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A data science interview questions usually fall within two spectrums: technical ones (based on programming languages like Python for Data Science) and non-technical ones (projects you have worked on in the past and how they have fared). While experience-based questions check whether you were able to apply data science techniques to real-life problems, the technical questions (for which we will prepare you) test your quick thinking ability.
To help you ace your interviews for Data Science jobs, we have put together a list of commonly asked interview questions along with their detailed answers.
What Is the Difference Between Data Science, AI, and Machine Learning?
Machine Learning for Data Science and Analytics is a subpart of Artificial intelligence. The latter focuses on a range of applications such as Robotics and Text Analysis, while the former is an application of AI premised on the idea that we should provide machines access to data and let them learn on their own.
A commonly held misconception is that Data Science as a subpart of Machine Learning, but the truth is Data Science uses Machine Learning to predict and analyse an unknown future.
What Are the Methods Used to Assess a Sound Logistic Model?
Data Science and Big Data Analytics testify that there are primarily three ways to assess whether the results of logistic regression analysis are sound or not. These are:
•You can use Lift to assess the logistic model. It allows you to compare your model with a random selection.
•You can use Concordance to recognise the ability of the logistic model by distinguishing between the event not happening and the event happening.
•You can use Classification Matrix to look at the false positives and the true negatives.
How Do You Treat Missing Values in Analysis?
You can correctly recognise the extent of missing values by identifying the variables with missing values. There are three possibilities from here onwards:
•Identifying a pattern in this can result in valuable insights for your business.
•If there are no patterns identified, then the missing values will have to be substituted with a default value, which is a minimum, maximum, or mean value.
•If 80% of the values for a variable are missing, you can ignore the variable altogether.
Describe the Box Cox Transformation in a Regression Model
There is a chance that the variable used for response for a regression analysis may not satisfy the assumptions of the ordinary least squares regression. As you increase the predictions, the residuals may snowball out of control. To avoid this, it is important to fix the variable used for response so that the data meets the assumptions. This is where a Box Cox transformation comes in handy.
This technique changes non-normal or skewed dependent variables into normal ones under this setup, despite the data not being normal, all the statistics for Data Science assume normality. Applying a Box Cox transformation in regression models ultimately helps you run a broader number of tests.
Can You Use Microsoft Excel to Perform Logistic Regression?
There are two ways in which you can use Microsoft Excel to perform logistic regressions. These are:
•You can load an add-in into Excel. These are available on plenty of websites.
•You can utilise both Excel's computational prowess and the fundamentals of logistic regression to build a logistic regression in Excel.
We hope that reviewing some of these basic interview questions beforehand will help boost your confidence and face interviews for Data Science jobs with no iota of stress. Remember, preparation is the key to success!