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Preparing for the RBI Grade B DSIM exam can feel confusing at the start. The syllabus looks long and technical, and many aspirants are not sure where to begin. But once you understand the paper structure and subject-wise topics, your preparation becomes more clear and focused. In this blog, we have provided the RBI Grade B DSIM syllabus, paper pattern, and detailed Statistics topics.
What is the RBI Grade B DSIM syllabus?
The RBI Grade B DSIM syllabus is designed to test a candidate's strong understanding of Statistics, Econometrics, Data Science, and analytical techniques used in policy research and data analysis at the Reserve Bank of India. The syllabus is set at a post-graduation level and covers both theoretical concepts and practical applications such as probability theory, regression models, statistical inference, time series analysis, machine learning, optimization methods, and database management systems. The exam is divided into three papers Paper 1 (Objective Statistics), Paper 2 (Descriptive Statistics), and Paper 3 (English Writing Skills) where the first two papers focus on advanced statistics topics and the third paper evaluates writing ability, clarity of expression, and understanding of financial or economic themes.
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What is the RBI Grade B DSIM exam pattern?
The RBI Grade B DSIM exam mainly tests the knowledge of Statistics, Econometrics, Data Science, and English writing skills. The selection process includes three written papers and an interview stage. Paper I is objective, while Papers II and III are descriptive in nature.
| Paper | Type | Subject | Duration | Marks |
|---|
| Paper 1 | Objective | Statistics | 120 Minutes | 100 |
| Paper 2 | Descriptive | Statistics | 180 Minutes | 100 |
| Paper 3 | Descriptive | English Writing | 90 Minutes | 100 |
| Interview | Personality + Technical | — | — | 75 |
What topics are covered in the RBI Grade B DSIM paper-wise syllabus?
The RBI Grade B DSIM paper-wise syllabus focuses mainly on advanced Statistics, Econometrics, Data Science, and analytical skills required for data modelling and policy research at the Reserve Bank of India. The exam includes three papers where Paper 1 and Paper 2 cover detailed statistics topics at a post-graduation level, while Paper 3 evaluates English writing ability and expression skills. Below are the main topics covered in each paper as per the official syllabus.
Paper 1 Objective Type (Statistics):
- Theory of Probability and Probability Distributions
- Sampling Theory and Sampling Methods
- Linear Models and Economic Statistics
- Statistical Inference and Non-Parametric Tests
- Stochastic Processes
- Multivariate Analysis
- Econometrics and Time Series Models
- Optimization and Statistical Computing
- Data Science, Artificial Intelligence and Machine Learning
- Database and Data Warehouse Management
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Paper 2 Descriptive Type (Statistics):
- Estimation methods and hypothesis testing
- Regression models and shrinkage techniques (Ridge, LASSO, Elastic Net)
- Index numbers and economic inequality measures
- Markov chains, Poisson process, Brownian motion
- ARIMA, SARIMA, ARCH/GARCH models
- Bayesian modelling, simulation, MCMC methods
- Neural networks, classification models, clustering techniques
- SQL queries, RDBMS concepts, ETL processes and data warehousing
Paper 3 English (Descriptive):
- Essay writing to assess analytical thinking
- Precis writing and comprehension
- Expression, clarity, and structured writing skills
- Understanding and interpretation of topics through written communication
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What are the topic covered under the RBI DSIM paper 1 syllabus?
Paper 1 of the RBI Grade B DSIM exam is an objective type Statistics paper that checks your conceptual clarity, analytical ability, and technical knowledge. The syllabus is based on post-graduation level Statistics and includes probability, econometrics, machine learning, optimization, and database concepts that are useful for data analysis and policy research at RBI.
