![]() ![]() Not just this, a data scientist gets exposure to work in diverse domains, solving real-life practical problems all by making use of trendy technologies. Data is considered the new oil of the future which when analyzed and harnessed properly can prove to be very beneficial to the stakeholders. Over the years, data science has gained widespread importance due to the importance of data. Is python and SQL enough for data science? Are coding questions asked in data science interviews? What are the top 3 technical skills of a data scientist? How do I prepare for a data science interview? How is the grid search parameter different from the random search tuning strategy? What is the importance of dimensionality reduction? How is feature selection performed using the regularization method? What are various assumptions used in linear regression? What would happen if they are violated? Is it good to do dimensionality reduction before fitting a Support Vector Model? Give one example where both false positives and false negatives are important equally? What are some examples when false positive has proven important than false negative? Estimate the probability of getting a head in the next coin toss. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Toss the selected coin 10 times from a jar of 1000 coins. Evaluate the probability of finding at least one shooting star in a one-hour duration? Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. What is better - random forest or multiple decision trees? How will you balance/correct imbalanced data? Differentiate between box plot and histogram. What do you understand by a kernel trick? What is the difference between the Test set and validation set? What are the differences between univariate, bivariate and multivariate analysis? What does the ROC Curve represent and how to create it? How will you treat missing values during data analysis? Will treating categorical variables as continuous variables result in a better predictive model? During analysis, how do you treat the missing values? What are the available feature selection methods for selecting the right variables for building efficient predictive models? Why is data cleaning crucial? How do you clean the data? ![]() How regularly must we update an algorithm in the field of machine learning? How do you approach solving any data analytics based project? What are the differences between correlation and covariance? Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset? Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning? What is the p-value and what does it indicate in the Null Hypothesis? What are Exploding Gradients and Vanishing Gradients? Let’s say your laptop’s RAM is only 4GB and you want to train your model on 10GB data set. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. What are Support Vectors in SVM (Support Vector Machine)? ![]() What are RMSE and MSE in a linear regression model? How are the time series problems different from other regression problems? What is a Gradient and Gradient Descent?ĭata Science Interview Questions for Experienced What is deep learning? What is the difference between deep learning and machine learning? What is the percentage chance of you seeing at least one star shooting from the sky if you are under it for about an hour? In a time interval of 15-minutes, the probability that you may see a shooting star or a bunch of them is 0.2. What is a random forest? Explain it’s working. What is Linear Regression? What are some of the major drawbacks of the linear model? What is logistic regression? State an example where you have recently used logistic regression. Define the terms KPI, lift, model fitting, robustness and DOE. What do you understand by Survivorship Bias? Are there any differences between the expected value and mean value? What do you understand by Imbalanced Data? What does it mean when the p-values are high and low? Differentiate between the long and wide format data. List down the conditions for Overfitting and Underfitting. What are some of the techniques used for sampling? What is the main advantage of sampling? What is the difference between data analytics and data science? Data Science Interview Questions for Freshers
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