HSBC Bank Data Scientist Interview Questions and Answers

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.

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

CNN is used for image recognition while MLP is used for general classification tasks.

• CNN uses convolutional layers to extract features from images while MLP uses fully connected layers.

• CNN is better suited for tasks that require spatial understanding like object detection while MLP is better for tabular data.

• CNN has fewer parameters than MLP due to weight sharing in convolutional layers.

• CNN can handle input of varying

Recommendation engines analyze user data to suggest items based on preferences and behavior.

• Recommendation engines use collaborative filtering to suggest items based on user behavior and preferences.

• They can also use content-based filtering to recommend items similar to ones the user has liked in the past.

• Some recommendation engines combine both collaborative and content-based filtering for more accurate suggestions.

• Ex...read more