Maharshi Dayanand University, Rohtak has published the MDU Syllabus 2019 for all UG/PG Courses. Candidates who wish to get the BCA, B.Tech, B.Ed, MBA and MA Syllabus can download it from the direct link given here and start their preparation for the upcoming Exam accordingly.
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|Category||MDU University Syllabus|
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MDU B.Tech (Computer Science & Engineering) Syllabus
Advanced Computer Architecture:
- Architecture And Machines
- Cache Memory Notion
- Memory System Design
- Concurrent Processors
- Shared Memory Multiprocessors
Software Project Management:
- Introduction to Software Project Management
- Project Evaluation & Estimation
- Activity planning & Risk Management
- Risk Management
- Resource allocation & monitoring the control
- Monitoring the control
- Managing contracts and people
- Software quality
- Introduction To Compilers
- Lexical Analysis
- Syntax Analysis
- Syntax Directed Translations
- Symbol Table & Error Detection And Recovery
- Code Optimization & Code Generation
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- Fundamental concepts of Artificial Neural Networks
- Single layer Perception Classifier
- Multi-layer Feed forward Networks
- Single layer feedback Networks
- Associative memories
- Self organizing networks
- CORE JAVA
- Database Networking
- Distributed Objects
- Javabeans Components
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Distributed Operating System:
- Introduction on
- Synchronization in Distributed System
- Processes and Processors in distributed systems
- Distributed Shared Memory
- Case study MACH
Advanced Database Management Systems:
- Data Models
- Query Optimization
- Database Transactions and Recovery Procedures
- Client Server Computing
- Distributed and Parallel Databases
- Deductive and Web Databases
- Emerging Databases
Computer Software Testing:
- Fundamentals and Testing types
- Reporting and analyzing bugs
- Problem Tracking System
- Localization and User Manuals testing
- Testing Tools and Test Planning
Real Time Systems:
- Task Assignment and Scheduling
- Real Time Databases
- Real Time Communication
- Real Time operating System
- Real Time Knowledge Based Systems and Programming Languages
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MDU B.Ed Syllabus
Course-I: Childhood and Growing Up:
Development of Child at different Stages (Childhood and Adolescence):
- Concept, Meaning and general principles of Growth and development. Stages of
- Development growth and development across various stages from infancy to
- Adolescence (Physical, intellectual, social and moral development)
- Piaget’s concept of cognitive development,
- Kohlberg’s theory of moral development
- Erikson’s psycho-social development theory
- Factors affecting Growth and development
- Relative role of heredity and environment in development.
- Concept of growth and maturation
- Parenting styles: influencing developmental aspects of childhood and adolescence.
- Impact of Media on growing children and adolescents: deconstruction of significant
- events that media highlights and creates
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Understanding Individual Difference:
- Concept of individual difference, Factors influencing individual difference, Educational Implications of individual differences for teachers in organizing educational activities
- Dimensions of differences in psychological attributes-cognitive, interest, aptitude, creativity, personality and values.
- Understanding individual from multiple intelligences perspective witha focus on Gardrner’s theory of multiple intelligences, Implications for teaching-learning
- Understanding differences based on a range of cognitive abilities—learning difficulties, slow learners and dyslexics, intellectual deficiency, intellectual giftedness. Implications for catering to individual variations in view of ‘difference’ rather than ‘deficit’ perspective.
- Methods and Ways to understand Children’s and Adolescents’ Behaviour: Gathering data about children from different contexts: naturalistic observations; interviews; reflective journals about children; anecdotal records and narratives
- Meaning, characteristics and kinds of Play; Play and its functions: linkages with the physical, social, emotional, cognitive.
- Games and group dynamics, rules of games and how children learn to negotiate differences and resolve conflict.
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Theoretical Perspectives to enhance Learning among Children and Adolescents:
- Learning: Meaning, implicit knowledge and beliefs.
- Perspective on Human Learning: connectionists or Behaviorist (Thorndike, Classical and Operant Conditioning)
- Cognitivist (Insightful learning, Tolman’s Sign learning theory)
- Bruner’s discovery learning:
- Concepts and principles of each perspective and their applicability in different learning
- Relevance and applicability of various theories of learning for different kinds of learning
- Role of learner in various learning situations as seen in different theoretical
- Role of teacher in teaching learning situations
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Deprivation and Deprived Children: Measures for their Adjustment and Education:
- Childhood in the context of poverty and globalization
- Current issues related to adolescents stress and role of the teacher (Increasing loneliness, changing family structures and rising permissiveness
- Issues in marginalization of difference and diversity
- Children living in urban slum, socially deprived girls: measures to bring improvement in their status
- Child rearing practices of children separated from parents practices of children’s separated children in crèches; children in orphanages
- Schooling: peer influences, school culture, relationships with teachers, teacher expectations and school achievement; being out of school, overage learner
- Understanding needs and behavioral problems of children and adolescents:
- Relationships with peers: friendships and gender; competition and cooperation,
- competition and conflict; aggression and bullying from early childhood to adolescence
- substance abuse, drug addiction,
- Impact of globalization, urbanization and economic changes on construction and
- experience of children in childhood and adolescent age.
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MDU M.Phil (Statistics) Entrance Exam Syllabus
Paper-I Research Methodology:
- Introduction: Meaning, objectives, types and significance of Research. Research Methods versus Methodology. Process of Research: Steps involved in research process, Research problem and its selection, Necessity of defining the problem, techniques involved in defining a problem with example.
