Rahul Deshmukh SRM College, Kudal Moodle-MOOCs

This practical course introduces participants to the creation, management, and delivery of Massive Open Online Courses (MOOCs) and digital learning content using the Moodle Learning Management System (LMS) hosted through Gnomio.com. Gnomio provides educators with a Moodle-based online learning environment where they can create virtual classrooms, organize courses, upload learning resources, conduct assessments, and monitor learner progress.

Key Topics:

• Introduction to MOOCs, e-content, LMS, and Moodle
• Creating a free Moodle site using Gnomio.com
• Configuring the Moodle dashboard and course settings
• Creating and organizing course sections and learning modules
• Developing e-content using text, PDFs, presentations, images, audio, and video
• Adding Moodle resources and activities
• Creating quizzes, assignments, discussion forums, and interactive activities
• Enrolling learners and managing users
• Using completion tracking and learner progress monitoring
• Conducting online assessments and generating reports
• Designing a complete sample MOOC course

Learning Outcome:
By the end of the course, participants will be able to create and manage a structured online course, develop digital learning materials, integrate assessments and activities, and deliver a complete MOOC through a Moodle platform hosted on Gnomio.com.

Algorithm and Basic Python Programming form the cornerstone of computer science and software development. Algorithms offer structured methods for solving problems efficiently, while Python provides an intuitive and flexible programming language to implement these methods. Mastering algorithms leads to optimized, high-performance software solutions, and Python’s clarity and simplicity make it an ideal language for writing and testing these solutions.

Algorithms are widely used in areas such as sorting, searching, graph theory, cryptography, and machine learning. At the same time, Python plays a crucial role in web development, artificial intelligence, data science, and process automation—demonstrating its broad utility. For those who enjoy logical thinking and strategy, understanding algorithms is both intellectually rewarding and practically valuable. Python’s beginner-friendly syntax makes it especially appealing to newcomers and experienced developers alike.

Developing proficiency in both algorithm design and Python opens up a wide range of career opportunities. Professionals with these skills can excel as software developers, data analysts, algorithm engineers, machine learning specialists, and research scientists. In essence, gaining expertise in these areas provides a solid foundation for success in the rapidly evolving world of technology and enhances one’s ability to solve real-world computational problems effectively.

About the course

The course covers the fundamentals of combinatorics and graph theory, as well as their applications. The study of graphs, trees, and networks is known as graph theory. Colouring of graphs, planar graphs, networking theory, applications of system of different representatives, and matching theory will all be explored. Combinatorics is the study of various ways for enumerating finite but massive sets. Applications of the principle of inclusion and exclusion, recurrence relations, generating functions, and equation-solving methods will all be covered.

Course Outcome

• Understand and apply the basic concepts of graph theory, including colouring of graph, planar
• graphs, networking theory, applications of system of distinct representative and matching theory
• Use permutations and combinations to solve counting problems with sets and multisets
• Compute a generating function and apply them to combinatorial problems
• Set up and solve a linear recurrence relation and apply the inclusion/exclusion principle

AComputing with Python and Doing Mathematics with Python are closely related yet distinct ways of using Python in computer science and mathematics. Python is widely used in both areas because of its simplicity, flexibility, and rich library support. Its readable syntax makes it ideal for expressing both computational procedures and mathematical ideas. In computing, Python is commonly applied to software development, automation, and data handling. In mathematics, it supports numerical computation, symbolic processing, and graphical visualization. Applications of mathematical computing with Python include numerical methods, statistical modeling, and scientific computing. Skills in Python programming are highly valued in the technology sector, while computational mathematics expertise is increasingly in demand in research and data-driven fields. Together, these complementary skills prepare individuals for diverse and rewarding careers across technology, data science, and academia.

This course is designed to provide a comprehensive introduction to the Java programming language. Java is a versatile, high-level, and object-oriented programming language widely used in various applications, including web development, mobile applications, and large-scale enterprise systems. This course will cover the fundamental concepts of Java, including basic syntax, data types, control structures, object-oriented programming (OOP), and key features such as inheritance and exception handling. Additionally, students will be introduced to basic graphics programming to create simple graphical applications. Whether you are new to programming or transitioning from another language, this course will equip you with the knowledge and skills to develop basic Java programs and understand core programming principles.

About this Course

Are you ready to dive into the world of data Science? You will need a understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of database that are appropriate for the kind of data you are working with and what processing the data requires.

In this course, you will learn the basic concepts about relational databases and Relational Database Management Systems (RDBMS). You’ll study relational data models and discover how they are created. You’ll be introduced to standard relational databases Oracle 10g. This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and practised them with tables. No prior knowledge of databases or programming is required. 

Course Outcome 

• Fundamental Relational Database concepts. Types of RDBMS objects. Popular database systems.
• Design a relational database and its schema. Create Entity Relationship Diagrams (ERD). 
• Work hands-on with Relational Databases such ORACLE 10g using command line and app.
• Create tables and load data. Use of SQL functions and executing SQL queries.

