Here is the list of Data Science Interview Questions, one needs to prepare:
General Data Science Interview Questions
Primarily, we will discuss some General Data Science Interview Questions which the candidate must nail:
What are the assumptions required for a Linear Regression?
A linear regression makes four assumptions:
- Linear Relationship: The independent variable x and the dependent variable y should have a linear relationship.
- Independence: There is no correlation between consecutive residuals, indicating independence. It is most common in time-series data.
- Homoscedasticity: the variance should be constant at all levels of x.
- Normality: the residuals are distributed normally.
How do you deal with a dataset that is missing several values?
There are several approaches to dealing with missing data. You may:
- Remove the rows that have missing values.
- Remove the columns that have several missing values.
- Fill in the blanks with a string or numerical constant.
- Replace the missing values with the column’s average or median value.
- To estimate a missing value, use multiple regression analyses.
- Replace missing values with average simulated values and random errors using multiple columns.
How do you explain the technical aspects of your findings to non-technical stakeholders?
First, learn more about the stakeholder’s background and use that knowledge to modify your wording. Learn about commonly used financial terms and use them to explain the complex methodology if he has a finance background.
Second, you must make extensive use of visuals and graphs. People are visual learners because they learn so much better when they use creative communication tools.
Third, speak in terms of outcomes. No attempt to explain the methodologies or statistics. Concentrate on how they can use the results of the analysis to improve the business or workflow.
Finally, encourage them to question you. People are afraid, if not embarrassed, to ask questions about unfamiliar subjects. Engage them in the discussion to establish a two-way communication channel. To have more idea about the technical questions, one can take a Data Science course in Bangalore, that offers complete information related to every topic.
Data Science Technical Interview Questions
What is the purpose of regularization in machine learning, and how does it help prevent overfitting?
Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the cost function. It involves introducing a regularization parameter (often denoted as lambda) that controls the strength of the penalty.
Regularization methods, such as L1 (Lasso) and L2 (Ridge), penalize large coefficients in the model, encouraging simpler and more generalizable models. This helps prevent overfitting, where the model performs well on the training data but fails to generalize to new, unseen data.
Explain the concept of cross-validation and why it is essential in model evaluation
To answer these Data Science Questions you can start by explaining Cross-validation. Cross-validation is a technique used to assess the performance of a machine learning model by partitioning the dataset into subsets for training and testing. The most common form is k-fold cross-validation, where the data is divided into k equally sized folds.
The model is trained on k-1 folds and tested on the remaining fold, repeating this process k times. This helps in obtaining a more robust performance estimate, as each data point is used for both training and testing. Cross-validation provides a better indication of how well the model generalizes to new data, reducing the impact of the specific data split on the evaluation.
What is the curse of dimensionality, and how does it affect machine learning models?
This is one of the important Data Scientist Interview Questions, to answer this question you can begin like: The curse of dimensionality refers to the challenges and issues that arise when dealing with high-dimensional data. As the number of features or dimensions increases, the amount of data needed to generalize accurately grows exponentially. This can lead to sparse data distributions, increased computational complexity, and the risk of overfitting.
Machine learning models may struggle to find meaningful patterns in high-dimensional spaces, and the performance on training data may not translate well to new, unseen data. Techniques such as feature selection and dimensionality reduction are often employed to mitigate the curse of dimensionality.
Differentiate between bagging and boosting in ensemble learning.
Bagging (Bootstrap Aggregating):
Bagging involves training multiple instances of the same learning algorithm on different bootstrap samples (random samples with replacement) from the training data. The final prediction is typically an average or a vote over the predictions of individual models. Random Forest is a common example of a bagging algorithm.
Boosting:
Boosting focuses on sequential training of multiple weak learners, where each model corrects the errors of its predecessor. Instances that are misclassified by the current model are given higher weights in subsequent models, emphasizing the challenging cases. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Explain the concept of word embedding in natural language processing (NLP) and mention a popular word embedding technique.
Word embedding is a technique in NLP that represents words as vectors in a continuous vector space, capturing semantic relationships between words. One popular word embedding technique is Word2Vec. Word2Vec uses neural networks to learn distributed representations of words based on their context in a given corpus. It preserves semantic relationships, allowing words with similar meanings to have similar vector representations.
Word embeddings are valuable in various NLP tasks, such as sentiment analysis, machine translation, and named entity recognition, as they capture the contextual meaning of words in a more meaningful way than traditional methods.
Coding Data Science Interview Questions
Given a dictionary consisting of many roots and a sentence, stem all the words in the sentence with the root forming it.
The function will take two arguments: a list of root words and a sentence.
Code
roots = [“cat”, “bat”, “rat”]
sentence = “the cattle was rattled by the battery”
Output:
It should return the sentence with root words.
