SUNRISE CLASSES IS IN LAXMI NAGAR NEAR METRO STATION EXIT FROM GATE NO. 5

RBI Grade B (DR)-DSIM 2025: Scheme of Selection and Detailed Syllabus

(i) Scheme of Selection

Selection will be through Online/Written Examination (WE) and Interview. There are 3 papers for the examination.

• Paper-I: Objective Type (on Statistics) .

• Paper-II and Paper-III 

The details are as follows:

• Other detailed information regarding the examination will be given in an Information Handout, which will be made available to the candidates for download along with the Admit Card for examination from the RBI website.

• Candidates have to secure minimum marks as may be prescribed by the Board.

• Candidates who secure the prescribed minimum aggregate marks in Paper-I will be shortlisted for Paper-II and Paper-III of the examination based on the aggregate marks obtained in Paper-I.

• The minimum aggregate cut-off marks for being shortlisted for Paper-II and Paper-III of the examination will be decided by the Board in relation to the number of vacancies.

• The Roll Numbers of candidates shortlisted for Paper-II and Paper-III examination will be published on the RBI website in due time after the Paper-I examination.

(ii) Paper-II and Paper-III – Online/Written Examination

The Paper-II and Paper-III Examination will be only for candidates who are shortlisted on the basis of the results of Paper-I.

Paper-II and Paper-III examinations will be conducted in shifts. Candidates are required to appear for all the papers in all the shifts.

A single Admit Card will be issued for both shifts.

The timetable for Paper-II and Paper-III will be intimated to the candidates concerned along with the Admit Card for Paper-II and Paper-III.

Medium of Examination

Question papers for the Written Examination (WE) will be set in Hindi and English, except Paper-III on English.

• Answers for Paper-II may be written either in Hindi or English.

• Paper-III will have to be answered in English only.

• Candidates may opt for the Interview in Hindi or English.

(iii) Interview Shortlisting

The number of candidates to be called for the Interview will be decided by the Board.

(iv) Final Selection

Final selection will be on the basis of performance in the Online/Written Examination – Paper-I, Paper-II and Paper-III – and Interview taken together.

The Interview will be of 75 marks.

Candidates may opt for the Interview either in Hindi or English.

(v) Personality Assessment

Candidates will be required to undergo a Personality Assessment conducted by the Bank before appearing for the Interview.

No marks will be allotted for the Personality Assessment, and it will not form part of the final selection criteria.

RBI-DSIM Syllabi

The standard of the papers will be that of a Post-Graduation Degree Examination of any recognized Institution/University in India.

Paper-I and Paper-II

The syllabus for Paper-I and Paper-II consists of the following major areas:

1. Theory of Probability, Probability Distributions and Sampling Theory

2. Linear Models and Economic Statistics

3. Statistical Inference: Estimation, Testing of Hypothesis and Non-Parametric Test

4. Stochastic Processes

5. Multivariate Analysis

6. Econometrics and Time Series

7. Optimization and Statistical Computing

8. Data Science, Artificial Intelligence and Machine Learning Techniques

9. Database and Data Warehouse Management

Detailed Syllabus

1. Theory of Probability, Probability Distributions and Sampling Theory

Probability Theory

• Classical and axiomatic approach of probability and its properties.

• Bayes Theorem and its application.

• Strong and weak laws of large numbers.

• Characteristic functions.

• Central Limit Theorem.

• Probability inequalities.

Probability Distributions

Standard probability distributions:

• Binomial

• Poisson

• Geometric

• Negative Binomial

• Uniform

• Normal

• Exponential

• Logistic

• Log-normal

• Beta

• Gamma

• Weibull

• Bivariate Normal, etc.

Sampling Distributions

• Exact sampling distributions – Chi-square, Student's t, F and Z distributions and their applications.

• Asymptotic sampling distributions and large sample tests.

• Association and analysis of contingency tables.

Sampling Theory

Standard sampling methods such as:

• Simple Random Sampling

• Stratified Random Sampling

• Systematic Sampling

• Cluster Sampling

• Two-stage Sampling

• Probability Proportional to Size (PPS), etc.

