In the rapidly evolving field of data science and analytics, job interviews can be a daunting hurdle, even for the most seasoned professionals. Altimetrik, a global digital transformation company, seeks individuals who are not only skilled in technical aspects but also adept at solving complex problems. To help you prepare, we’ve compiled a list of common interview questions and answers in data science and analytics that you might encounter at Altimetrik.
Table of Contents
Technical Interview Questions
Question: What is the confusion matrix?
Answer: A confusion matrix is a table used to evaluate the performance of a classification model. It summarizes the counts of true positive, true negative, false positive, and false negative predictions, providing insights into the model’s accuracy, precision, recall, and F1 score. It helps understand where the model is making correct and incorrect predictions in a binary or multiclass classification problem.
Question: What is regularization?
Answer: Regularization is a technique used in machine learning to prevent the overfitting of a model to the training data. It involves adding a penalty term to the model’s objective function, which discourages large coefficients or complex model structures. This helps to generalize the model better to unseen data by controlling its complexity and improving its performance on new, unseen examples. Common regularization methods include L1 (Lasso) and L2 (Ridge) regularization.
Question: How to fill in missing values?
Answer: Filling missing values in a dataset is crucial to ensure accurate analysis and modeling. Common techniques include:
- Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the column.
- Forward Fill or Backward Fill: Use the last known value (forward fill) or the next known value (backward fill) to fill missing values in time series data.
- Predictive Imputation: Use machine learning algorithms to predict missing values based on other features in the dataset.
- Hot-Deck Imputation: Replace missing values with values from similar rows or nearby observations.
- KNN Imputation: Replace missing values using the average of k-nearest neighbors’ values.
Question: What is accuracy in a model?
Answer: Accuracy in a model refers to the proportion of correctly classified instances out of the total instances evaluated. It’s a common evaluation metric used in classification problems. Mathematically, accuracy is calculated as the number of correct predictions divided by the total number of predictions made. However, accuracy alone may not be sufficient in cases of imbalanced datasets, where one class dominates the other. In such cases, additional metrics like precision, recall, and F1-score are often used for a more comprehensive evaluation.
Question: What is backpropagation?
Answer: Backpropagation is an algorithm used to train neural networks by adjusting weights and biases to minimize prediction errors. It works by calculating the gradients of the error concerning each parameter and updating them in the opposite direction of the gradient. This process iteratively improves the network’s ability to make accurate predictions through multiple training epochs.
Question: What is linear regression?
Answer: Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. The goal is to find the best-fitting line (or hyperplane in higher dimensions) that describes the linear relationship between the variables. In essence, it helps us understand how the value of the dependent variable changes as the independent variables vary.
Question: Explain Logistic regression.
Answer: Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable based on one or more independent variables. Unlike linear regression, which predicts a continuous outcome, logistic regression predicts the probability of the dependent variable belonging to a particular category.
Question: Difference between the probability density function and mass function?
Answer:
Probability Density Function (PDF):
- PDF is associated with continuous random variables.
- It represents the probability that a continuous random variable falls within a particular range of values.
- The area under the PDF curve over a given range represents the probability of the variable falling within that range.
- Example: Normal distribution curve represents a PDF.
Probability Mass Function (PMF):
- PMF is associated with discrete random variables.
- It gives the probability that a discrete random variable takes on a specific value.
- Each point on the PMF represents the probability of that specific value occurring.
- Example: For a fair six-sided die, the PMF would assign a probability of 1/6 to each of the possible outcomes (1, 2, 3, 4, 5, 6).
Python and SQL Interview Questions
Question: What is the difference between list and tuple in Python?
Answer: Lists are mutable, meaning their elements can be changed, added, or removed. Tuples are immutable, meaning their elements cannot be changed once defined.
Question: Explain the use of “lambda” functions in Python.
Answer: Lambda functions are anonymous functions defined using the lambda keyword. They are used for small, one-time operations without the need to define a full function.
Question: What is the purpose of the “yield” keyword in Python?
Answer: The yield keyword is used in generator functions to return a value without exiting the function. It allows the function to “yield” multiple values one at a time.
Question: What is the difference between “WHERE” and “HAVING” clauses in SQL?
Answer: The “WHERE” clause is used to filter rows based on a condition before grouping. The “HAVING” clause is used to filter groups based on a condition after grouping.
Question: Explain the difference between INNER JOIN and LEFT JOIN in SQL.
Answer: An INNER JOIN returns rows when there is at least one match in both tables. A LEFT JOIN returns all rows from the left table and the matched rows from the right table, with unmatched rows in the right table showing NULL values.
Question: How do you find the second-highest salary in an Employee table using SQL?
Answer:
SELECT MAX(Salary) AS SecondHighestSalary FROM Employees
WHERE Salary < (SELECT MAX(Salary) FROM Employees);
Question: What is a subquery in SQL?
Answer: A subquery is a query nested within another query. It can be used to return data that will be used in the main query’s condition, filter, or computation.
ML and Statistics Interview Questions
Question: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on labeled data, where the model learns to map input data to known output labels. Unsupervised learning involves training on unlabeled data, where the model finds patterns and relationships in the data without explicit output labels.
Question: Explain the bias-variance tradeoff in machine learning.
Answer: The bias-variance tradeoff refers to the tradeoff between a model’s ability to capture the complexity of the underlying data and its ability to generalize to unseen data. A high-bias model is overly simple and may underfit the data, while a high-variance model is overly complex and may overfit the data.
Question: What are some common regularization techniques used in machine learning?
Answer: Common regularization techniques include L1 regularization (Lasso), L2 regularization (Ridge), and ElasticNet regularization. These techniques add penalty terms to the model’s objective function to prevent overfitting by discouraging large coefficients.
Question: What is the Central Limit Theorem?
Answer: The Central Limit Theorem states that the distribution of sample means from a population with any distribution approaches a normal distribution as the sample size increases, regardless of the original distribution’s shape.
Question: Explain the difference between Type I and Type II errors.
Answer: Type I error (false positive) occurs when a true null hypothesis is incorrectly rejected. Type II error (false negative) occurs when a false null hypothesis is not rejected.
Question: What is the p-value in statistics?
Answer: The p-value is the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true. It is used to determine the significance of results in hypothesis testing.
Question: What is the purpose of hypothesis testing in statistics?
Answer: Hypothesis testing is used to make inferences about population parameters based on sample data. It involves formulating null and alternative hypotheses, collecting data, and using statistical tests to determine whether to reject or fail to reject the null hypothesis.
Technical Interview Topics
- Python, SQL, Machine learning.
- Simple Machine learning questions
- Basic questions on Statistics.
- NLP, Machine learning algorithms
- Data preparation techniques, Python.
Conclusion
Altimetrik looks for candidates who not only have the technical know-how but can also apply their knowledge to solve real-world problems. To stand out, be prepared to discuss past projects or experiences that showcase your problem-solving skills and your ability to work as part of a team.
Remember, the key to a successful interview lies in preparation, understanding the fundamentals, and being able to apply theoretical knowledge to practical scenarios. Best of luck in your data science and analytics interviews at Altimetrik!
