Statistics Computer

Learn what intellectual property is, the main types of IPR, and why intellectual property matters in agriculture and innovation.

Learn the basic coordinate geometry of straight lines, slope, and standard equation forms used in applied mathematics.

Lecture notes covering Statistical Methods as per ICAR 5th Dean Committee syllabus. Course Code: STAM 101 | Credits: 2(1+1).

Learn what statistics studies, how data are collected, and how primary, secondary, qualitative, and quantitative data are classified.

Learn what a computer is, how input-processing-output works, and the main hardware parts of a basic computer system.

Patents.

3D Analytical Geometry.

Lec 02.

Lecture notes covering Agri-Informatics as per ICAR 5th Dean Committee syllabus. Course Code: STAM 102 | Credits: 2(1+1).

Units of Memory, Hardware.

Plant Variety Protection.

Bionomial.

Lec 03.

Personal Computers, Types.

Lecture notes covering Intellectual Property Rights as per ICAR 5th Dean Committee syllabus. Course Code: STAM 104 | Credits: 1(1+0).

Copyright and Trademark.

Conic sections.

Lec 04.

Computer Viruses, Worms &.

Geographical Indications.

Differential Calculus(1).

Lec 05.

Operating System — Some.

Trade Secrets.

Differential Calculus(2).

Lec 06.

WINDOWS: GUI, Desktop &.

Understand how patents, biodiversity rules, and benefit-sharing issues intersect with agricultural biotechnology.

Differential Equations.

Lec 07.

MSWORD: Word-processing.

International Agreements.

Equation of a circle.

Lec 08.

MSEXCEL: Electronic.

Increasing & Decreasing.

Understand null and alternative hypotheses, standard error, critical region, decision errors, and the logic of statistical significance testing.

Use of in-built functions.

Integration.

Lec 10.

Data Analysis Tools-.

Understand when a matrix has an inverse, how to find it by the adjoint method, and how inverse matrices help solve simultaneous equations.

Lec 11.

t-test for two samples.

Matrix & Determinants.

Lec 12.

Creating Graphs.

Maxima & Minima of several.

Learn the meaning of regression, fitting a simple linear regression equation, and testing the significance of the regression coefficient.

Understand the concept of a database, the main components of a DBMS, and how MS Access is used to create and manage simple databases.

Models.

Lec 14.

Principles of Programming.

Core BSc Agriculture modules covering data analysis, agri-informatics, applied mathematics, and intellectual property rights for academic and competitive-exam preparation.

Partial Differentiation.

Lec 15.

Internet.

Permutation & Combination.

Lec 16.

Learn the basics of web browsing, browsers, search tools, email structure, and safe digital communication.

Learn the difference between physical optimum and economic optimum for a single input response function.

Latin square design.

Introduction to AI concepts, techniques, and applications in modern agriculture — from crop disease detection to precision farming and chatbot advisory systems.

Population Dynamics.

Factorial experiments.

Complete ML workflow for agriculture — algorithms, deep learning, evaluation metrics, data sources in India, and Python tools for crop yield prediction and disease classification.

Progression.

2Square factorial experi.

Precision agriculture principles, IoT sensor systems, smart irrigation, drones with AI, blockchain for traceability, and India's digital agriculture infrastructure — AgriStack, e-NAM, and agritech startups.

Second order differential.

2cube factorial experim.

Solving Simulteneous.

Split plot design.

Supply & Demand.

Strip plot design.

Vector Algebra.

Long term experiments.

Lecture notes covering Applied Mathematics. Course Code: STAM 103 | Credits: 2(2+0).