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Data Science with less than a year in Agricultural Statistics and practical skills in Python, R, and
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven Data Analyst fresher with a strong academic foundation in Agricultural Statistics. Experienced in applying Python, R, and Machine Learning to real-world agricultural datasets. Passionate about transforming raw data into actionable insights and contributing to data-driven decision-making.
Tamil Nadu Agricultural University (TNAU), Coimbatore
M.Sc. Agricultural Statistics · Agricultural Statistics
November 1, 2024 – Present
Tamil Nadu Agricultural University (TNAU), Coimbatore
B.Sc. (Hons.) Agriculture · Agriculture
February 1, 2020 – September 1, 2024
Crop Yield Prediction using Machine Learning
June 1, 2026 – Present
Built and compared ML models (Random Forest, Gradient Boosting, Decision Tree, Linear Regression) to predict crop yields from historical agricultural data. Performed full data pipeline: collection, cleaning, feature engineering, model training & evaluation using Python (Scikit-learn) and R. Conducted EDA using Matplotlib, Seaborn, and ggplot2 to identify patterns, correlations, and seasonal trends. Applied ANOVA, correlation analysis, and experimental design methods to validate model assumptions. Visualized predictions and model metrics (RMSE, R2, MAE) in Power BI and Excel dashboards.
Data Analysis using Python
Swayam / NPTEL (Govt. of India certified)
June 1, 2026 – Present
Statistics for Data Science
Swayam / NPTEL
June 1, 2026 – Present
Kaggle Micro-courses: Pandas
Kaggle
June 1, 2026 – Present
Kaggle Micro-courses: Data Visualization
Kaggle
June 1, 2026 – Present
Kaggle Micro-courses: Intro to ML
Kaggle
June 1, 2026 – Present
Kaggle Micro-courses: Intermediate ML
Kaggle
June 1, 2026 – Present
Data Analyst with Python track
DataCamp
June 1, 2026 – Present
Copyright applied for website developed for statistical analysis – TNAU AG STAT
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Agricultural Statistics and the focus of their research project on crop yield prediction demonstrate a strong alignment with data science roles, particularly those involving domain-specific applications. The breadth of skills across Python, R, various ML algorithms, and visualization tools indicates a willingness to learn and adapt. However, the lack of diverse project types beyond academic research and the absence of professional experience might limit their exposure to varied team dynamics and corporate cultural environments. The ongoing nature of their master's degree and certifications suggests a continuous learning mindset, which is a positive cultural indicator.
Soft Skills & Operational Fit
The candidate's psychometric test score of 320/500 suggests average performance in areas like logical reasoning, work attitude, stress handling, and team collaboration. While not explicitly weak, it indicates potential areas for development in operational fit and soft skills required for a senior role. The English test score of 62/100 indicates moderate communication clarity, which could impact professional language usage and overall team interaction.