Farm to Market Mastery: From Data Analysis to Product Selection

The aim of “Market Mastery” is to empower farmer’s market participants with data-driven insights and practical strategies for success. It provides a deep understanding of market dynamics, enabling informed product selection and marketing decisions. This workshop equips individuals, regardless of experience, with actionable knowledge to thrive in the competitive farmer’s market landscape.

• Introduction to Farmer’s Markets: Provide participants with an understanding of the farmer’s market industry, its significance, and its role in local economies.
• The Purpose of the Study: Clarify the objectives and goals of the workshop, emphasizing the importance of data analysis in farmer’s market success.
• About the Datasets: Familiarize participants with the datasets related to farmer’s markets that will be used for analysis throughout the workshop.
• How to Analyze Farmer’s Market Data: Teach participants how to perform descriptive data analysis to identify patterns, trends, and insights relevant to setting up new farmer’s markets.
• K-Means Clustering: Introduce participants to the concept of K-Means clustering and its application in grouping similar farmer’s markets.
• Factors for Similarity: Discuss the factors that can be utilized to establish similarities between farmer’s markets, enabling clustering.
• Predicting Clusters: Explain how to predict clusters for new farmer’s markets and the importance of this process in location selection.
• Characteristics Selection: Guide participants on the study required to choose the characteristics and attributes of newly planned farmer’s markets effectively.
• Market Basket Analysis (MBA): Introduce participants to MBA, a powerful technique used in various industries, including retail, and explain its relevance to selecting products for farmer’s markets.
• MBA Concepts: Explore the fundamental concepts involved in MBA, such as association rules and support-confidence analysis.

Day 1: Farmer’s Market: Descriptive Analysis of the Data

• Introduction to Farmer’s Markets.
• The purpose of the study.
• About the datasets to analyze Farmer’s Markets.
• How to analyze the Farmer’s Market data to determine possible locations for setting up new Farmer’s Markets.

Day 2: Farmer’s Market: Creating Models

• How to use K-Means clustering to create clusters of similar Farmer’s Markets?
• What factors can be used to form similarities between Farmer’s Markets?
• Once clustered, how do we predict the clusters for new Farmer’s Markets?
• What study needs to be performed to select characteristics of the newly planned Farmer’s Market?

Day 3: Farmer’s Market: Selecting Products to be sold at the new Farmer’s Market

• What is Market Basket Analysis (MBA)?
• Where is an MBA used? Examples: Amazon, Netflix, any Retailer, any departmental store, etc.
• Concepts involved in MBA.
• How to apply for an MBA for Product Selection for the new Farmer’s Market?

Graduates, Post Graduates, Research Scholars, Academicians of Data Scientists, Data Science Students, and Machine Learning Enthusiasts., Industry Professionals of Agriculture and Food Industry and Entrepreneurs and Startups of Agriculture

• Data Analysis Skills: Participants will acquire skills in data collection, cleaning, and analysis, specifically related to farmer’s market datasets.
• Market Understanding: Attendees will develop a clear understanding of what farmer’s markets are and their significance within local economies.
• Data-Driven Decision-Making: Participants will learn how to analyze data effectively to make informed decisions, particularly related to setting up new farmer’s markets.
• Dataset Familiarity: Participants will become familiar with the datasets used for analysis in the workshop.
• K-Means Clustering Proficiency: Participants will gain proficiency in using K-Means clustering to group similar farmer’s markets based on chosen factors.
• Factors Identification: Attendees will understand how to identify and use factors that form similarities between farmer’s markets, enhancing their clustering accuracy.
• Predictive Modeling Skills: Participants will learn how to predict clusters for new farmer’s markets, aiding in strategic location selection.
• Characteristics Selection: The workshop will provide insights into the study required to select the characteristics and attributes of newly planned farmer’s markets effectively.
• Market Basket Analysis (MBA): Participants will gain a comprehensive understanding of MBA and its relevance in various industries, such as retail (e.g., Amazon), entertainment (e.g., Netflix), and departmental stores.
• MBA Concepts: Attendees will become familiar with the fundamental concepts involved in MBA, including association rules and support-confidence analysis.
• Practical Application: Participants will learn how to apply MBA techniques specifically for product selection at new farmer’s markets, ensuring that product offerings align with market demand.

Dr. Himadri B. Bohidar is currently associated with National Center for Excellence in Agro- nanotechnology TERI-Deakin Nanobiotechnology Center, Gurgaon, NCR. He received his Ph.D. Degree in Physics from Indian Institute of Technology, Delhi in 1981 and earned Academic Experience of 31 years in Teaching & Research. Then earned Administrative Experience in JNU for 8 Years. He is having 12 Years of Administrative Experience at National Level. His Area of Expertise includes Nanoscience and Nanotechnology, Nano-bio interface science, Soft Matter Science, Polymer Physics, Biophysics. He has 221(h index: 28) Publications in International Journals and author of 2 Books i.e., Polymer Physics and Molecular Biophysics: Cambridge University Press, 2014, UK, and Self-Assembly of Nanoparticles: Wiley- VCH, Berlin, Germany, 2017). He has filed 9 Patents (one granted). He has supervised 23 Ph.D. students in his career till now. Externally funded Projects Handled is of Rs. 207 lakhs (from DST, DBT and SERB).