Syllabus

Block 1: Introduction

Unit I: Introduction to Data Science

1 Introduction to Data Science

2 Fundamentals of Analytics, Intelligence, and Machine Learning

Unit II: Fundamentals of Research

3 Fundamentals of R and R Studio

  • Fundamentals of R programming
  • Basics of R Studio interface
  • Overview of key R packages

4 Data Preparation and Transformation in R

  • Data manipulations and transformations
  • Handling missing values and imputations
  • Normalization and standardization
  • Creating and managing dummy variables

5 Data Visualization in R

  • Basics of 2D data visualization techniques
  • Introduction to 3D data visualization tools in R

6 Understanding the Machine Learning Analytical Cycle

  • Basic architecture of the analytical cycle in machine learning
  • Key components of an analytical process

7 Descriptive Analytics

  • Overview of descriptive analytics
  • Data manipulation and visualization
  • Measures of central tendency and dispersion
  • Measures of distribution and association

8 Inferential Statistical Techniques

  • Hypothesis testing: t-test and F-test
  • Analysis of Variance (ANOVA)
  • Chi-square test
  • Basic statistical modelling framework.

Block 2 Machine and Deep Learning

Unit I: Regression and Classification Models in Supervised Learning

9 Introduction to Supervised Learning

  • Basic framework of supervised machine learning
  • Overview of regression and classification models

10 Regression Models

  • Linear regression
  • Nonlinear regression
  • Multiple regression
  • Polynomial regression
  • Quantile regression

11 Advanced Regression Techniques

  • Lasso regression
  • Ridge regression
  • Stepwise regression

12 Classification Models

  • Logistic regression

Unit II: Advanced Machine Learning Methods and Unsupervised Learning

13 Advanced Techniques in Supervised Learning

  • Linear discriminant analysis
  • Principal component analysis (PCA)
  • Factor analysis
  • Support vector machines (SVM)
  • Naïve Bayes classifier
  • Nearest neighbors
  • Decision trees
  • Random forest
  • Ensemble methods

14 Model Validation and Improvement

  • K-fold cross-validation
  • X Gradient Boosting

15 Introduction to Unsupervised Learning

  • Basic framework of unsupervised machine learning
  • Concepts of clustering

16 Clustering Techniques

  • K-means clustering
  • C-means clustering
  • Hierarchical clustering

17 Advanced Topics in Unsupervised Learning

  • Hidden Markov models
  • Forecasting models:
    • AR (Auto-Regressive)
    • MA (Moving Average)
    • ARMA (Auto-Regressive Moving Average)
    • ARIMA (Auto-Regressive Integrated Moving Average)

Unit III: Deep Learning and Applications in Agribusiness

18 Introduction to Deep Learning

  • Basic framework of neural networks
  • Types of neural networks

19 Advanced Neural Network Techniques

  • Feedforward neural networks
  • Backpropagation
  • Recurrent neural networks (RNN)
  • Convolutional neural networks (CNN)
  • Reinforcement neural networks
  • Concurrent neural networks

20 Deep Learning Applications

  • Computer vision
  • Object detection and localization

21 Optimization Techniques

  • Gradient descent optimization for loss function
  • Regularization methods: L1 and L2

22 IoT and Agribusiness Applications

  • Introduction to IoT
  • Applications of deep learning in agribusiness

23 Practical Applications Using R Studio

  • Illustrations from agribusiness domains
  • Practical exercises in R Studio

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.