Data science is a combination of computer science and data mining. During a data science interview, you may be faced with questions based on the fundamentals of computing and working with data. So, if you are looking for the types of questions you are asked in a data science interview, this article is for you. In this article, I’ll walk you through some of the very important data science interview questions and answers you should know.
Data Science Interview Questions and Answers
Most data science interview questions are based on the fundamentals of working with data which includes questions based on:
- Data management
- Data Science tools and technologies
- Machine Learning algorithms
- Deep Learning if you have some hands-on experience
So below are some of the most important data science interview questions and their answers that you should know.
What are the assumptions of the linear regression algorithm?
Below are the assumptions of the linear regression algorithm that you should know:
- There is a linear relationship between dependent and independent features.
- All the features are multivariate normally.
- There is very little or no multicollinearity in the dataset.
- There is very little or no autocorrelation in the dataset.
- It also assumes that there is homoscedasticity in the data set.
Why is the Naive Bayes algorithm known as naive?
The Naive Bayes algorithm is said to be naive because of its naive assumption which implies that the conditional independence of causes. Simply put, the presence of one cause is not normally independent of the presence of other causes. This can be considered very difficult to accept in many cases where the probability of a particular feature is strictly correlated with another feature.
How to choose between Normalization or Standardization to scale the features of a dataset?
During normalization, the values are shifted and resized so that they end up being between o and 1. The standardization method first subtracts the mean value and then divides it by standard deviation so that the resulting distribution of the features has a mean as 0 and standard deviation as 1. You need to use normalization when the dataset does not follow a normal distribution, and you need to use standardization when the dataset follows a normal distribution.
What questions do you always solve when exploring a dataset?
To explore your data, you can ask questions from the dataset to understand how you should prepare your data to train machine learning models or gather insights from the data to add value to an organization. Here are some of the questions you should always ask about your data when working on a data science project:
- Is the dataset skewed towards a range of values?
- Are there any missing values in the dataset? If yes, what approach you should choose to fill the missing values?
- Are there any outliers in the data? If yes, then how you will handle those outliers?
- What approach you should use to rescale the data?
- How to handle the long tail of categorical features?
How is Sparse PCA different from a standard PCA?
Using the standard PCA, we can only select the most important midrange features, assuming each instance can be rebuilt using the same components. By using a sparse PCA, we can use a limited number of components, but without the limitation given by a dense projection matrix. So, by using the power of sparse PCA, we can solve many more dimensionality reduction problems more efficiently than standard PCA.
How to convert textual data into numerical data?
Below is how to convert a textual dataset into numerical values:
aman are doing good hi hope how is kharwal my name you 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1 2 1 0 0 0 0 0 0 1 1 1 1 0
What Bias and Variance tell about a machine learning model?
Bias is the difference between predicted values and expected results. A machine learning model with a low bias is a perfect model and a model with a high bias is expected with a high error rate on the training and test sets. Variance is the variability of your model’s predictions over different sets of data. A machine learning model with high variance indicates that the model may work well on the data it was trained on, but it will not generalize well on the dataset it has never seen before.
How to calculate the bias and variance of a machine learning model?
Below is how you can calculate the bias and variance of a machine learning model:
What is Overfitting and how to avoid it?
Overfitting means the machine learning model performed very well on the training data but does not generalize well. This happens when the model is very complex compared to the amount and noise of the training dataset.
Here are some of the steps you can take to avoid overfitting:
- Simplify the machine learning model by selecting one of the fewer parameters by reducing the number of features in the training dataset or by constraining the model.
- Collect more training data and if you have a limited amount of data, increase the size of the training data.
- Remove outliers and explore your data further to correct more data errors.
Which strategy you should choose to fill in missing values and why?
The first strategy is to remove the entire row containing a missing value. This is not a bad idea, but it can only be considered when the data is very large. If removing missing values results in a data shortage, then this will not be an ideal dataset for any data science task. This is where the second strategy comes in, which is to fill in the missing values according to the other known values. This strategy can be considered in any type of dataset.
So these are the type of questions you may face in your data science interview. Before appearing for your interview you should practice some questions based on data structures and algorithms also. You can find some coding interview questions based on data structures and algorithms from here. I hope you liked this article on the Data science interview questions and answers you should know. Feel free to ask your valuable questions in the comments section below.