About this Course

Are you ready to dive into the world of data Science? You will need a understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of database that are appropriate for the kind of data you are working with and what processing the data requires.

In this course, you will learn the basic concepts about procedural extension language for SQL (PL/SQL). This course introduces students to PL/SQL, Oracle’s procedural extension language for SQL and the Oracle relational database with programming to handle data. You will explore the differences between SQL and PL/SQL. 

Course Outcome 

About the course

This course introduces you to programming and the Python programming language. Data management, conditionals, loops, variables, and functions, as well as lists and dictionaries, are all introduced to students. This course provides a review of the many tools available for writing and running Python, as well as an introduction to the Anaconda and SymPy packages with programming, to begin students working immediately. It also includes hands-on coding assignments that include developing custom functions, reading and writing to files, and using commonly used data structures. Because it digs deeper into certain important programming principles, this course may be more robust than some other beginner python courses.

Course Outcome

• To acquire programming skills in core Python and to understand why Python is a useful scripting language for developers.
• To learn how to use lists, tuples, and dictionaries, indexing and slicing to access data  in Python programs.
• To learn how to write loops and decision statements, how to write functions and pass arguments in Python.
• To develop the skill of designing Mathematical user Interfaces with SymPy package in Python

About Course

In mathematics, an ordinary differential equation (ODE) is a differential equation containing one or more functions of one independent variable and the derivatives of those functions. Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve those equations and interpret the solutions. This course focuses on Higher Order linear Differential Equations, Systems of First Order Linear differential equations and Numerical Solutions of Ordinary Differential Equations.

Course outcome

• Find the solution of higher-order linear differential equations and to solve the homogeneous and non-homogeneous linear differential equations with constant coefficients, method of undetermined coefficients and method of variations of parameters.
• To solve systems of linear differential equations, determine the type of a linear differential equation systems.
• Use the operator method to solve linear systems with constant coefficients, solve the linear systems in normal form and to solve the homogeneous linear systems with constant coefficients.
• Demonstrate understanding of common numerical methods and how they are used to obtain approximate solutions, 
• Derive numerical methods for various mathematical operations and tasks, such as Taylor's series method, Picard's method for successive approximation and its convergence, Euler's method, Modified Euler's Method, Runge-Kutta methods and solving first order ordinary differential equation
• Derive Numerical solution of simultaneous and higher order ordinary differential equation using: Runge-Kutta fourth order method for solving simultaneous ODE and Finite difference method for the solution of two point linear boundary value problem.

About the course

The course covers the fundamentals of combinatorics and graph theory, as well as their applications. The study of graphs, trees, and networks is known as graph theory. Colouring of graphs, planar graphs, networking theory, applications of system of different representatives, and matching theory will all be explored. Combinatorics is the study of various ways for enumerating finite but massive sets. Applications of the principle of inclusion and exclusion, recurrence relations, generating functions, and equation-solving methods will all be covered.

Course Outcome

• Understand and apply the basic concepts of graph theory, including colouring of graph, planar
• graphs, networking theory, applications of system of distinct representative and matching theory
• Use permutations and combinations to solve counting problems with sets and multisets
• Compute a generating function and apply them to combinatorial problems
• Set up and solve a linear recurrence relation and apply the inclusion/exclusion principle

About the Course

Partial Differential Equations (PDEs) appear as mathematical models for many a physical phenomena. Closed-form solutions to most of these PDEs cannot be found. One of the possible ways to understand the models is by studying the qualitative properties exhibited by their solutions. In this course, we study first order partial differential equations in two independent variables, Semilinear and Quasilinear equations in two independent variables, method of characteristics, the Characteristics Cauchy Problem and its solutions as well as Non-linear equations in two independent variables including Monge Strip and Charpit Equations, Solution of Cauchy problem, Determination of Complete integral. After we Classifications of second order partial differential equations in two and more than two independent variables, method of reduction to normal form, the Cauchy problem. Potential theory and elliptic differential equations, boundary value problems and Cauchy problem, Poisson's theorem, the mean value and the Maximum-Minimum properties

Course Outcome

• Classify different types of PDEs
• Define different notions of solutions
• Analyze the properties of solutions of PDEs

About the Course

Partial Differential Equations (PDEs) appear as mathematical models for many a physical phenomena. Closed-form solutions to most of these PDEs cannot be found. One of the possible ways to understand the models is by studying the qualitative properties exhibited by their solutions. In this course, we study first order partial differential equations in two independent variables, Semilinear and Quasilinear equations in two independent variables, method of characteristics, the Characteristics Cauchy Problem and its solutions as well as Non-linear equations in two independent variables including Monge Strip and Charpit Equations, Solution of Cauchy problem, Determination of Complete integral. After we Classifications of second order partial differential equations in two and more than two independent variables, method of reduction to normal form, the Cauchy problem. Potential theory and elliptic differential equations, boundary value problems and Cauchy problem, Poisson's theorem, the mean value and the Maximum-Minimum properties

Course Outcome

• Classify different types of PDEs
• Define different notions of solutions
• Analyze the properties of solutions of PDEs

About the course

The course covers the fundamentals of combinatorics and graph theory, as well as their applications. The study of graphs, trees, and networks is known as graph theory. Colouring of graphs, planar graphs, networking theory, applications of system of different representatives, and matching theory will all be explored. Combinatorics is the study of various ways for enumerating finite but massive sets. Applications of the principle of inclusion and exclusion, recurrence relations, generating functions, and equation-solving methods will all be covered.

