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Reserve Bank Information Technology Data Scientist Interview Questions, Process, and Tips
Reserve Bank Information Technology Data Scientist Interview Experiences
1 interview found
I applied via Recruitment Consulltant and was interviewed in Jun 2022. There were 4 interview rounds.
Test time 35 min, based on Python SQL Stats
(2 Questions)
- Q1. Explain ML algorithms
- Ans.
ML algorithms are mathematical models used to identify patterns and make predictions from data.
ML algorithms can be supervised, unsupervised, or semi-supervised
Supervised algorithms include linear regression, decision trees, and neural networks
Unsupervised algorithms include k-means clustering and principal component analysis
Semi-supervised algorithms combine elements of both supervised and unsupervised learning
ML algo...
- Q2. Explain SQL joins, explain join in a given situation
- Ans.
SQL joins are used to combine data from two or more tables based on a related column.
Joins are used to retrieve data from multiple tables in a single query.
Common types of joins are inner join, left join, right join, and full outer join.
Joining tables can be done using the JOIN keyword and specifying the columns to join on.
Example: SELECT * FROM table1 JOIN table2 ON table1.column = table2.column;
Joins can be used to c...
(3 Questions)
- Q1. Explain projects you have worked
- Q2. The algorithm used in the project (in my case LSTM)
- Ans.
The algorithm used in the project is LSTM.
LSTM stands for Long Short-Term Memory and is a type of recurrent neural network.
It is commonly used for sequential data analysis such as time series forecasting, speech recognition, and natural language processing.
LSTM networks have the ability to remember long-term dependencies and avoid the vanishing gradient problem.
They consist of memory cells, input gates, output gates, a...
- Q3. Why LSTM? how does it work?
- Ans.
LSTM is a type of recurrent neural network that can handle long-term dependencies.
LSTM stands for Long Short-Term Memory.
It uses gates to control the flow of information.
It can remember information for a longer period of time compared to traditional RNNs.
It is commonly used in natural language processing and speech recognition tasks.
LSTM has been shown to be effective in predicting stock prices and weather patterns.
Interview Preparation Tips
Skills evaluated in this interview
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Forecasting problem - Predict daily sku level sales
(2 Questions)
- Q1. What is difference between bias and variance
- Ans.
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
- Q2. Parametric vs non parametruc model
- Ans.
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
Skills evaluated in this interview
Interview Questionnaire
1 Question
- Q1. Questions about current and past projects and machine learning concepts.
Interview Preparation Tips
Experience: It was telephonic interview, started with introduction then interviewer asked me about current project and technologies using for implementation. Interviewer was very calm and friendly so I was relaxed, in the end we discussed about job profile, work environment and their expectation from me.
Tips: Know your projects work in details, answer to asked questions very brief and precise, if you don't know answer then be truthfull about it.
College Name: IIT Guwahati
I was interviewed in Apr 2017.
Interview Questionnaire
2 Questions
- Q1. Past and current projects work, and few basic machine learning concepts
- Q2. Questions about machine learning concepts, E.g. classification, regression, clustering, over fitting vs under fitting, boosting etc.
Interview Preparation Tips
Experience: It was telephonic interview, started with introduction, asked me about projects, technologies.
Tips: Know your projects work in detail.
Round: Technical Interview
Experience: It was video conferencing. Interviewer was very informal that made me relaxed and comfortable, asked me various questions on data science, I tried to answer those with how I used those concept in my projects work. It ended on positive note.
Tips: Be truthful, don't assume anything.
College Name: IIT Guwahati
Forecasting problem - Predict daily sku level sales
(2 Questions)
- Q1. What is difference between bias and variance
- Ans.
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
Bias is the error introduced by approximating a real-world problem, leading to underfitting.
Variance is the error introduced by modeling the noise in the training data, leading to overfitting.
High bias can cause a model to miss relevant relationships between features and target variable.
High variance can cause a model to ...
- Q2. Parametric vs non parametruc model
- Ans.
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.
Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.
Examples of parametric models inc...
Skills evaluated in this interview
Data Science Intern Interview Questions & Answers
METRO Global Solutions Center
Anonymous
posted on 5 Sep 2024
I was interviewed in Aug 2024.
It was a MCQ based test
(2 Questions)
- Q1. ML related question regression , classification , cost function and gradient descent
- Q2. Project related qs
Data Scientist Intern Interview Questions & Answers
Pitney Bowes
Astha Jaiswal
posted on 11 May 2017
I was interviewed in Nov 2016.
Interview Questionnaire
4 Questions
- Q1. Questions related to Machine Learning Algorithms.
- Q2. Questions related to Computer Vision.
- Q3. A couple of puzzles.
- Q4. Where do you see yourself in 5 years?
Interview Preparation Tips
Experience: I said I wanted to be at MIT Media Lab.
Skills: Programming, Machine Learning
College Name: University of Delhi
Interview Questionnaire
1 Question
- Q1. Questions about current and past projects and machine learning concepts.
Interview Preparation Tips
Experience: It was telephonic interview, started with introduction then interviewer asked me about current project and technologies using for implementation. Interviewer was very calm and friendly so I was relaxed, in the end we discussed about job profile, work environment and their expectation from me.
Tips: Know your projects work in details, answer to asked questions very brief and precise, if you don't know answer then be truthfull about it.
College Name: IIT Guwahati
I was interviewed in Apr 2017.
Interview Questionnaire
2 Questions
- Q1. Past and current projects work, and few basic machine learning concepts
- Q2. Questions about machine learning concepts, E.g. classification, regression, clustering, over fitting vs under fitting, boosting etc.
Interview Preparation Tips
Experience: It was telephonic interview, started with introduction, asked me about projects, technologies.
Tips: Know your projects work in detail.
Round: Technical Interview
Experience: It was video conferencing. Interviewer was very informal that made me relaxed and comfortable, asked me various questions on data science, I tried to answer those with how I used those concept in my projects work. It ended on positive note.
Tips: Be truthful, don't assume anything.
College Name: IIT Guwahati
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