BCS051 Statistical Computing Syllabus
AKTU B.Tech · 3rd Year · Unit-wise syllabus (effective 2024-25)
BCS051 Statistical Computing (also written as KCS051) is a subject in the 3rd year of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University with an L-T-P of 3-0-0. The syllabus is divided into 5 units: Descriptive Statistics and Probability, Inferential Statistics and Linear Methods for Regression Analysis, Pseudo-Random Numbers and Monte Carlo Integration, Resampling Methods, Introduction to R programming and Using R for statistical analysis. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCS051 Subject Details
| Subject Code | BCS051 / KCS051 |
|---|---|
| Subject Name | Statistical Computing |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year |
| L-T-P | 3-0-0 |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-17 |
BCS051 Statistical Computing Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: Descriptive Statistics · Probability
Descriptive Statistics: Diagrammatic representation of data, measures of central tendency, measures of dispersion, measures of skewness and kurtosis, correlation, inference procedure for correlation coefficient, bivariate correlation, multiple correlations, linear regression and its inference procedure, multiple regression. Probability: Measures of probability, conditional probability, independent event, Bayes’ theorem, random variable, discrete and continuous probability distributions, expectation and variance, markov inequality, chebyshev’s inequality, central limit theorem.
Unit 2: Inferential Statistics · Linear Methods for Regression Analysis
Inferential Statistics: Sampling & Confidence Interval, Inference & Significance. Estimation and Hypothesis Testing, Goodness of fit, Test of Independence, Permutations and Randomization Test, t- test/z-test (one sample, independent, paired), ANOVA, chi-square. Linear Methods for Regression Analysis: multiple regression analysis, orthogonalization by Householder transformations (QR); singular value decomposition (SVD); linear dimension reduction using principal component analysis (PCA).
Unit 3: Pseudo-Random Numbers · Monte Carlo Integration
Pseudo-Random Numbers: Random number generation, Inverse-transform, acceptance-rejection, transformations, multivariate probability calculations. Monte Carlo Integration: Simulation and Monte Carlo integration, variance reduction, Monte Carlo hypothesis testing, antithetic variables/control variates, importance sampling, stratified sampling Markov chain Monte Carlo (McMC): Markov chains; Metropolis-Hastings algorithm; Gibbs sampling; convergence
Unit 4: Resampling Methods
Resampling Methods: Cross-validation, Bootstrapping, Jackknife resampling, percentile confidence intervals, permutation tests Density Estimation: Univariate density estimation, kernel smoothing, multivariate density estimation Numerical Methods: Root finding; more on numerical integration; numerical maximization/minimization; constrained and unconstrained optimization; EM (Expectation- Maximization) algorithm; simplex algorithm
Unit 5: Introduction to R programming · Using R for statistical analysis
Introduction to R programming: History of R programming, starting and ending R, R as a scientific calculator , handling package, workspace, inspecting variables, operators and expressions inR, data objects and types, vectors, matrices and arrays, lists and data frames, built-in and user-defined functions , strings and factors, flow control and loops, advanced looping, date and times. Using R for statistical analysis: Importing data files, exporting data, outputting results, exporting graphs, graphics in R, interactively adding information of plot, performing data analysis tasks. R commands for descriptive statistics, data aggregation, representation of multivariate data, code factorization and optimization, statistical libraries in R.
BCS051 Course Outcomes
- At the end of course , the student will be able to:
- Understand and apply the probability distributions, random number generation and density K2, K4, K6
- estimations to perform analysis of various kinds of data
- Understand and manipulate data, design and perform simple Monte Carlo experiments, and be K5, K6
- able to use resampling methods
- CO 3 Perform statistical analysis on variety of data K2, K5
- CO 4 Perform appropriate statistical tests using R and visualize the outcome K2, K4
- CO 5 K2, K3
- Discuss the results obtained from their analyses after creating customized graphical and
- numerical summaries
BCS051 Reference Books
- S.C. Gupta & V.K. Kapoor, “Fundamentals of Mathematical Statistics”, Sultan Chand & Sons
- Sheldon M. Ross, “Introduction to Probability and Statistics for Engineers and Scientists”, Academic Press.
- Dudewicz, E.J., Mishra, S.N., “Modern Mathematical Statistics”, Willy
- Purohit S. G., Gore S. D., Deshmukh S. K., “Statistics using R, Narosa
- Rizzo, M. L., “Statistical Computing with R”, Boca Raton, FL: Chapman & Hall/CRC Press
- Normal Maltoff, The Art of R programming, William
- Dalgaard, Peter, “Introductory statistics with R”, Springer Science & Business Media
- M. D. Ugarte, A. F. Militino, A. T. Arnholt, “Probability and Statistics with R”, CRC Press
- Kundu, D. and Basu, A., “Statistical computing – existing methods and recent developments”, Narosa
- Gentle, James E., Härdle, Wolfgang Karl, Mori, Yuich, “Handbook of Computational Statistics”, Springer
- Givens and Hoeting, “Computational Statistics”, Wiley Series in Prob. and Statistics
- Michael J. Crawley “The R Book”, John Wiley and Sons.
- Richard Cotton, “Learning R”, O’Reilly
- Brain S. Everitt, “A Handbook of Statistical Analysis Using R”, Second Edition, LLC
- Randall E. Schumacker, “Learning Statistics Using R”, Sage.
- Jared P. Lander, “R for Everyone” Addison Wesley.
- Monahan, J.F., “Numerical methods of statistics”, Cambridge University Press.
- Robert, C. and Casella, G., “Introducing Monte Carlo Methods with R”, Springer Verlag, New York.
Frequently Asked Questions about BCS051
What is the syllabus of BCS051 Statistical Computing?
The AKTU BCS051 Statistical Computing syllabus is divided into 5 units: Descriptive Statistics and Probability; Inferential Statistics and Linear Methods for Regression Analysis; Pseudo-Random Numbers and Monte Carlo Integration; Resampling Methods; Introduction to R programming and Using R for statistical analysis. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCS051?
BCS051 Statistical Computing has 5 units in the AKTU syllabus, with an L-T-P (Lecture-Tutorial-Practical) structure of 3-0-0.
Is BCS051 the same subject as KCS051?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCS051 and KCS051 depending on the scheme year. The syllabus content is the same.
Where can I download BCS051 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCS051?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-17.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-17.