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This course introduces the fundamentals of Data Science in Market Studies, focusing on the use of data-driven methods to understand agricultural markets, consumer behaviour, pricing patterns, sales performance, and market demand. Learners will explore the complete analytical workflow, including data collection, pre-processing, exploratory data analysis, statistical analysis, data visualization, and predictive analytics. The course emphasizes the application of Data Science to agricultural market data for generating meaningful insights and supporting evidence-based business and marketing decisions.
This course introduces the fundamentals of Data Science in Market Studies, focusing on the use of data-driven methods to understand agricultural markets, consumer behaviour, pricing patterns, sales performance, and market demand. Learners will explore the complete analytical workflow, including data collection, pre-processing, exploratory data analysis, statistical analysis, data visualization, and predictive analytics. The course emphasizes the application of Data Science to agricultural market data for generating meaningful insights and supporting evidence-based business and marketing decisions.
Through practical case studies, learners will examine customer segmentation and personalized marketing for agricultural e-commerce, market price trend analysis for tomato farmers, customer preference analysis for organic food products, and regional sales performance analysis of agricultural inputs. The course demonstrates how classification, clustering, regression, forecasting, and visualization techniques can be applied to identify market trends, understand customer preferences, forecast prices, evaluate sales performance, and develop effective marketing strategies.