Course Outcome

• Understand and apply the basic concepts of graph theory, including colouring of graph, planar
• graphs, networking theory, applications of system of distinct representative and matching theory
• Use permutations and combinations to solve counting problems with sets and multisets
• Compute a generating function and apply them to combinatorial problems
• Set up and solve a linear recurrence relation and apply the inclusion/exclusion principle

About the course 

      This course introduces the fundamental concepts and techniques of numerical solution of algebraic equations, systems of algebraic equations, numerical solution of differentiation, integration, and their interrelationships, as well as their applications in engineering and science. It also develops problem-solving skills with both theoretical and computational oriented problems. It is crucial in the solution of different engineering and science challenges. This course also includes an advanced introduction to numerical analysis using Scilab Open Source Software, with a focus on the accuracy and efficiency of numerical algorithms.

Course Outcome:

1. Apply numerical methods to find our solution of algebraic equations using different methods under different conditions, and numerical solution of system of algebraic equations.
2. Apply various interpolation methods and finite difference concepts
   
3. Understand the main features of the SCILAB program development environment to enable their usage in the higher learning.
   
4. Implement simple mathematical functions/equations in numerical computing environment such as SCILAB.
   

About Course

In mathematics, an ordinary differential equation (ODE) is a differential equation containing one or more functions of one independent variable and the derivatives of those functions. Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE's) deal with functions of one variable, which can often be thought of as time. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve those equations and interpret the solutions. This course focuses on Higher Order linear Differential Equations, Systems of First Order Linear differential equations and Numerical Solutions of Ordinary Differential Equations.

Course outcome

• Find the solution of higher-order linear differential equations and to solve the homogeneous and non-homogeneous linear differential equations with constant coefficients, method of undetermined coefficients and method of variations of parameters.
• To solve systems of linear differential equations, determine the type of a linear differential equation systems.
• Use the operator method to solve linear systems with constant coefficients, solve the linear systems in normal form and to solve the homogeneous linear systems with constant coefficients.
• Demonstrate understanding of common numerical methods and how they are used to obtain approximate solutions, 
• Derive numerical methods for various mathematical operations and tasks, such as Taylor's series method, Picard's method for successive approximation and its convergence, Euler's method, Modified Euler's Method, Runge-Kutta methods and solving first order ordinary differential equation
• Derive Numerical solution of simultaneous and higher order ordinary differential equation using: Runge-Kutta fourth order method for solving simultaneous ODE and Finite difference method for the solution of two point linear boundary value problem.

About this Course

     This course provides an overview of Java syntax and how it differs from those of other languages, as well as an introduction to Java and object-oriented programming. This specialisation is for students with no prior programming experience who wish to learn Java programming skills as well as the underlying computer science concepts that will enable them learn other programming languages quickly. Students will learn how to write their own Java classes and methods, as well as unit test their code. After finishing this course, you will be able to create simple Java classes that exemplify the concept of encapsulation, import other classes for use, work with strings, print output, and apply sophisticated math functions. You'll also be able to arrange and access classes, as well as use some of the Java runtime environment's standard classes.

Course Outcome 

• Identify core aspects of object-oriented programming and features of the Java language.
  
• Develop programs that use Java collections and apply core object-oriented programming concepts using classes, polymorphism, and method overloading.
  
• Write, compile, and execute Java programs that may include basic data types and control flow constructs using Integrated Development Environments (IDEs) 
• Write, compile, and execute Java programs using arrays and recursion.
• Write, compile and execute Java programs using object oriented class structures with parameters, constructors, and utility and calculations methods, including inheritance, test classes and exception handling.

About this Course

Are you ready to dive into the world of data Science? You will need a understanding of how data is stored, processed, and accessed. You’ll need to identify the different types of database that are appropriate for the kind of data you are working with and what processing the data requires.

In this course, you will learn the basic concepts about procedural extension language for SQL (PL/SQL). This course introduces students to PL/SQL, Oracle’s procedural extension language for SQL and the Oracle relational database with programming to handle data. You will explore the differences between SQL and PL/SQL. 

• Basic PL/SQL block and programming concepts
• Conditionally control code flow (loops, control structures).
• Declare PL/SQL variables.
• Create anonymous PL/SQL blocks, stored procedures and functions.
• Describe stored procedures and functions.
  
• Work with different data types.
• Declare identifiers and trap exceptions.