Cognizer Data Scientist Interview Questions and Answers for Freshers

Explain dynamic programming with memoization

Q1. Where are you from, and why are you joining the company
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Q2. Why are you joining the company
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Q1. How do you handle outliers? How to handle imbalance dataset? Feature engineering techniques?
Ans. 
Outliers can be handled by removing, transforming or imputing them. Imbalanced datasets can be handled by resampling techniques. Feature engineering involves creating new features from existing ones.
• Outliers can be removed using statistical methods like z-score or IQR.

• Outliers can be transformed using techniques like log transformation or box-cox transformation.

• Outliers can be imputed using techniques like mean imputat...read more
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Q1. 1. Explain the project in detail 2. Explain me your 5 favourite models 3. Questions on probability
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Q1. It was a HR round and HR has asked me what's your salary expectations.
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Q1. What is hypothesis testing
Ans. 
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• It involves formulating a hypothesis about a population parameter, collecting data, and using statistical tests to determine if the data supports or rejects the hypothesis.

• There are two types of hypotheses: null hypothesis (H0) and alternative hypothesis (H1).

• Common statistical tests for hypothesis testing include...read more
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Q2. What is null hypothesis
Ans. 
Null hypothesis is a statement that there is no significant difference or relationship between variables being studied.
• Null hypothesis is typically denoted as H0 in statistical hypothesis testing.

• It is the default assumption that there is no effect or relationship.

• The alternative hypothesis (Ha) is the opposite of the null hypothesis.

• For example, in a study testing a new drug, the null hypothesis would be that the drug...read more
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Q3. What is supervised and unsupervised learning
Ans. 
Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.
• Supervised learning requires labeled data for training

• Unsupervised learning does not require labeled data

• Examples of supervised learning include classification and regression

• Examples of unsupervised learning include clustering and dimensionality reduction
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Q1. Normal Data Science questions
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Q1. Salary discussion and HR related queries
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