Tesco Data Scientist Interview Questions and Answers

Q1. Past experience
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Case Study was related to customer propensity to buy.

Q1. Linear Regression Assumptions
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Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Assumption of linearity: The relationship between the independent and dependent variables is linear.

• Assumption of independence: The residuals are independent of each other.

• Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Assumption of normality: The resid...read more
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Q2. ML Algorithms and Evaluation Metrics.
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Q1. What is VIF(variance inflation factor)
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VIF is a measure of multicollinearity in regression analysis, indicating how much the variance of an estimated regression coefficient is increased due to collinearity.
• VIF values greater than 10 indicate high multicollinearity

• VIF is calculated for each predictor variable in a regression model

• VIF is calculated as 1 / (1 - R^2) where R^2 is the coefficient of determination from regressing a predictor on all other predicto
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Q1. Why do you want to join?
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I am impressed by your company's innovative projects and collaborative work culture.
• I admire the company's commitment to cutting-edge technology and data-driven solutions.

• I am excited about the opportunity to work with a talented team of data scientists and researchers.

• Your company's reputation for fostering a collaborative and inclusive work environment is appealing to me.
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Array-based question

Q1. Explain Xgboots
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XGBoost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting.

• It is an implementation of gradient boosted decision trees designed for speed and performance.

• XGBoost is widely used in machine learning competitions and real-world applications.

• It can handle missing data, regularization, and parallel processing efficiently.

• XGBoost ...read more
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Q2. Explain random forest
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Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree in the forest makes a prediction, and the final prediction is the average or majority vote of all trees.

• Random forest is used for classification and regression tasks.

• It helps reduce overfitting and ...read more
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Q1. Explain about your project
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Q2. Explain the sequence to sequence models and transformers
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Sequence to sequence models are used in natural language processing to convert input sequences into output sequences.
• Sequence to sequence models are commonly used in machine translation tasks, where the input is a sentence in one language and the output is the translated sentence in another language.

• Transformers are a type of sequence to sequence model that use self-attention mechanisms to weigh the importance of diffe...read more
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Q1. Bias and variance with respect to model
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Bias and variance are two types of errors that can occur in a model.
• Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.

• Variance refers to the error introduced by modeling the noise in the training data, leading to overfitting.

• Balancing bias and variance is crucial for creating a model that generalizes well to unseen data.
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Q2. Hypotheses test
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Q1. Data explain about yourself
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I am a highly motivated and detail-oriented individual with a strong background in data analysis and problem-solving.
• Proficient in data manipulation and analysis using tools like Excel, SQL, and Python.

• Experienced in conducting statistical analysis and creating data visualizations.

• Strong problem-solving skills with the ability to identify patterns and trends in data.

• Excellent communication skills, both written and verb...read more
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Q2. Explain About yourself
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I am a highly motivated and detail-oriented individual with a strong background in finance and accounting.
• Bachelor's degree in Finance

• 3 years of experience in financial analysis

• Proficient in financial modeling and forecasting

• Strong analytical and problem-solving skills

• Excellent communication and interpersonal skills
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Q3. Explain knowledge and skills
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Knowledge and skills refer to the understanding and abilities that an individual possesses.
• Knowledge is the theoretical understanding of a subject or topic.

• Skills are the practical abilities to perform tasks or apply knowledge.

• Knowledge can be acquired through education, training, or experience.

• Skills are developed through practice and hands-on experience.

• Examples of knowledge include understanding of programming langu...read more
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Q4. Explain about qualification
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Qualification refers to the education, skills, and experience required for a particular job or role.
• Qualification includes academic degrees, certifications, and relevant work experience.

• It demonstrates the candidate's knowledge, expertise, and ability to perform the job.

• Examples of qualifications can be a Bachelor's degree in Computer Science, a certification in project management, or 5 years of experience in marketing...read more
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Q1. Knowledge about vacancy requirements
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