BCS301 | Mathematics for Computer Science

Module 1
Probability Distributions: Review of basic probability theory. Random variables (discrete and continuous), probability mass and density functions. Mathematical expectation, mean and variance. Binomial, Poisson and normal distributions- problems (derivations for mean and standard deviation for Binomial and Poisson distributions only)-Illustrative examples. Exponential distribution.
Module 2
Joint probability distribution: Joint Probability distribution for two discrete random variables, expectation, covariance and correlation.
Markov Chain: Introduction to Stochastic Process, Probability Vectors, Stochastic matrices, Regular stochastic matrices, Markov chains, Higher transition probabilities, Stationary distribution of Regular Markov chains and absorbing states.
Module 3
 Statistical Inference 1 : Introduction, sampling distribution, standard error, testing of hypothesis, levels of significance, test of significances, confidence limits, simple sampling of attributes, test of significance for
large samples, comparison of large samples.
Module 4
Statistical Inference 2 : Sampling variables, central limit theorem and confidences limit for unknown mean. Test of
Significance for means of two small samples, students ‘t’ distribution, Chi-square distribution
as a test of goodness of fit. F-Distribution. 
Module 5
Design of Experiments & ANOVA : Principles of experimentation in design, Analysis of completely randomized design,
randomized block design. The ANOVA Technique, Basic Principle of ANOVA, One-way ANOVA, Two-way ANOVA, Latin-square Design, and Analysis of Co-Variance. 

BCS301 | Model Question Paper with Solution

MQP Solution
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BCS301 | Passing Package
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Passing Package
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