A multidisciplinary approach created to extract actionable insights from large volumes of data, Data Science is key to making the most of generated information.
This blog covers the top 30 questions and answers most discussed in Data Science interviews. These will ensure you are well-equipped for the responsibilities associated with employment in this lucrative field!
Top Data Science Interview Questions and Answers for 2 Years Experience
A data scientist’s primary responsibility is to apply scientific, statistical and mathematical methods to raw information.
To better illustrate your experience and knowledge in the field, discover 28 of the most-asked questions with answers in Data Science interviews below:
Q1. What is Data Science?
Answer – Data Science is a combination of machine-learning principles, algorithms and tools to discover patterns hidden in raw data.
Q2. What are the differences between supervised and unsupervised learning?
Answer – Supervised learning used input data that is labelled and a training data set. Unsupervised learning on the other hand is unlabelled and uses input data set.
Q3. How will you deal with unbalanced binary classification?
Answer – The best way to deal with unbalanced binary classification is by reconsidering the metrics involved in evaluating your model.
Q4. What is the difference between a histogram and a box plot?
Answer – Histograms show the frequency of a variable and its values and are used to determine the probability distribution. Box plots, on the other hand, are used to gather information like outliers, range and quartiles.
Q5. If you are given a data set that has variables with more than 30% missing, how will you interpret the data?
Answer – The easiest way to handle data values that are missing is by removing the rows with the missing information.
Q6. What is cross-validation?
Answer – A technique used to assess how a model performs with a new independent dataset.
Q7. What is logistic regression?
Answer – Logistic regression is also referred to as the logit model. This is a method that forecasts the binary outcome from a combination of linear predictor variables.
Q8. What is the disadvantage of using the linear model?
Answer – One of the disadvantages of using the linear model is that it cannot be used for binary or count results.
Q9. What is selection bias?
Answer – This is an error conducted by a researcher who decides what sample is going to be studied. With selection bias, participants are not random.
Q10. List the steps to maintain a deployed model
Answer – The steps to maintain a deployed model are – monitor, evaluating, compare and rebuilding.
Q11. What is a false positive and a false negative?
Answer – A false positive occurs when an incorrect identification is made of an absent condition. A false negative in the incorrect identification (absence) of the condition when it is present.
Q12. If you have to generate a predictive model using multiple regression, how will you validate the model?
Answer – Adjusting R-squared can be a good way to validate the model. Another way to validate the model would be to use cross-validation.
Q13. What is dimensionality reduction?
Answer – Dimensionality reduction refers to the conversion of a data set with significant dimensions into a data set with fewer fields.
Q14. What is a confusion matrix?
Answer – A confusion matric is a table of dimensions 2X2 that contains the 4 outputs supplied by a binary classifier.
Q15. How should outlier values be ideally treated?
Answer – Outliers are dropped only if it is a garbage value. If the outlier has extreme values, it can be removed.
Q16. Can time-series data be declared stationery?
Answer – Yes, time-series data can be declared stationary if the mean and variance of the series are constant with time.
Q17. What is NLP?
Answer – NLP is the acronym for Natural Language Processing. It is an artificial intelligence brand that endows a machine with the ability to comprehend human language.
Q18. What is ANN?
Answer – ANN is Artificial Neural Networks. It is a special group of algorithms that have led to significant development in machine learning, allowing adaption according to changes in input.
Q19. What is Random Forest?
Answer – Random Forest is a method of machine learning that helps perform a wide range of classification and regressions tasks.
Q20. Is dimension reduction important?
Answer – There are significant benefits to dimension reduction. These include a reduction in time and storage space and the removal of multicollinearity.
Q21. What are feature vectors?
Answer – Feature vectors are n-dimensional vectors that represent an object with numerical features. In machine learning, feature vectors are often used to represent symbolic and numeric characteristics of an object.
Q22. What is the difference between true positive rate and false-positive rate?
Answer – True Positive Rate (TPR) is the ratio depicting True Positive to False Negatives and True Positives. The False Positive Rate (FPR) is the ratio of False Positive to False Positives and True Positives.
Q23. What is root cause analysis?
Answer – Root cause analysis is a problem-solving technique that is used to isolate the root cause of problems or faults.
Q24. What are recommender systems?
Answer – Recommender systems are a subclass of information filtering systems that predict the rating a user may give to a product.
Q25. What kind of problems would you use for Principal Component Analysis?
Answer – Principle Component Analysis is most commonly used for easily summarizing data, visualization purposes, reducing memory and speeding up an algorithm.
Q26. Do you think a random forest is better than a decision tree?
Answer – Yes, random forest is better than decision tree because is a lot more accurate and robust, with less likely potential for overfitting.
Q27. Explain K-means clustering methods?
Answer – K-means clustering method is a technique to classify data using a group of clusters called K-clusters.
Q28. What is the p-value?
Answer – p-value allows you to test the strength of your results when conducting a hypothesis test in Statistics.
From significant career opportunities for growth and development to high earning potential, the benefits associated with Data Science are many.
Made Academy offers a reputed Data Science program that trains students on a list of scientific and statistical fundamentals. Students learn how to apply data science in practical business scenarios, making them adept at forming excellent business strategies. With an emphasis on data visualization and practical data analysis, students of the Made Academy Data Science program are exposed to the practical workings of the field in the classroom.
If this sounds like its right up your alley, call the following number for more information on the course:
+91 9513505501 / (080) 26794741
You can also fill the application form for additional information on the Data Science program.