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Introduction to Data Science; Data Acquisition; Data Pre-processing; Exploratory Data Analysis (EDA); Probability & Statistics for Data Science; Introduction to Machine Learning; Data Visualization Techniques and Big Data Concepts; Practical Data Science Workflow and Applications; Case Study 1: Student Performance Analysis Using Educational Data; Case Study 2: Customer Segmentation for Retail Business Using Clustering; Case Study 3: House Price Prediction Using Regression Analysis; Case Study 4: Spam Email Detection Using Classification Techniques; Case Study 5: Smart Agriculture Using IoT Data Acquisition; Success Story 1: Netflix Recommendation System for Personalized Content Delivery; Success Story 2: Google Maps Traffic Prediction Using Data Analytics; Success Story 3: Data-Driven Healthcare for Early Disease Detection; Success Story 4: Amazon Product Recommendation System; Success Story 5: Predictive Maintenance in Smart Manufacturing Using Data Science.
This course introduces the fundamentals of Data Science, covering the complete data science lifecycle, including data collection, pre-processing, exploratory data analysis (EDA), statistical analysis, data visualization, and machine learning techniques. Learners will explore methods for acquiring, processing, analyzing, and interpreting data to generate meaningful insights and support evidence-based decision-making.
The course emphasizes the application of Data Science in Agriculture and Allied Sciences, including agrometeorology, plant breeding and genetics, animal breeding and genetics, agronomy, supply chain management, post-harvest management, and agricultural market studies. Through practical workflows, real-world case studies, and success stories, learners will understand how predictive analytics, classification, regression, clustering, and visualization techniques are used to solve agricultural challenges. The course also introduces emerging technologies such as Big Data, IoT-enabled data acquisition, and Artificial Intelligence to demonstrate the growing role of Data Science in sustainable agriculture and modern digital ecosystems.
