Syllabus

The complete block-and-topic map of Business Analytics for Agriculture. Click any block to open its overview, or jump straight to a numbered topic — from data science foundations and the R toolkit through statistics, machine learning, deep learning and IoT applications in agribusiness.

Two Blocks · 23 Topics
IBlock I — Introduction8 topics
Unit I — Introduction to Data Science
1Introduction to Data Science — Overview, Role of a Data Scientist, Significance in Agribusiness 2Fundamentals of Analytics, Intelligence and Machine Learning — Algorithm, Big Data, Business Analytics, AI, Data Mining
Unit II — Fundamentals of Research
3Fundamentals of R and R Studio — R Programming, R Studio Interface, Key Packages 4Data Preparation and Transformation in R — Manipulation, Missing Values, Normalization, Dummy Variables 5Data Visualization in R — 2D and 3D Visualization Techniques 6Understanding the Machine Learning Analytical Cycle — Architecture and Key Components 7Descriptive Analytics — Central Tendency, Dispersion, Distribution and Association 7+Fundamentals of Statistical Tests — Hypothesis Testing, Errors, One- and Two-Tailed Tests, Power 8Inferential Statistical Techniques — t-test, F-test, ANOVA, Chi-square, Statistical Modelling
IIBlock II — Machine and Deep Learning15 topics
Unit I — Regression and Classification Models in Supervised Learning
9Introduction to Supervised Learning — Framework, Regression and Classification Overview 10Regression Models — Linear, Nonlinear, Multiple, Polynomial and Quantile Regression 11Advanced Regression Techniques — Lasso, Ridge and Stepwise Regression 12Classification Models — Logistic Regression
Unit II — Advanced Machine Learning Methods and Unsupervised Learning
13Advanced Techniques in Supervised Learning — LDA, PCA, Factor Analysis, SVM, Naïve Bayes, Decision Trees, Random Forest, Ensembles 14Model Validation and Improvement — K-Fold Cross-Validation, Gradient Boosting 15Introduction to Unsupervised Learning — Framework and Clustering Concepts 16Clustering Techniques — K-means, C-means and Hierarchical Clustering 17Advanced Topics in Unsupervised Learning — Hidden Markov Models, AR / MA / ARMA / ARIMA Forecasting
Unit III — Deep Learning and Applications in Agribusiness
18Introduction to Deep Learning — Neural Network Framework and Types 19Advanced Neural Network Techniques — Feedforward, Backpropagation, RNN, CNN, Reinforcement and Concurrent Networks 20Deep Learning Applications — Computer Vision, Object Detection and Localization 21Optimization Techniques — Gradient Descent, L1 and L2 Regularization 22IoT and Agribusiness Applications — Introduction to IoT and Deep Learning in Agribusiness 23Practical Applications Using R Studio — Agribusiness Illustrations and Exercises

Course Highlights

  • Hands-On Focus: The course emphasizes practical, hands-on learning through real-world case studies and interactive exercises, ensuring students gain applicable skills in data science and analytics.

  • Flexible Tool Assignment: Students will work with a variety of tools such as Excel, R, Python, and SPSS, providing flexibility and adaptability to different analytics platforms.

  • Comprehensive Scope: The curriculum covers a wide range of topics, from foundational data science concepts to advanced machine learning techniques, ensuring a well-rounded understanding tailored to the agribusiness sector.