| Main Topic | Sub Topics Covered |
|---|
| Theory of Probability, Distributions & Sampling | • Classical and axiomatic probability • Bayes theorem • Laws of Large Numbers (LLN) and Central Limit Theorem (CLT) • Characteristic functions and probability inequalities • Binomial, Poisson, Normal, Beta, Gamma, Weibull, Logistic distributions • Chi-square, t, F, Z distributions • Sampling methods – SRS, stratified, cluster, PPS • Ratio and regression estimation |
| Linear Models & Economic Statistics | • Linear algebra, matrices, quadratic forms • Simple and multiple regression • Gauss-Markov setup and weighted least squares • Dummy variables and multicollinearity • Ridge, LASSO, Elastic Net • Index numbers, Gini coefficient, Lorenz curve • National accounts basics |
| Statistical Inference | • Estimation concepts and MVUE • Rao-Blackwell, Lehmann-Scheffe, Cramer-Rao bound • MLE, least squares, minimum Chi-square • Bayes estimators • Hypothesis testing and Neyman-Pearson theory • Likelihood ratio tests • Non-parametric tests • Kernel density estimation |
| Stochastic Processes | • Poisson and compound Poisson process • Markov chains and Chapman-Kolmogorov equations • Stationary distributions • Brownian motion and martingales |
| Multivariate Analysis | • Multivariate normal distribution • Logit and probit models • Principal Component Analysis (PCA) • Factor analysis • Canonical correlation • Discriminant analysis • Cluster analysis |
| Econometrics & Time Series | • OLS and GLS methods • Heteroscedasticity and autocorrelation tests • Instrumental variables and panel regression • AR, MA, ARMA models • ARIMA and SARIMA • Box-Jenkins methodology • ARCH/GARCH models |
| Optimization & Statistical Computing | • Taylor theorem and convex functions • Newton and gradient methods • Lagrange multipliers • Linear programming and simplex method • Simulation and bootstrap methods • EM algorithm • Bayesian estimation and MCMC |
| Data Science, AI & Machine Learning | • Linear and logistic regression • Naïve Bayes and SVM • Decision trees and neural networks • Random forest and boosting • Clustering techniques • NLP basics • Feature selection and cross-validation |
| Database & Data Warehouse Management | • RDBMS fundamentals • SQL queries and joins • Database normalization • NoSQL databases • ETL processes • OLAP vs OLTP • Indexing and big data basics |
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What are the topic covered under the RBI Grade B DSIM paper 2 syllabus?
Paper 2 is a descriptive Statistics paper and follows the same syllabus areas as Paper 1, but the focus is on detailed explanations, derivations, and applied analytical writing. Candidates are expected to demonstrate deeper understanding of statistical theory, econometric modelling, and data science applications through structured answers.
| Main Topic | Sub Topics Covered |
|---|
| Probability & Sampling (Descriptive Level) | • Laws of probability • Distribution properties • Asymptotic distributions • Contingency tables • Sampling designs • Survey errors and non-response issues |
| Linear Models & Regression Analysis | • Polynomial regression • Box-Cox transformation • Regression with correlated observations • Hypothesis testing in regression • Confidence regions • Outlier detection and treatment |
| Economic Statistics | • Construction of index numbers • Base shifting and splicing • Deflating of index numbers • Measurement of inequality • Basics of macroeconomic statistics |
| Statistical Inference | • Likelihood ratio tests • Bartlett's test • Kolmogorov–Smirnov test • Wilcoxon tests and Friedman test • Order statistics |
| Stochastic Processes | • Non-homogeneous Poisson process • Recurrent events • Stationary distributions • Random walk limits |
| Multivariate & Classification Methods | • PCA interpretation • Factor loadings • Discriminant rules • Cluster validation indices |
| Econometrics & Time Series | • Simultaneous equation models • Distributed lag models • ARIMA diagnostics • Stationarity tests • Volatility modelling |
| Optimization & Computing | • Gradient-based optimization • Bayesian modelling • Gibbs sampling • Metropolis-Hastings algorithm • Robust regression techniques |
| Machine Learning Applications | • Random forest • Boosting techniques • Neural networks • Kernel regression • Feature engineering and hyperparameter tuning |
| Database Systems | • SQL operations • Data integration • Data warehouse schemas • Indexing and query optimization |
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What are the topic covered under the RBI DSIM paper 3 syllabus?
Paper 3 is a descriptive English paper designed to assess communication skills, clarity of thought, and professional writing ability. RBI expects DSIM candidates to interpret data insights and present them clearly, so this paper focuses on structured writing rather than technical statistics.
| Section | Skills and Topics Covered |
|---|
| Essay Writing | Analytical writing on economic, financial, or social themes |
| Precis Writing | Summarizing information clearly and logically |
| Reading Comprehension | Understanding and interpreting passages |
| Expression & Writing Skills | Grammar usage, clarity, coherence, professional tone |
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