Code
sample R code
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x+y[1] 8Business Analytics for Agriculture
Welcome
Business Analytics for Agriculture is a concise and practical guide designed to equip students with the knowledge and skills to strategically apply data science tools and techniques in the agribusiness sector. This course covers foundational concepts of data science, statistical methods, and machine learning, with a strong focus on their applications in agriculture.
The curriculum includes:
- Introduction to Data Science: Understanding its evolution, significance in agribusiness, and the role of data scientists, alongside key concepts like algorithms, big data, machine learning, artificial intelligence, and business analytics.
- Research Fundamentals and R Programming: Hands-on training in data manipulation, transformation, visualization, and analysis using R Studio. Key statistical methods like t-tests, ANOVA, and Chi-square tests are also covered.
- Machine and Deep Learning: Comprehensive exploration of supervised and unsupervised machine learning techniques, including regression models, clustering, decision trees, and neural networks. Practical applications in forecasting and IoT for agriculture are emphasized.
- Practical Applications: Real-world case studies focusing on descriptive, prescriptive, and predictive analytics using Excel, SPSS, and R. Topics include data visualization, regression models, forecasting, and decision analytics specific to agribusiness.
By the end, students are expected to emerge as competent professionals ready to leverage analytics for strategic decision-making in agriculture.
Inclusion of codes in this book
Throughout the book, the codes used for the analysis are included in this document as shown below. And the output of each code is given below the code.
About the Author
Vijayakumar P is a skilled data analytics & data science professional with a deep academic foundation and practical experience. He excels in a variety of programming languages and tools essential to the field, such as R, Python, Advanced Excel, Tableau, and SPSS. This combination of skills enables him to efficiently solve complex data problems and contribute to advancements in data analysis.
Known for his systematic approach and insightful analyses, Vijayakumar consistently enhances data-driven decision-making processes. His dedication to precision and his ability to harness technology elevate his work, driving innovation and understanding in every project he undertakes.
For inquiries or more information, contact Vijayakumar at Linkedin.
Reference books
Statistics for Management (Levin, Rubin)