The scope of Data Science domain is soaring, creating a huge demand and opportunities for data scientists, data science developers and data analytics professionals. If you have already decided to take your career in this domain, you need to prepare yourself with these interview questions listed here in this article.
Table of Contents
- Top Data Science Interview Questions You Should be Prepared for
- Concluding Lines
Top Data Science Interview Questions You Should be Prepared for
Distinguish between Supervised and Unsupervised Learning.
In supervised learning, input data is labelled, whereas, in unsupervised learning, input data is unlabelled. Supervised uses a training data set, while unsupervised uses the input data set. Supervised learning is best suited for making predictions. Unsupervised, on the other hand, is used for analysis. Supervised enables classification and regression, unsupervised n the other hand enables Classification, Density Estimation, & Dimension Reduction.
What are the two main methods for feature selection?
There are mainly two methods, one is filter methods, and the other is wrapper methods.
Filter Methods
This method involves
- Linear discrimination analysis
- ANOVA
- Chi-Square
The best analogy for selecting features is “bad data in, bad answer out.”
Wrapper Methods
This method involves:
- Forward Selection: It involves testing one feature at a time until and unless a good fit is obtained.
- Backward Selection: It is the opposite method of forward selection. Features are tested and then removed to see whatever works better
- Recursive Feature Elimination: Recursively looks for all the features and how they pair together.
How to avoid overfitting your model?
Overfitting means it is only set for a small amount of data and may therefore fail to fit additional data or predict future observations.
There are three main methods to avoid overfitting:
- Use cross-validation techniques, such as k folds cross-validation.
- Train with more data as it can help algorithms detect the signal better.
- Remove irrelevant input features.
- Use regularization techniques, such as LASSO, that penalize certain model parameters if there are chances of overfitting.
Explain Normal Distribution?
Normal Distribution is the most common probability distribution where random variables are distributed in the form of the symmetrical, bell-shaped curve. Unlike other probability distributions that change their properties after a transformation, Normal Distribution retains the normal shape throughout.
Normal Distribution is Unimodal, Symmetrical, Asymptotic and Mean Mode, and Median are all located in the center.
Explain the role of data cleaning in the analysis.
Data cleansing or scrubbing is all about correcting and removing inaccurate data.
Cleaning data from multiple sources to transform it into the desired format is a cumbersome process, and it can take around 80% of the time for just cleaning data. Data cleaning is crucial because wrong data can drive a business to wrong decisions and poor analysis.
Explain dimensionality reduction. Are there any benefits?
Dimensionality reduction is the process of converting a data set with vast dimensions into fewer dimensions. This process is carried in order to convey similar information but in a precise manner. Dimensionality reduction not only helps in compressing data but also helps in reducing storage space, computation time and eradicates redundant features.
Differentiate between univariate, bivariate, and multivariate analysis.
Univariate, bivariate, and multivariate are all descriptive statistical analysis techniques that can be distinguished on the basis of variables involved at a given point in time.
If only one variable is involved, for example, the pie charts of sales based on region involving only one variable are referred to as univariate analysis. The main purpose of such analysis is to describe the data and find patterns that exist within it. This technique is used to find out whether there is any relationship between the two variables or not.
Analysis that deals with the study of more than one variable to understand the effect of variables on the responses are termed as bivariate analysis. If more than two variables are involved in understanding the effect of variables on the responses, it is known as multivariate analysis. Some of these methods are Multidimensional Scaling, Multiple Regression Analysis, Partial Least Square Regression, and many others.
What is the difference between Cluster and Systematic Sampling?
Cluster sampling is applied when it becomes difficult to study the target population spread across a wider area. This type of sampling divides the population into groups/clusters and then takes a random sample from each cluster.
Systematic sampling, on the other hand, is a statistical technique where elements are selected from an ordered sampling frame. This sampling involves selecting fixed intervals from the larger population to create the sample.
Concluding Lines
Hope these Data Science interview questions and answers will help you mentally prepare answers for them and land your dream job as a Data Science Developer or a Data Science Analytics.
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