FOUNDATIOS OF BUSINESS ANALYTICS (BCOM FIN)

Divya p FACULTY

FOUNDATOINS OF BUSINESS ANALYTICS       Module 1: Introduction to Business Analytics & Probability Business Analytics Why Analytics Types of Business Analytics Random Experiment Sample Space and Events Probability Estimation using Relative Frequency Algebra of Events Fundamental Concepts of Probability Axioms of Probability Joint Probability Marginal Probability Independent Events Conditional Probability Applications of Simple ProbabilityBaye's Theorem  Module 2: Theoretical Distributions Random Variables Probability Density Function (PDF) Cumulative Distribution Function (CDF) of a Continuous Random Variable Binomial Distribution Poisson Distribution Normal Distribution Chi-Square Distribution Student’s t Distribution F Distribution

Divya p FACULTY

   Programme: BBA Course Title: Decision Science Course Type: Core Course Semester: I Academic Level : 2020–20 Credits: 4 Lecture per Week: 4 Pre-requisites  No pre-requisites required Course Summary Various concepts of Operations Research Identification and development of business solutions Use of quantitative techniques helps students understand tools required to solve optimal problems in management Course Outcomes (CO) CO1: Develop an understanding of basic management science techniques and their role in managerial decision-making CO2: Help students formulate business situations into quantitative models for optimal decision-making CO3: Build capabilities in students for analyzing different business problems using management science and finding optimal solutions within constraints CO4: Enable students to apply techniques such as linear programming in managerial decision-making CO5: Apply techniques to business decision-making situations CO6: Develop mathematical models for real-life business and management problems Decision Science  syllabus  Module I – Introduction to Operations Research Meaning of Operations Research (OR) Scope of Operations Research Limitations of Operations Research Methodology of Operations Research OR Models Applications of Operations Research Importance of Ethics in Operations Research Linear Programming: Basic concepts Applications in management decision-making Mathematical formulation of Linear Programming Problems Graphical solution of Linear Programming Models Module II – Transportation and Assignment Problems Transportation Problem: Initial Basic Feasible Solution North West Corner Rule Vogel’s Approximation Method (VAM) Test for Optimality: Modified Distribution (MODI) Method Assignment Problem: Introduction Solution methods Hungarian Method Maximization in Assignment Problems Unbalanced Assignment Problems Travelling Salesman Problem

Divya p FACULTY

FOUNDATOINS OF BUSINESS ANALYTICS       Module 1: Introduction to Business Analytics & Probability Business Analytics Why Analytics Types of Business Analytics Random Experiment Sample Space and Events Probability Estimation using Relative Frequency Algebra of Events Fundamental Concepts of Probability Axioms of Probability Joint Probability Marginal Probability Independent Events Conditional Probability Applications of Simple ProbabilityBaye's Theorem  Module 2: Theoretical Distributions Random Variables Probability Density Function (PDF) Cumulative Distribution Function (CDF) of a Continuous Random Variable Binomial Distribution Poisson Distribution Normal Distribution Chi-Square Distribution Student’s t Distribution F Distribution

Divya p FACULTY

SUBJECT DETAILS Programme: BCA Course Code: BCA2CJ103 / BCA2MN102 Course Title: Numerical Analysis and Operations Research Type of Course: Major / Minor (B2) Semester: II Academic Level: 100–199 Credits: 4 Lecture Hours: 4 per week PRE-REQUISITES Understanding of algebraic concepts, equations, and inequalities Basic knowledge of derivatives and integrals COURSE SUMMARY Covers numerical methods and operations research Focus on error analysis and solution techniques Includes algebraic and transcendental equations Topics include interpolation, numerical integration Introduces linear programming and OR principles COURSE OUTCOMES  CO1: Solve algebraic and transcendental equations using numerical methods CO2: Apply interpolation and numerical integration techniques CO3: Understand fundamentals of Operations Research CO4: Solve Linear Programming Problems using optimization techniques CO5: Understand and solve transportation problems CO6: Solve assignment problems for optimal solutions MODULE I – NUMERICAL ANALYSIS I Errors in numerical calculations Sources of errors Solution of algebraic and transcendental equations Bisection method Method of false position Newton–Raphson method MODULE II – NUMERICAL ANALYSIS II Polynomial interpolationLagrange interpolation Newton’s forward difference interpolation Newton’s backward difference interpolation Numerical solution of definite integrals Simpson’s 1/3 rule Simpson’s 3/8 rule Trapezoidal method MODULE III – OPERATIONS RESEARCH I Introduction to Operations Research Definition, advantages, and limitations of OR Linear Programming Problem (LPP) Formulation of LPP Feasible solution and optimal Solution  Dual of LPP Graphical solution of LPP Simplex method Big-M method MODULE IV – OPERATIONS RESEARCH II Transportation problem Balanced and unbalanced transportation problems Finding basic feasible solutions Northwest corner method Least cost method Vogel’s approximation method Optimized (MODI) method Assignment model Balanced and unbalanced assignment problems Hungarian method for optimal solution MODULE V – OPEN ENDED MODULE (OTHER NUMERICAL METHODS) Any two additional methods to solve algebraic and transcendental equations Any two additional methods for polynomial interpolation Any two additional methods to solve definite integrals Any one additional method to solve LPP

Divya KK

This course is designed to help commerce students enhance their English communication skills, particularly focusing on professional oral and written communication, critical reading, presentation skills, and discipline-specific communication strategies. earning Outcomes (COs) After completing the course, you should be able to: Interpret and critically evaluate complex texts (literary, technical, academic).  Enhance practical English language skills in personal, social, and professional contexts.  Formulate perspectives and articulate ideas clearly in speech and writing.  Deliver effective presentations and participate confidently in discussions/debates using rhetorical and communication tools.  Develop collaborative communication skills, including teamwork, active listening, and clear expression. Utilise digital tools for research, collaboration, and multimedia presentation

Divya KK

OBJECTIVES OF THE COURSE:a. To expose the students to diverse cultures and modes of expression.b. To enable them to explore issues of cultural plurality and hybridityc. To expose the learners to literary negotiations of colonization and decolonization, identity, inequality,marginalization and so on.COURSE DESCRIPTION:A. COURSE SUMMARY:Module 1: 5 hrsModule 2: 20 hrsModule 3: 25 hrsModule 4: 2o hrsModule 5: 8 hrsEvaluation: 12 hrs