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

Supplementary Material

+Foundations of Data Analysis Using Excel10 sections

Not part of the 23-topic syllabus. A hands-on Excel supplement, covering the spreadsheet work that precedes the R toolkit.

·Basics of Excel. Interface, Cell Referencing, Shortcuts, Tables, Data Organization ·Essential Formulas and Functions. Aggregation, SUMIFS, Logical, Text and Date Functions ·Data Cleaning and Preparation. Nine Common Defects and How to Fix Each ·Lookup and Reference Functions. VLOOKUP, INDEX and MATCH, XLOOKUP ·Sorting, Filtering and Conditional Formatting. SUBTOTAL, Advanced Filter, Dynamic Arrays ·PivotTables and PivotCharts. Field Zones, Grouping, Slicers, Calculated Fields ·Charts and Visualization. Choosing a Chart, Trendlines, Histograms, Sparklines ·Statistical Analysis with the Analysis ToolPak. t-Tests, z-Test, ANOVA, Correlation, Regression ·Power Query and Power Pivot. Applied Steps, Unpivot, Merge and Append, Data Model ·From Excel to R. readxl, Function Translation, the Same Analysis Both Ways

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.