“the cat was rat by the bat”
Solution:
def replace_words(roots, sentence):
words = sentence.split(” “)
for index, word in enumerate(words):
for root in roots:
if word.startswith(root):
words[index] = root
return ” “.join(words)
Given a string, determine if it’s a palindrome after lowering all letters and removing non-alphanumeric characters.
Input:
The function takes a string as input.
code
text = “Anna”
Output:
It should return True if it’s a palindrome, else False.
Solution:
import re
def is_palindrome(text):
text = text.lower()
rx = re.compile(‘\W+’)
text = rx.sub(”, text).strip()
rev = ”.join(reversed(text))
return text == rev
Given a dataset of test scores, write Pandas code to return the cumulative percentage of students that received scores within specified buckets.
Input:
The dataset has user_id, grade, and test_score columns.
Output:
The function should return a data frame with grades, bucket scores, and cumulative percentage of students getting bucket scores.
Solution:
def bucket_test_scores(df):
# Implementation
Explain what confidence intervals are in the context of statistical experiments.
Confidence intervals are a range of estimates for an unknown parameter that you expect to fall within a certain percentage of the time when the experiment is repeated or re-sampled. It is commonly expressed at a 95% confidence level, indicating that the interval contains the true parameter in 95% of such intervals. Confidence intervals are used in various statistical estimates, such as proportions, population means, differences between means, or proportions, and estimates of variation among groups.
Explain how you would manage an unbalanced dataset in machine learning.
To handle an unbalanced dataset, several techniques can be employed:
Undersampling: Resample the majority class features to make them equal to the minority class features.
Oversampling: Resample the minority class features to make them equal to the majority class features.
Creating synthetic data: Use techniques like SMOTE (Synthetic Minority Oversampling Technique) to create synthetic data points.
Combination of under and over-sampling: Use methods like SMOTEENN (SMOTE and Edited Nearest Neighbors) for a combination of over-sampling and cleaning.
Amazon Data Science Interview Questions
Explain the concept of confidence intervals.
The confidence interval is a range of estimates for an unknown parameter that you expect to fall within a certain percentage of the time when repeating the experiment or re-sampling the population.
The 95% confidence level is commonly used in statistical experiments and represents the percentage of times you expect an estimated parameter to be reproduced. The alpha value determines the upper and lower bounds of the confidence intervals.
Confidence intervals can be used to estimate proportions, population means, differences between population means or proportions, and estimates of variation among groups.
Complete our Statistical Thinking in Python (Part 1) course to lay the groundwork for statistics.
How do you manage an unbalanced dataset?
In machine learning, unbalanced datasets can lead to biased models, especially when the minority class is crucial. Several techniques can be used to manage unbalanced datasets:
Undersampling:
- Resample the majority class features to make them equal to the minority class features.
- Helps balance the class distribution, but may lead to a loss of information.
Code:
from imblearn.under_sampling import RandomUnderSampler
RUS = RandomUnderSampler(random_state=1)
X_US, y_US = RUS.fit_resample(X_train, y_train)
Oversampling:
- Resample the minority class features to make them equal to the majority class features.
- Methods include repetition or weightage repetition of the minority class features.
Code:
from imblearn.over_sampling import RandomOverSampler
ROS = RandomOverSampler(random_state=0)
X_OS, y_OS = ROS.fit_resample(X_train, y_train)
Creating Synthetic Data:
- Use techniques like SMOTE (Synthetic Minority Oversampling Technique) to create synthetic data points.
- Addresses the problem of repetition in oversampling.
Code:
from imblearn.over_sampling import SMOTE
SM = SMOTE(random_state=1)
X_OS, y_OS = SM.fit_resample(X_train, y_train)
Combination of Under and Over Sampling:
- Improve model biases and performance by using a combination of over and under-sampling.
- Methods like SMOTEENN (SMOTE and Edited Nearest Neighbours) provide automatic functionality.
Code:
from imblearn.combine import SMOTEENN
SMTN = SMOTEENN(random_state=0)
X_OUS, y_OUS = SMTN.fit_resample(X_train, y_train)
Query to Return Total Sales for Each Product (March 2023):
Assuming a table named orders with columns product_id, qty, and order_dt, the SQL query would be:
Code:
SELECT
product_id,
SUM(qty) AS total_sales
FROM
orders
WHERE
order_dt >= ‘2023-03-01’
AND order_dt < ‘2023-04-01’
GROUP BY
product_id;
This query retrieves the product_id and the total quantity (SUM(qty)) sold for each product within the specified date range, filtering for March 2023. To learn more about the AI, SQL or other course one can join Introtallents Artificial Intelligence Training in Bangalore.