data analyst
Intellipaat Softwere solutions
Total years of experience :1 years, 9 Months
Applied Python expertise for real-world data analysis, machine learning, and deep learning applications.
- Demonstrated proficiency in managing missing data and outliers through statistical methods and tools like
NumPy, Pandas, and correlation analysis.
- Expertly conducted EDA (Exploratory Data Analysis) and crafted advanced data visualizations using Matplotlib and Seaborn.
- Developed diverse machine learning models, including Regression, KNN, K Means, Decision Trees, and Random Forests.
- Conducted primary and secondary research to inform strategic decision-making, producing insightful reports
using data analytics tools.
- Led comprehensive surveys for qualitative research, collaborating seamlessly with stakeholders to drive process improvements.
Led comprehensive primary and secondary research, utilizing advanced tools (Fulcrum, Prodgee, Purespectrum, Flamingo) for both quantitative and qualitative analysis.
- Managed end-to-end surveys using CATI, CAWI, PAPI, and CAPI methods, ensuring precise data collection
and analysis.
- Collaborated with stakeholders for effective client management, providing insights for market segmentation and industry analysis.
- Led quantitative analysis and data mining projects, employing logical reasoning and analytical skills to
interpret complex datasets.
- Executed market sizing and trend analysis with tools like Fulcrum and Prodgee, contributing to strategic
decision-making.
- Conducted competitive analysis, identifying emerging trends and key performance indicators for market
forecasting.
- Orchestrated and supervised end-to-end research projects, including survey management, designing, and
implementation.
- Employed quantitative and qualitative research methodologies for in-depth market and trend research.
- Utilized CATI, CAWI, PAPI, and CAPI methods, emphasizing critical thinking and independent thinking in project management.
Applied Python expertise for real-world data analysis, machine learning, and deep learning applications. – Demonstrated proficiency in managing missing data and outliers through statistical methods and tools like NumPy, Pandas, and correlation analysis. – Expertly conducted EDA (Exploratory Data Analysis) and crafted advanced data visualizations using Matplotlib and Seaborn. – Developed diverse machine learning models, including Regression, KNN, K Means, Decision Trees, and Random Forests. – Conducted primary and secondary research to inform strategic decision-making, producing insightful reports using data analytics tools. – Led comprehensive surveys for qualitative research, collaborating seamlessly with stakeholders to drive process improvements