Interviews can be intimidating, especially when you are just one step away from your dream job. The pressure of cracking an interview is nothing less than the excitement of starting a professional journey. Perhaps, it is even more than that. To help you pass your Data Science interview smoothly and gain some confidence for the D-Day, we have compiled some commonly asked Data Science interview questions.
By reviewing these frequently asked interview questions and their answers, you will have a good idea about the actual interview. Plus, It will enhance your understanding of the subject, and groom you for the first impression.
You can literally impress your potential employers with your knowledge by cracking the interview in one go!
Data Science Interview Questions
Q.1 What is the main advantage of sampling? Mention some techniques used for it.
It can be difficult to Analyse the whole Data at once, especially when the Dataset is too big to manage. Therefore, it is important to obtain some data samples that represent the entire population to simplify the process. While doing this, it is necessary to carefully take sample data out of the large data that truly represents the entire dataset.
Based on statistical application, there are mainly two types of sampling techniques:
Probability Sampling techniques: Simple random sampling, Clustered sampling, and Stratified sampling.
Non-Probability Sampling techniques: Convenience sampling, snowball sampling, Quota sampling, etc.
Q2. Explain the Dimensionality Reduction.
Dimensionality reduction refers to the process of converting large-dimensional data sets into smaller ones (fields) that concisely convey similar information.
It helps in reducing data storage space as well as calculation time as there are few variables to measure. It eliminates repetitive features, like, storing value in one unit(meters) rather than using two different units (meters and inches).
Q3. What do you understand about recommender systems?
A recommender system, also known as a recommendation engine, is a subclass of an information filtering system that predicts the user’s preference for a particular product. It is widely used in various consumer-centric industries for suggesting products to their customers.
Recommendation systems can work on a single variable, like movies, or on multiple variables like books, search inputs, news, etc. Many companies use predictive analysis to develop their recommendation system and personalize their user experience.
Q4. Which feature selection methods are used for selecting variables?
The filter and the wrapper method are the two main feature selection techniques used to pick the right variable.
Filter Methods include selecting features directly from a dataset for a machine learning algorithm. These methods depend on the properties of the variables to separate features from the data before learning begins. It involves techniques like Linear discrimination analysis, Chi-Square, and ANOVA.
However, Wrapper methods work by assessing a subset of features by applying machine learning algorithms. It uses a search strategy to study the space of possible subsets of features and evaluate each subset according to the quality of performance of a given algorithm. It includes techniques like Forward Selection, Backward Selection, and Recursive Feature Elimination.
Q5. What is the Difference between Classification and Regression?
The major difference between Classification and Regression is that Classification predicts the discrete labels, whereas Regression produces continuous quantitative values. In the Classification process, we pay more attention to accuracy, while in Regression, we focus more on the error term.
Q6. Differentiate between Point Estimates and Confidence Intervals?
The point estimate provides us with a specific value as an estimate of a population parameter. The moment method and the maximum likelihood estimation are used to obtain Point Estimators for the population parameters.
On the other hand, confidence intervals provide us with a range of values that may include the population parameter. We mostly prefer the confidence interval as it shows the probability of an interval to contain the population parameter. This probability is called Confidence Level or Confidence coefficient, represented by 1 — alpha, where alpha is the significance level.
Q7. Why do you perform A/B Testing?
A/B testing is a hypothesis analysis for randomized experiments that includes two variables A and B.
The objective is to identify any changes made to the website to maximize or increase the results. A/B testing is widely used to determine the best online marketing and promotion strategies for businesses. For example, it can help in defining the click-through rate for a banner ad.
Q8. What do you understand by a p-value?
A p-value is a number between 0-1. It is helpful when you perform a hypothesis test in statistics to represent the strength of the result. The record being tested is known as the Null Hypothesis.
A Low p-value (≤ 0.05) shows strength against the null hypothesis which means it can be rejected. Whereas a High p-value (≥ 0.05) suggests the strength for the null hypothesis, which means it can be accepted. A p-value of 0.05 means that the Hypothesis could go either way.
Q9. ‘People who bought this also bought…’ Which algorithm does Amazon use for these recommendations?
Amazon uses an “item-based collaborative filtering” algorithm for its recommendation system. It describes the user behaviour by analyzing their purchase history, ratings, wishlist, etc. In this algorithm, the item feature is unknown. The system predicts what might interest a buyer based on the preferences of other users.
Amazon also suggests ‘brought together’ items to add to your cart. For example, a certain number of people bought a laptop and a laptop bag at the same time. Next time, when someone buys a laptop, they might get recommendations to purchase a bag as well.
Q10. How should you maintain a deployed model?
We can maintain a deployed model by following these steps:
Monitoring: You need to monitor every model constantly in order to maintain accuracy in their performance. If something changes, you should know how these changes will affect the model.
Evaluating: You need to calculate the evaluation metrics to decide whether it will require a new algorithm.
Comparing: Compare the new models in order to determine which of them produces the best results.
Rebuilding: The last step is to re-build the best-performing model on the current state of data.
Data Science is a Rewarding Career
The job of a Data Scientist can be challenging and stressful, but it is rewarding at the same time. According to Glassdoor, a data scientist’s average salary is ₹9,00,000, and a fresher can easily earn around ₹500,000 per annum. In addition, ample new opportunities and job roles are also emerging in the Data Science field.
These commonly asked Data Science interview questions can help you increase your confidence for your job interview and get you closer to your dream career.
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