Capgemini Jr. Data Scientist Interview Questions and Answers

It was an half hour test 20 questions purely based on ML and statistical knowledge.

Q1. ML techniques, statistics behind them and questions about your project
Add your answer

Q1. What are the different supervised models used
Ans. 
Supervised models include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
• Linear regression: used for predicting continuous outcomes

• Logistic regression: used for binary classification

• Decision trees: used for classification and regression tasks

• Random forests: ensemble method using multiple decision trees

• Support vector machines: used for classification ...read more
Answered by AI
Add your answer
Q2. Linear regression, logistic regression, decision tree
Add your answer

Q1. What are the salary expectations
Add your answer

Q1. Evaluation metrics used in multiclass classification
Ans. 
Evaluation metrics for multiclass classification
• Accuracy

• Precision

• Recall

• F1 Score

• Confusion Matrix
Answered by AI
Add your answer
Q2. Difffereence between append and extend
Ans. 
Append adds a single element to a list while extend adds multiple elements.
• Append adds the element as it is to the end of the list.

• Extend takes an iterable and adds each element of the iterable to the end of the list.

• Append can be used to add a single element to a list.

• Extend can be used to add multiple elements to a list.

• Example: list1 = [1, 2, 3], list2 = [4, 5], list1.append(4) will result in [1, 2, 3, 4], list1.ext
Answered by AI
Add your answer
Q3. Sort a list and take out second minimum
Ans. 
Sort a list and extract the second minimum value.
• Sort the list in ascending order using the sort() method.

• Extract the second minimum value using indexing.

• Handle cases where the list has less than two elements.
Answered by AI
Add your answer

Q1. Introduction and overview of the projects
Add your answer

Q1. Computer vision questions
Add your answer

Hackrank coding round of machine learning questions

Q1. What is logistic regression
Ans. 
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (e.g., 0 or 1, yes or no).

• It estimates the probability that a given input belongs to a certain category.

• It uses the logistic function to model the relationship between the dependent variable and independent variables.

• Coe...read more
Answered by AI
Add your answer
Q2. What is linear regression
Ans. 
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that repres...read more
Answered by AI
Add your answer
Q3. What experince do you have in model deployment
Ans. 
I have experience deploying machine learning models using cloud services like AWS SageMaker and Azure ML.
• Deployed a sentiment analysis model on AWS SageMaker for real-time predictions

• Deployed a recommendation system model on Azure ML for batch predictions

• Used Docker containers to deploy models in production environments
Answered by AI
Add your answer
Q4. What are transformers ?
Ans. 
Transformers are models used in natural language processing (NLP) that learn contextual relationships between words.
• Transformers use self-attention mechanisms to weigh the importance of different words in a sentence.

• They have revolutionized NLP tasks such as language translation, sentiment analysis, and text generation.

• Examples of transformer models include BERT, GPT-3, and RoBERTa.
Answered by AI
Add your answer
Q5. What is hyperparameter tuning
Ans. 
Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model.
• Hyperparameters are parameters that are set before the learning process begins, such as learning rate, number of hidden layers, etc.

• Hyperparameter tuning involves trying out different combinations of hyperparameters to find the ones that result in the best model performance.

• Techniques for hyperparameter tuning...read more
Answered by AI
Add your answer

Normal there is not advance requirment

Q1. Will you work will less amount package
Add your answer
Q2. You wouldn't get any travel cost?
Add your answer
Q3. What about machine learning
Ans. 
Machine learning is a branch of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data.
• Machine learning involves training algorithms to learn patterns from data and make predictions or decisions.

• It can be supervised, unsupervised, or semi-supervised learning.

• Examples include recommendation systems, image recognition, and natural langu
Answered by AI
Add your answer
Q4. Tellm me about any cloud platform
Ans. 
A cloud platform is a service that allows users to store, manage, and process data remotely.
• Cloud platforms provide scalable and flexible storage solutions

• They offer various services such as computing power, databases, and analytics tools

• Examples include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform
Answered by AI
Add your answer
Q5. Telll me about pointer
Ans. 
A pointer is a variable that stores the memory address of another variable.
• Pointers are used to access and manipulate memory directly.

• They are commonly used in programming languages like C and C++.

• Example: int *ptr; // declaring a pointer variable
Answered by AI
Add your answer

Fibonacci series print

Sales dataset to predict future sales

Do well and contribute well

Do well and contribute well

Do well and contribute well

Q1. Why we choose this job
Ans. 
I am passionate about using data to solve complex problems and make meaningful insights.
• I have a strong background in statistics and data analysis

• I enjoy working with large datasets and finding patterns within them

• I am excited about the opportunity to work on real-world problems and make a positive impact
Answered by AI
Add your answer
Q2. What are your expectations
Ans. 
I expect challenging projects, opportunities for growth, supportive team environment, and continuous learning.
• Challenging projects that allow me to apply my skills and knowledge

• Opportunities for growth and advancement within the company

• Supportive team environment where collaboration and knowledge sharing are encouraged

• Continuous learning through training, workshops, and mentorship programs
Answered by AI
Add your answer

Q1. Tell me about your self
Ans. 
I am a data-driven individual with a strong background in statistics and machine learning.
• Graduated with a degree in Statistics

• Proficient in programming languages like Python and R

• Experience with data visualization tools like Tableau

• Completed projects involving predictive modeling and data analysis
Answered by AI
Add your answer
Q2. What are your salary expectations
Ans. 
I am looking for a competitive salary based on my skills and experience.
• Research industry standards for Jr. Data Scientist salaries

• Consider my level of experience and education

• Factor in the cost of living in the area where the job is located
Answered by AI
Add your answer