ANUJ YADAV

Intern at Internship Studio, specializing in data analysis and visualization to support data-driven decision-making and reporting.

• Applied Python for efficient data manipulation and analysis, streamlining data processing workflows.
• Utilized Pandas library for comprehensive data cleaning, transformation, and analysis, ensuring data accuracy and readiness for reporting.
• Developed interactive dashboards and visual reports using Power BI, enhancing stakeholder comprehension of key performance indicators.
• Streamlined data handling processes by applying advanced MS Excel functions (VLOOKUP, HLOOKUP), significantly improving data management efficiency.
• Explored Tableau to visualize complex data trends, facilitating the presentation of actionable, data-driven insights for strategic decision-making.

Completed a 10-week vocational training program at Mahindra Excellence Centre, gaining practical expertise in agricultural machinery maintenance.

• Acquired hands-on expertise in the repair and maintenance of agricultural tractors, focusing on engines, hydraulic systems, and electrical components.
• Developed practical troubleshooting skills for complex mechanical and electrical issues, enhancing diagnostic capabilities in field conditions.
• Gained in-depth knowledge of front axle systems and their maintenance, contributing to comprehensive vehicle servicing and longevity.
• Applied preventative servicing protocols to optimize tractor performance and reliability, directly supporting operational uptime.

Conducted research on wheat irrigation needs using FAO CROPWAT 8.0. Analyzed 20 years of meteorological data to estimate evapotranspiration and effective rainfall. Determined net irrigation requirement (~173 mm) and gross irrigation requirement (~248 mm) for wheat. Optimized irrigation scheduling to improve water use efficiency and crop yield. Gained experience in agro-hydrological modeling, data analysis, and sustainable water management practices Conducted research on wheat irrigation needs using FAO CROPWAT 8.0 and CLIMWAT for climate data analysis. Analyzed 20 years of meteorological data to estimate evapotranspiration and effective rainfall. Determined net irrigation requirement (~173 mm) and gross irrigation requirement (~248 mm) for wheat using model simulations. Applied MATLAB for data distribution analysis, and used Python (Pandas, NumPy) & R for data preprocessing, visualization, and interpretation. Optimized irrigation scheduling to improve water use efficiency, crop yield, and sustainable resource management.

Highly motivated and detail-oriented student with a strong background in agricultural technology and a passion for sustainable solutions. As a key member of a project team, I gained hands-on experience in business development and project management by creating a model for a hydroponic micro-industry. key contributions include: Financial Analysis: Developed a comprehensive budget for a new venture, calculating an annual net profit of over ₹7 lakhs by managing costs related to raw materials, labor, electricity, and rent. Market Research: Conducted in-depth market analysis to identify specific target audiences, including commercial, institutional, and individual growers, and formulated strategies to reach them. Cost Optimization: Sourced affordable components and designed a scalable system, directly addressing the challenge of high initial investment and contributing to a profitable business model. Problem-Solving: Identified critical challenges within the hydroponics industry, such as supply chain issues and skill gaps, and proposed practical solutions. Sustainable Practices: Championed a business model that reduces water usage by up to 90% and promotes pesticide-free farming, demonstrating a commitment to environmental responsibility.

Published in International River-Basin Foundation (WCPI) – River Water User Association Researched crop water requirements and irrigation scheduling for wheat in Prayagraj using the FAO CROPWAT 8.0 model and CLIMWAT database. Analyzed 20 years of meteorological data (2000–2019) to estimate evapotranspiration, effective rainfall, and irrigation needs using the FAO Penman–Monteith method. Results showed a net irrigation requirement of ~173 mm and a gross irrigation requirement of ~248 mm for wheat. Applied MATLAB for data distribution analysis, and used Python (Pandas, NumPy) and R for data interpretation and visualization. Study demonstrated that optimized irrigation scheduling can enhance water use efficiency, reduce losses, and improve crop yield and quality, supporting sustainable water resource management.