Indiafilings Data Scientist Interview Questions and Answers

Many Mcq,s.Similar to cat exam

Ml case study . Eg loan default prediction

Medium General Aptitude questions and technical(Big Data, Python etc.)

Q1. ML Algorithms (SVM, Random forest, bagging boosting, ridge, etc)
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Q1. Deep equations and understading of DL and ML Algorithms
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Understanding deep equations and algorithms in DL and ML is crucial for a data scientist.
• Deep learning involves complex neural network architectures like CNNs and RNNs.

• Machine learning algorithms include decision trees, SVM, k-means clustering, etc.

• Understanding the math behind algorithms helps in optimizing model performance.

• Equations like gradient descent, backpropagation, and loss functions are key concepts.

• Practica...read more
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Q1. How to extract numbers pre decimal point from a long list of decimalnumbers with efficiency
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Use string manipulation to efficiently extract numbers before the decimal point from a list of decimal numbers.
• Split each decimal number by the decimal point and extract the number before it

• Use regular expressions to match and extract numbers before the decimal point

• Iterate through the list and extract numbers using string manipulation functions
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Python test is taken

Q1. Salary discussion
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Q1. What is cross validation ?
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Cross validation is a technique used to assess the performance of a predictive model by splitting the data into training and testing sets multiple times.
• Cross validation helps to evaluate how well a model generalizes to new data.

• It involves splitting the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset.

• Common types of cross validation include k-fold cross validation and lea...read more
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Q1. Projects and Data Science concepts
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Q1. Python and coding skills
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Q1. Self Intro and projects discussion
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Q2. Feature selection methods
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Feature selection methods help in selecting the most relevant features for building predictive models.
• Feature selection methods aim to reduce the number of input variables to only those that are most relevant.

• Common methods include filter methods, wrapper methods, and embedded methods.

• Examples include Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Lasso regression.
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Q1. Central Limit Theorem
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Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
• The Central Limit Theorem is essential in statistics as it allows us to make inferences about a population based on a sample.

• It states that regardless of the shape of the population distribution, the sampling distribution of the sample mean will be approximately normally distribut...read more
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