Block I: Introduction

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Unit I: Introduction to Data Science

1 Introduction to Data Science

• Overview of Data Science
  • Introduction
  • Evolution of data science
• Role of a Data Scientist
  • Work profile of a data scientist
  • Career in data science
  • Nature of data science
  • Typical working day of a data scientist
• Significance of Data Science in Agribusiness
  • Importance of data science in agribusiness

2 Fundamentals of Analytics, Intelligence, and Machine Learning

• Fundamentals
  • Algorithm
  • Big data
  • Business analytics
  • Statistical learning
  • Machine learning
  • Artificial intelligence
  • Data mining
  • Difference between analysis and analytics
  • Difference between business intelligence and business analytics
• Process of Business Analytics
  • Typical process of the business analytics cycle

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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.

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