills, experience, and professional engagement suggests significant potential for collaboration and value generation in any data-centric project or initiative.
