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.
RBI Grade B DSIM Syllabus 2027
The RBI Grade B DSIM syllabus tests candidates on Statistics, Data Analysis, Econometrics, and Data Science used in policy research. It is based on postgraduate-level concepts and covers topics such as probability, regression, statistical inference, time series analysis, machine learning, optimization, and database management.
The exam has 3 papers Paper 1 is an objective Statistics paper, Paper 2 is a descriptive Statistics paper, and Paper 3 tests English writing skills. Papers 1 and 2 focus on advanced statistical and analytical concepts, while Paper 3 checks candidates' ability to write clearly on economic and financial topics related to the Reserve Bank of India.
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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 1 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 are the important dates for RBI Grade B DSIM 2027 exam?
The RBI Grade B DSIM 2027 notification will contain important dates related to online registration, application fee payment, application correction, Phase 1, Phase 2, interview, and final result. Based on the RBI Grade B 2027 recruitment timeline, the DSIM notification is expected in March-April 2027, while the application process is likely to remain open for around 20 to 25 days. Phase 1 is expected to be conducted in May-June 2027, followed by Phase 2 in June-July 2027.
| Events | RBI Grade B DSIM 2027 (Expected) | RBI Grade B DSIM 2026 (Official) |
|---|
| Notification Release Date | March-April 2027 | 29th April 2026 |
| Online Application Start Date | March-April 2027 | 29th April 2026 |
| Last Date to Apply Online | 20-25 days after notification | 20th May 2026 |
| Last Date for Fee Payment | Along with application deadline | 20th May 2026 |
| Application Correction Window | As per official notification | 25th-26th May 2026 |
| Last Date to Print Application | As per official notification | 5th June 2026 |
| Phase 1 Exam | May-June 2027 (Expected) | 14th June 2026 |
| Phase 2 Exam | June-July 2027 (Expected) | 26th July 2026 |
| Interview | July-August 2027 (Expected) | To be announced |
| Final Result | August-September 2027 (Expected) | To be announced |
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What topics are covered in the RBI Grade B DSIM paper-wise syllabus?
The RBI Grade B DSIM syllabus covers advanced Statistics, Econometrics, Data Science, and analytical skills needed for data analysis and policy research. The exam has three papers, with Papers 1 and 2 covering postgraduate-level Statistics and Paper 3 focusing on English writing and communication skills. The syllabus tests both quantitative knowledge and clear writing ability. The key topics covered in each paper are given below.
| Paper | Type | Topics (Simple Pointers) |
|---|
| Paper 1 | Objective (Statistics) | • Probability theory and distributions
• Sampling theory and 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, AI and machine learning
• Database and data warehouse management |
| Paper 2 | Descriptive (Statistics) | • Estimation methods and hypothesis testing
• Regression models (Ridge, LASSO, Elastic Net)
• Index numbers and inequality measures
• Markov chains, Poisson process, Brownian motion
• ARIMA, SARIMA, ARCH/GARCH models
• Bayesian modelling, simulation, MCMC methods
• Neural networks, classification and clustering
• SQL queries, RDBMS, ETL and data warehousing |
| Paper 3 | Descriptive (English) | • Essay writing for analytical thinking
• Precis writing and comprehension
• Clear expression and structured writing
• Understanding and explaining topics in writing |
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What are the topics 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 topics 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 modeling, 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 topics 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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