Data Science Interview Challenge

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Data Science Interview Challenge

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Theoretical Questions

1. How do you handle overfitting in a model?

2. Explain bias-variance trade-off.

3. What is early stopping in the context of training a model?

4. How would you handle imbalanced datasets?

5. Which of the following best describes the difference between data normalization and scaling?

6. How does cross-validation work?

7. Which statement best describes the difference between precision and recall?

8. Which kind of models are utilized by the bagging ensemble method?

9. How does a Random Forest algorithm function?

10. Which of the following is not an ensemble method?

11. In which scenario is a high recall more important than high precision?

How do you handle overfitting in a model?

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Increasing the amount of training data.

By using data augmentation.

By using highly complex models.

By using a regularization technique.

Explain bias-variance trade-off.

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It's about choosing the optimal number of clusters in KMeans.

It's the balance between a model's flexibility and its ability to generalize to unseen data.

It's the trade-off between L1 and L2 regularization.

It's the decision between early stopping and training for more epochs.

What is early stopping in the context of training a model?

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It's a regularization technique involving the magnitude of coefficients.

It's a method to prevent overfitting by terminating training once a certain condition is met, often when the validation loss starts increasing.

It's a technique to balance datasets.

How would you handle imbalanced datasets?

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By using L1 or L2 regularization techniques.

With early stopping during training.

By using methods like oversampling, undersampling, or synthetic data generation.

By adding weights to classes.

Which of the following best describes the difference between data normalization and scaling?

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Normalization is about making data follow a Gaussian distribution while scaling adjusts the range of the data.

Scaling involves converting data to a range between 0 and 1, while normalization makes data zero-centered.

Both normalization and scaling adjust the range of data to between 0 and 1.

How does cross-validation work?

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It trains the model on the entire dataset and then tests on the same dataset.

It divides the dataset into 'k' equal partitions and uses each partition as a test set while the remaining parts as a training set, iterating 'k' times.

It trains the model once on a random subset and tests on the remaining data.

It tests the model 'k' times on the same validation set after training on the whole dataset.

Which statement best describes the difference between precision and recall?

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Precision is the number of true positives divided by the number of true positives plus false negatives, while recall is the number of true positives divided by the number of true positives plus false positives.

Precision measures the number of correct predictions, while recall measures the number of false predictions.

Precision focuses on the accuracy of the positive predictions, while recall focuses on capturing as many positives as possible.

Which kind of models are utilized by the bagging ensemble method?

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Different types of models for each ensemble.

Same type models for each ensemble.

Exclusively for Decision Trees.

Exclusively for shallow Neural Networks.

How does a Random Forest algorithm function?

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It builds multiple decision trees and averages their results.

It constructs a single decision tree with random feature selection.

It uses randomness to choose the depth of a single decision tree.

Which of the following is not an ensemble method?

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Boosting

Bagging

Stacking

Scaling

In which scenario is a high recall more important than high precision?

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Recommending products to online shoppers.

Filtering out spam emails.

Diagnosing a rare, deadly disease.

Identifying suitable advertisements for users based on their browsing history.

War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 7. Kapitel 6

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