Data Science Interview Questions

Data Science Interview Questions and Answers

Data science interview questions and answers are helpful at the time of giving an interview on data science. Data science is used in multiple sectors. A career in data science is highly demanded in today’s life. While giving the interview on data science technology following questions and answers are very helpful. At the time of applying for the post of data scientist then, we need to prepare the question and answer.

Data Science Interview Questions and Answers

Top 15 Data Science Interview Questions and Answers

Below are the top question and answers to Data Science. These questions are helpful while giving mock tests or interviews.

Q1. What is data science?

Answer:

Data science combines AI, ML, math, and statistical programs. Data science is an application-specific analytic technique. Data science is application-specific pipelines that extract the data for decision-making and strategic planning. Data science is used for writing algorithms and building statistical models. Data science discovers new questions for driving innovation.

Q2. How we are defining the logistic regression?

Answer:

Logistic regression in data science measures the relationship between dependent and independent variables. In data science, we are estimating the probability by using the logistic function. We are using logistic regression in data science at the time of developing new algorithms. Logistic regression is widely used in data science.

Q3. How data science will differ from traditional application programming?

Answer:

Data science takes a different approach at the time of building systems that provide value in application development. In traditional programming, we use an input analyzer to figure out the expected output. In data science, we are writing the code which contains statements and rules needed to transform the provided input. The process of rule generation in data science is called training.

Q4. What is the use of supervised machine learning?

Answer:

Supervised ML is used to create models that are employed for classifying and predicting things. The supervised learning model works with the data which contains input as well as expected outputs. The supervised ML algorithms contain the labeled data. We are commonly using supervised ML algorithms in linear regression.

Q5. What is the use of unsupervised machine learning?

Answer:

This type of machine learning is useful to extract information that is meaningful for large data. This algorithm works on the data which does not contain the mappings from input as well as output. It contains unlabeled data. It will be used in k-means clustering algorithms.

Q6 – What is a bias error in data science?

Answer:

Bias is nothing but an error that occurs in data science when our algorithms are not enough for capturing trends of data. This error also occurs when data is not understood by our algorithms, so it will end to build the model. This will lead to lower accuracy,, and those algorithms lead to high bias in logistic or linear regression.

Q7. What is dimensionality reduction in data science?

Answer:

Dimensionality reduction in data science is the process to convert the dataset with high dimensions to low dimensions. We are doing the dimensionality reduction by dropping some columns or fields from the specified dataset. It will not be done eventually. In this process, fields or dimensions are dropped after confirming the remaining information will describe similar information.

Q8. Why we are using python in data science for data cleaning?

Answer:

Python contains multiple libraries that are used to clean the data. We use Keras, NumPy, pandas, and many more libraries to clean the data. While working on data, data scientists need to clean and transform the huge datasets into the form they are working in. It is very important to deal with redundant data with better results while removing the missing values.

Q9. Which popular libraries are we using in data science?

Answer:

We are using TensorFlow, Scipy, Pandas, Matplotlib, and Pytorch libraries in data science for extracting, cleaning, and deploying the models. TensorFlow library supports parallel computing with library management. SciPy is used to solve the different solutions. Pandas library is used to implement the ETL capabilities from the application of business. Matplotlib library is free and open-source. We can use this library as a replacement for MATLAB, which results in better performance.

Q10. What is variance error in data science?

Answer:

Variance is a type of error that occurs in the model of data science. This error occurs when algorithms are used to train the high-complexity data. This makes the model very sensitive when performing the training dataset, but on the testing dataset, we are performing the model which we have not seen. Variance will leads the poor accuracy in results and testing.

Q11. What is deep learning in data science?

Answer:

Deep learning in data science is a kind of ML in which neural networks are used to imitate the structure of the human brain. Basically, deep learning is an advanced version of neural networks which make the machines learn from data. The deep learning neural networks will comprise multiple layers that was connected to each other.

Q12. What is precision in data science?

Answer:

At the time of implementing algorithms for data classification, that time precision will help us to get the positively predicted values. Precision helps us to get the positive class values that were positively predicted. Precision measures the accuracy of positive predictions.

Q13. When we are using classification techniques in data science?

Answer:

Classification techniques are mainly used when our output contains categorical variables. In classification techniques, we have two labels that were true and false. For calculating the accuracy, we need to divide the sum of observations by the total number of observations.

Q14. When we are using regression techniques in data science?

Answer:

Regression techniques are mainly used when our output contains continuous variables. Regression techniques consist to find the mathematical relationship between two variables’ measurements, x and y, in that value of the y variable is predicted from the measurement of the x variable.

Q15. What is the importance of data cleaning in data science?

Answer:

Data cleaning in data science is the process to remove and update incorrect information. It is used to improve the quality. At the time of running the algorithm we need to gather the proper insights, it is necessary to contain clean and correct data but it will contain the relevant information.

Conclusion

In this article, we have explained the questions and answers of data science. These questions and answers are very useful at the time of giving the interview and attending any test related to data science.