• Ratio Estimation

• Regression Estimation

• Non-sampling errors

• Problem of non-response

2. Linear Models and Economic Statistics

Linear Algebra

• Vectors and matrices

• Spanning of vector space

• Matrix algebra

• Inverse of partitioned matrices

• g-inverse

• Orthogonal matrices

• Properties of idempotent matrices

• Characteristic roots and vectors

• Cayley-Hamilton theorem

• Quadratic forms

• Definite, semi-definite and indefinite forms

• Simultaneous reduction of two quadratic forms

• Properties of similar matrices

Regression Analysis

Simple Linear Regression

• Assumptions

• Estimation

• Inference

• Diagnostic checks

Other topics include:

• Polynomial regression

• Transformations on Y or X such as:

  • Box-Cox transformation

  • Square-root transformation

  • Log transformation, etc.

• Method of weighted least squares

• Inverse regression

Multiple Regression

• Standard Gauss-Markov setup

• Least squares estimation and related properties

• Regression analysis with correlated observations

• Simultaneous estimation of linear parametric functions

• Testing of hypotheses

• Confidence intervals and regions

• Multicollinearity

• Shrinkage models:

  • Ridge Regression

  • LASSO

  • Elastic Net

• Model selection criteria

• Residual diagnostics

• Categorical data analysis using dummy variables

• Outlier detection and treatment

Economic Statistics

• Definition and construction of index numbers

• Standard index numbers

• Conversion of chain-base index to fixed-base and vice versa

• Base shifting

• Splicing

• Deflating of index numbers

• Measurement of economic inequality:

  • Gini's coefficient

  • Lorenz curves, etc.

• Basics of macroeconomics

• National accounts

3. Statistical Inference: Estimation, Testing of Hypothesis and Non-Parametric Test

Estimation

Topics include:

• Concepts of estimation

• Unbiasedness

• Sufficiency

• Consistency

• Efficiency

• Factorization theorem

• Complete statistic

• Minimum Variance Unbiased Estimator (MVUE)

• Rao-Blackwell theorem and its applications

• Lehmann-Scheffe theorem and its applications

• Cramer-Rao inequality

Methods of Estimation

• Method of Moments

• Maximum Likelihood Estimation

• Method of Least Squares

• Method of Minimum Chi-square

• Basic idea of Bayes estimators

Principles of Test of Significance

• Type-I errors

• Type-II errors

• False-positive errors

• False-negative errors

• Critical region

• Level of significance

• Size

• p-value and its interpretation

• Power

• Best critical region

• Most powerful test

• Uniformly Most Powerful (UMP) test

• Neyman-Pearson theory of testing of hypothesis

• Likelihood ratio tests

• Tests of goodness of fit

• Bartlett's test for homogeneity of variances

Non-Parametric Test

The following non-parametric procedures are included:

• Kolmogorov-Smirnov test

• Sign test

• Wilcoxon Signed-Rank test

• Wilcoxon Rank-Sum test

• Mann-Whitney U-test

• Kruskal-Wallis one-way ANOVA test

• Friedman's test

• Kendall's Tau coefficient

• Spearman's coefficient of rank correlation

Other topics include:

• Distribution of order statistics

• Distribution fitting

• Kernel density estimation

4. Stochastic Processes

Poisson Process

• Arrival distributions

• Interarrival distributions

• Conditional arrival distributions

• Non-homogeneous processes

• Law of Rare Events

• Poisson Process

• Compound Poisson Processes

Markov Chains

• Transition probability matrix

• Chapman-Kolmogorov equations

• Regular chains

• Stationary distributions

• Periodicity

• Limit theorems

• Patterns for recurrent events

• Brownian Motion

  • Limit of Random Walk

  • Defining characteristics

  • Peculiarities

• Martingales

5. Multivariate Analysis

Topics include:

• Multivariate Normal Distribution

• Properties and characterization of Multivariate Normal Distribution

• Logit-Probit models

• Mahalanobis' \(D^2\) statistics

• Linear Discriminant Analysis (LDA)

• Canonical Correlation Analysis

• Principal Components Analysis (PCA)

• Factor Analysis

• Cluster Analysis

6. Econometrics and Time Series

Econometrics

• General linear model and its extensions

• Ordinary Least Squares (OLS)

• Generalized Least Squares (GLS)

• Estimation and prediction

• Heteroscedastic disturbances

• Pure and mixed estimation

• Autocorrelation

• Consequences of autocorrelation

• Related tests

• Theil BLUS procedure

• Estimation and prediction

• Multicollinearity

• Implications of multicollinearity

• Tools for handling multicollinearity

• Ridge regression

Additional topics:

