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# Data Science Interview Questions and Answers

Question - 1 : - What is Data Science?

Data Science is a field of computer science that explicitly deals with turning data into information and extracting meaningful insights out of it. The reason why Data Science is so popular is that the kind of insights it allows us to draw from the available data has led to some major innovations in several products and companies. Using these insights, we are able to determine the taste of a particular customer, the likelihood of a product succeeding in a particular market, etc.

Question - 2 : - Differentiate between Data Analytics and Data Science

 Data Analytics Data Science Data Analytics is a subset of Data Science. Data Science is a broad technology that includes various subsets such as Data Analytics, Data Mining, Data Visualization, etc. The goal of data analytics is to illustrate the precise details of retrieved insights. The goal of data science is to discover meaningful insights from massive datasets and derive the best possible solutions to resolve business issues. Requires just basic programming languages. Requires knowledge in advanced programming languages. It focuses on just finding the solutions. Data Science not only focuses on finding the solutions but also predicts the future with past patterns or insights. A data analyst’s job is to analyse data in order to make decisions. A data scientist’s job is to provide insightful data visualizations from raw data that are easily understandable.

Question - 3 : - What do you understand about linear regression?

Linear regression helps in understanding the linear relationship between the dependent and the independent variables. Linear regression is a supervised learning algorithm, which helps in finding the linear relationship between two variables. One is the predictor or the independent variable and the other is the response or the dependent variable. In Linear Regression, we try to understand how the dependent variable changes w.r.t the independent variable. If there is only one independent variable, then it is called simple linear regression, and if there is more than one independent variable then it is known as multiple linear regression.

Question - 4 : - What do you understand about the true-positive rate and false-positive rate?

True positive rate: In Machine Learning, true-positive rates, which are also referred to as sensitivity or recall, are used to measure the percentage of actual positives which are correctly identified. Formula: True Positive Rate = True Positives/Positives False positive rate: False positive rate is basically the probability of falsely rejecting the null hypothesis for a particular test. The false-positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positive) upon the total number of actual events. Formula: False-Positive Rate = False-Positives/Negatives.

Question - 5 : - How is Data Science different from traditional application programming?

Data Science takes a fundamentally different approach in building systems that provide value than traditional application development.

In traditional programming paradigms, we used to analyze the input, figure out the expected output, and write code, which contains rules and statements needed to transform the provided input into the expected output. As we can imagine, these rules were not easy to write, especially, for data that even computers had a hard time understanding, e.g., images, videos, etc.

Data Science shifts this process a little bit. In it, we need access to large volumes of data that contain the necessary inputs and their mappings to the expected outputs. Then, we use Data Science algorithms, which use mathematical analysis to generate rules to map the given inputs to outputs.

This process of rule generation is called training. After training, we use some data that was set aside before the training phase to test and check the system’s accuracy. The generated rules are a kind of a black box, and we cannot understand how the inputs are being transformed into outputs.

However, If the accuracy is good enough, then we can use the system (also called a model).

As described above, in traditional programming, we had to write the rules to map the input to the output, but in Data Science, the rules are automatically generated or learned from the given data. This helped solve some really difficult challenges that were being faced by several companies.

Question - 6 : - What is bias in Data Science?

Bias is a type of error that occurs in a Data Science model because of using an algorithm that is not strong enough to capture the underlying patterns or trends that exist in the data. In other words, this error occurs when the data is too complicated for the algorithm to understand, so it ends up building a model that makes simple assumptions. This leads to lower accuracy because of underfitting. Algorithms that can lead to high bias are linear regression, logistic regression, etc.

Question - 7 : - What is dimensionality reduction?

Dimensionality reduction is the process of converting a dataset with a high number of dimensions (fields) to a dataset with a lower number of dimensions. This is done by dropping some fields or columns from the dataset. However, this is not done haphazardly. In this process, the dimensions or fields are dropped only after making sure that the remaining information will still be enough to succinctly describe similar information.

Question - 8 : - Why is Python used for Data Cleaning in DS?

Data Scientists have to clean and transform the huge data sets in a form that they can work with. It is important to deal with the redundant data for better results by removing nonsensical outliers, malformed records, missing values, inconsistent formatting, etc.

Python libraries such as  Matplotlib, Pandas, Numpy, Keras, and SciPy are extensively used for Data cleaning and analysis. These libraries are used to load and clean the data and do effective analysis. For example, a CSV file named “Student” has information about the students of an institute like their names, standard, address, phone number, grades, marks, etc.

Question - 9 : - Why is R used in Data Visualization?

R provides the best ecosystem for data analysis and visualization with more than 12,000 packages in Open-source repositories. It has huge community support, which means you can easily find the solution to your problems on various platforms like StackOverflow.

It has better data management and supports distributed computing by splitting the operations between multiple tasks and nodes, which eventually decreases the complexity and execution time of large datasets.

Question - 10 : - Explain selection bias.

Selection bias is the bias that occurs during the sampling of data. This kind of bias occurs when a sample is not representative of the population, which is going to be analyzed in a statistical study.

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