This
course is designed to provide students with the theoretical and practical knowledge of various data structures , algorithms and its implementation in OOP.
- Teacher: APARNA VIJAYAN
Course Objectives
To expose the students to
1) The basics of real analysis required for their subsequent course work.
2) Learn when certain theorems apply and when they do not
3) Identify the correct definitions and theorems to deal with unknown problems
Course- Learning Outcomes
CO1: Students will understand the basics& describe the fundamental properties of the real numbers that underpin the formal development of real analysis of Real analysis
CO2: Students will understand the definition & concepts related to sequences
CO3: Students will demonstrate an understanding of the theory of sequences and series, limits, continuity, differentiation and integration.
CO4: Students will be able to find out Lim sup & lim inf
CO5: Students will know the purpose of power series& uniform convergence and able to solve problems related to them
CO6: Students will apply the theory in the course to solve a variety of problems at an appropriate level of difficulty.
CO7: Students will Practice the problems related to Riemann integral
CO8: Students will demonstrate skills in constructing rigorous mathematical arguments;
- Teacher: K USHA PRAMEELA
Course Objective
The objective of this course is to provide an understanding for the graduate student on statistical concepts to include Correlation and Regression analysis, Multiple correlations, Attributes, Sampling distribution and Estimation theory.Course Outcomes:
By completing this course the student will learn to perform the following:
CO-1 Explain Bi-variate data, Understand the principle of least squares and the procedure of fitting of any functional relationship.
CO-2 Describe different types of correlation, elaborate methods of calculating correlation of both ungrouped and grouped data.
CO-3 Understand the method of regression analysis that helps in estimating the values of a variable from the knowledge of one or more variables and the distinction between correlation, regression and multiple correlations.
CO-4 Explain the difference between quantitative and qualitative variables.
CO-5 Explain the notations and terminology used in the classification of attributes.
CO-6 Explain independence of attributes, consistency of data, various measures of Association.
CO-7 Differentiate census and sample enumeration.
CO-8 Understand the concepts of population, sample, parameter, statistic, estimator and estimate, sampling distribution and standard error.
CO-9 Describe types of estimation and the characteristics of an estimator.
CO-10 Explain the difference between ML estimation and moment estimation.
- Teacher: Dr. SRIDHAR BABU MOTHUKURI