• Linear regression

• Stochastic regression

• Instrumental Variable Regression

• Panel regression

• Autoregressive linear regression

• Distributed lag models

• Estimation of lags by OLS method

• Simultaneous linear equations model and its generalization

• Identification problem

• Restrictions on structural parameters

• Rank and order conditions

• Different estimation methods for simultaneous equations model

• Prediction

• Simultaneous confidence intervals

Time Series

• Exploratory analysis of time series

• Concepts of weak stationarity

• Concepts of strong stationarity

• AR processes

• MA processes

• ARMA processes and their properties

• Model identification based on ACF and PACF

• Model estimation

• Diagnostic tests

• Box-Jenkins models

• ARCH models

• GARCH models

Inference with Non-Stationary Models

• ARIMA model

• SARIMA model

• Determination of the order of integration

• Trend stationarity

• Difference stationarity processes

• Tests of non-stationarity

7. Optimization and Statistical Computing

Optimization

• Unconstrained optimization using calculus

  • Taylor's theorem

  • Convex functions

  • Coercive functions

• Unconstrained optimization through iterative methods:

  • Newton's method

  • Gradient-based methods

  • Conjugate gradient-based methods

  • Quasi-Newton methods

• Constrained optimization

  • Penalty methods

  • Lagrange multipliers

• Convex sets

• Convex hull

• Formulation of a Linear Programming Problem

• Theorems dealing with vertices of feasible regions and optimality

• Graphical solution

• Simplex method

Statistical Computing

• Simulation techniques for various probability models

• Resampling methods:

  • Jackknife

  • Bootstrap

  • Cross-validation

• Techniques for robust linear regression

• Nonlinear regression

• Generalized linear regression problems

• Tree-structured regression and classification

• Analysis of incomplete data:

  • EM algorithm

  • Single imputation

  • Multiple imputation

• Bayesian modelling and estimation

• Markov Chain Monte Carlo (MCMC)

• Annealing techniques

• Gibbs sampling

• Metropolis-Hastings algorithm

• Neural Networks

• Association Rules

• Learning algorithms

8. Data Science, Artificial Intelligence and Machine Learning Techniques

Topics include:

• Introduction to supervised pattern classification

• Introduction to unsupervised pattern classification

• Unsupervised learning

• Reinforcement learning

• Basics of optimization

• Model accuracy measures

• Linear Regression

• Logistic Regression

• Penalized Regression

• Naïve Bayes

• Nearest Neighbor

• Decision Tree

• Support Vector Machine

• Kernel Density Estimation

• Kernel Discriminant Analysis

• Classification under a regression framework

• Neural Networks

• Kernel Regression

• Trees and Random Forests

Clustering Methods

• Hierarchical methods

• Non-hierarchical methods

• K-means

• K-medoids

• Linkage methods

Cluster Validation Indices

• Dunn Index

• Gap Statistics

Ensemble Learning

• Bagging

• Random Forest

• Boosting

  • Adaptive Boosting

  • Gradient Boosting

Deep Learning and NLP

• Recurrent Neural Network (RNN)

• Convolutional Neural Network (CNN)

• Natural Language Processing (NLP)

Feature Selection, Regularization and Model Tuning

• Recursive Feature Elimination (RFE)

• Variance Inflation Factor (VIF)

• Ensembling methods

• Stacking methods

• Elastic Net regularization

• Hyperparameter tuning through Grid Search

• Feature importance interpretation

• Cross-validation strategies

9. Database and Data Warehouse Management

The syllabus includes:

• Data structures

• Fundamentals of Relational Database Management Systems (RDBMS)

• Fundamentals of non-traditional databases

• NoSQL databases

• Principles of database normalization

• Data redundancy elimination

• Consistency maintenance

Structured Query Language (SQL)

• Querying

• Updating

• Aggregating

• Managing relational data

Database Joins

• Inner Join

• Left Join

• Right Join

• Outer Join

• Applications of joins to data merging and integration

NoSQL Databases

Overview of:

• Document-based databases

• Key-value databases

• Wide-column stores

• Graph databases

Data Warehousing

• Data warehousing concepts

• Star schemas

• Snowflake schemas

• ETL – Extract, Transform, Load processes

• OLAP vs OLTP

• Database indexing

• Database optimization

• Basics of big-data frameworks

• Storage systems for large-scale data handling

Paper-III

English

The paper on English shall be framed in a manner to assess the candidate's writing skills, including expression and understanding of the topic.

C. Manner of Conduct of Examinations for Grade ‘B’ (DR)-DEPR/DSIM

This section deals with the manner in which examinations for RBI Grade ‘B’ (DR)-DEPR/DSIM are conducted.