- Research Design: Meaning, Need, Feature and Importance of Research Design, various research designs.
- Types of data and various methods of data collection, framing of questionnaire, checklist, concept of reliability and validity methods, compilation of data, coding, editing and tabulation of data, various sampling methods.
- Random Number Generation, Mid-square method of Generating Pseudo-Random
- Numbers, Simulation techniques: Monte-Carlo Simulation and Applications.
- Use of data analysis tools like SPSS, Minitab and MS Excel
Statistical techniques for analyzing data: Measures of Central tendency measures of Dispersion, Importance of sampling distributions. Testing of Hypothesis:Parametric and Non-Parametric tests. Application of analysis of variable (ANOVA) and Covariance (ANCOVA)
Preparation of Dissertation: Types and layout of Research, Precautions in preparing the research dissertation, Bibliography, reference and annexure, discussion of results, draurg conclusions given suggestions and recommendations to the concerned persons.
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M.Phil- 1st Semester Paper II, III Stochastic Processes:
Stochastic Processes, Random Walk model, Gambler’s Ruin problem, Ballot Problem, Applications of Ballot problem, Generalized Random Walk.
Continuous time Discrete State Markov Process, Population Models, Poison Process, Continuous Time and Continuous State Markov Process, Differention process, Kolmograow backward and forward difference equation, Wiener Process, First passage Time distribution
Renewal theory, renewal equation, renewal theorems, Central limit theorem for renewal theory, Delayed and equilibrium renewal process, residual and excess life times renewal, renewal process.
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Applications to population growth, Queuing models, Epidemic processes, simple epidemic, General epidemic, application in ecology, biology and sociology
Advanced Theory of Sample Surveys:
Types of Sampling: Simple Random, Stratified Random and systematic sampling, Estimation in Ratio and Regression estimators, (For One and two variables), Double sampling for ration and regression estimators, double Sampling for stratification.
Sampling with varying probabilities, ordered and unordered estimators, Sampling Strategies due to Horvitz Thomson, Yales and Grundy Form Midzuno Sen, Brewerand Durbin Scheme (Sample size two only) Rao-Hartley, cochran Scheme for sample size n with random grouping and PPS systematic sampling, Double sampling for PPS estimation.
Single stage cluster sampling: multi-stage sampling, selection of PSU’s with unequal probabilities, Selection of PSU with replacement, stratified multi-stage sampling, Estimation of ratios, choice of sampling and sdub-sampling fraction, Repetitive Surveys, sampling on more than two occasions.
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Non-sampling errors, response errors, response bias, the analysis of data, Estimation of variance components uncorrelated response error, response and sampling variance, the problem of non-response, some example of sources of error. Variance estimation, method Estimation of random groups sub population.
Regression Analysis and Bayesian Inference:
Simple Linear Regression, Estimation of parameters, Matrix Approach to Linear Regression, R2 and adjusted R2, Weighted Least Squares. Model Adequacy Checking, Residual Analysis, methods of scaling residuals- Standardized and student zed residuals Press Residual, Residual Plots, PRESS Statistic
Diagnostics for Leverage and Influence, Variable Selection and Model Building, Computational Techniques for Model Selection- Mallow’s Cp , Stepwise Regression, Forward Selection, Backward Elimination. Elementary Ideas of Logistic and Poisson regression
Mixture Distributions, Exponential Family of distributions, Prior and Posterior distributions, Baye’s theorem and computation of posterior distribution, Natural conjugate family of priors for a model, Conjugate families for exponential family models
Non – Informative and Improper priors, Jeffrey’s Prior, Asymptotically Locally invariant prior. Maximum entropy priors, Bayes estimation
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Reliability Theory And Modeling:
Reliability and Quality, Types of Reliability, Failures Data Analysis: Failure, Types of Failures, Causes of Failures, Failure Rate, Mean Time To Failure (MTTF),Mean Time Between Failures (MTBF), MTTF interms of failure density. Linear and Non-Linear Hazard Model, The Weibul Model, Gamma Model, Normal Failure Model and Markov Model. Determination of Distribution functions and reliability of hazard models and Markov model
Evaluation of mean time to system failure (MTSF) and reliability for various structures such as series, parallel, series parallel, parallel series, non –series parallel, mixed-mode and k-out-of-n structures. Methods of reliability improvement: Redundancy And Maintenance. Reliability analysis using redundancy and maintenance, Availability function
Repairable Systems: Instantaneous repair Rate and Mean Time to Repair. Reliability and Availability Analysis of a Two-Unit parallel system with repair using markov model. Economics of Reliability Engineering: Manufactures cost, Customers cost, Reliability Achievement and utility cost Models, Depreciation cost models and Availability cost Model for parallel system. Availability Analysis of a system using reneval theoretic approach
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Reliability and availability analysis of single-unit and two –unit cold standby systems with constant failure rate and repair using Regenerative Point and Supplementary Variable Techniques. Evaluation of reliability by the Methods-Decomposition method, Cut-set method, Event space method and Boolean function technique
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MDU University Rohtak syllabus
Candidates who are pursuing their graduation and post-graduation from MDU University can check the Maharshi Dayanand University Syllabus for different courses. MDU SYLLABI is available on the official website of the Maharshi Dayanand University.
Maharshi Dayanand University Exam Syllabus
MDU Distance Syllabus will help you to know all the subjects you will learn during your graduation or post-graduation. You will also be able to prepare for the exam as per the topics given in MDU Syllabus 2019.
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