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Data Analyst with less than a year in Machine Learning & Statistical Analysis
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Postgraduate student in Agricultural Statistics at TNAU with hands-on experience in ANFIS modelling, time series forecasting, and machine learning applied to real agricultural datasets. Proficient in Python, R, SQL, and Excel. Seeking a data analyst internship to apply statistical modelling skills to solve real-world problems.
Tamil Nadu Agricultural University
M.Sc. · Agricultural Statistics
January 1, 2024 – January 1, 2026
Sethu Bhaskara Agricultural College & RF
B.Sc. (Hons.) · Agriculture
January 1, 2020 – January 1, 2024
St. Joseph's Hr. Sec. School
Higher Secondary (12th)
January 1, 2016 – January 1, 2019
St. Joseph's Hr. Sec. School
Secondary School (10th)
January 1, 2016 – January 1, 2019
NoviTech R&D Pvt. Ltd.
Machine Learning, Data Analytics & AI
January 1, 2025 – March 31, 2025
India
Sakthi Sugars Limited
Field Exposure
May 1, 2024 – May 31, 2024
Sivaganga, Tamil Nadu, India
Rainfall Forecasting Using ANFIS
January 1, 2026 – Present
Developing univariate & multivariate ANFIS models on 35 years (1991–2025) of IMD rainfall data for 4 agro-climatically distinct districts of Tamil Nadu. Applying Mann-Kendall trend test & Sen's slope estimator to identify long-term rainfall variability across selected districts of Tamil Nadu. Evaluating model performance using RMSE, MAE, MAPE and R2 to identify the best-performing forecasting model for agricultural planning
Effect of Insect Frass on Crop Growth & Yield
January 1, 2026 – January 1, 2026
Conducted a comparative growth trial on tomato, marigold, and amaranthus across 9 treatments, identifying vermicompost as the top performer with 54.5 cm plant height in tomato vs 35.3 cm in control. Analyzed weekly growth data across 8 weeks tracking 4 parameters (plant height, leaf length, leaf width, leaf count) and found low-dose insect frass (50g-100g) outperformed high concentration doses which caused scorching. Estimated physico-chemical properties of insect frass including nitrogen (1.54%) and pH (7.6), concluding it as a viable organic fertilizer alternative at optimal concentrations
Data Analysis with R Programming
Google Coursera
June 1, 2026 – Present
Python for Data Analytics
Digi Skill Dev. Centre
June 1, 2026 – Present
Basic Statistical Analysis & Interpretation using SPSS
GISS Works
June 1, 2026 – Present
MATLAB & Simulink Training
TNAU
June 1, 2026 – Present
Introduction to SQL
Simplilearn
June 1, 2026 – Present
Multivariate Analysis using SPSS
GISS Workshop
June 1, 2026 – Present
Basic Statistics using R
SWAYAM (BlueSky)
June 1, 2026 – Present
Introduction to Data Analytics & SQL
Simplilearn
June 1, 2026 – Present
Business Analytics with Excel
Microsoft
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects are highly specialized in agricultural statistics, which aligns well with roles requiring domain-specific data analysis. The breadth of tools and certifications (Python, R, SQL, MATLAB, SPSS, Excel, Power BI) indicates a willingness to learn and adapt to various technical environments. The internships, though short, show initiative in gaining practical experience in ML, Data Analytics, and AI. The focus on agricultural data suggests a potential fit for organizations in related sectors or those valuing domain expertise in data analysis.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to conduct detailed analysis, track multiple parameters, and draw conclusions from data, suggesting good analytical and problem-solving skills. The academic background in agricultural statistics implies a methodical approach to research and data interpretation. However, the provided data does not offer direct insights into communication, teamwork, or leadership skills in a professional setting.