As per the analysis brought by IBM, the year 2020 was projected to witness an increase in the demand for data science professionals by 28%. It shouldn't be shocking that data scientists are rising to fame in the age of big data and machine learning. Companies that are adept at utilizing vast volumes of data to enhance customer service, product development, and business operations will be well-positioned to prosper in this economy.

And if you're pursuing a career as a data scientist, you need to be ready to dazzle potential employers with your expertise. You must be able to ace your upcoming interview with the help of the data science interview questions that we have curated. The most frequent data science interview questions for freshers have been compiled by taking into consideration the expertise level of the prospective candidates at the initial stage. 

Best data science interview questions for Freshers

The following are data science interview questions at the most basic level. 

1. Define Data Science?

Data science can also be referred to as a discipline related to computer science that concerns transforming data into information and drawing valuable conclusions and decisions through the results and analysis drawn from data. It is a discipline that is well-liked and preferred given its advantages and the insights that enable businesses and any user. It helps to gain some significant improvements in numerous goods and businesses. Through the data analysis, customers' preferences can be ascertained and future predictions of the productivity and possible success of any product in the market.

2. What is your knowledge about linear regression?

Understanding the linear relationship between the dependent and independent variables is made easier by linear regression. A supervised learning approach that aids in determining the linear relationship between two variables is linear regression. The answer, or dependent variable, comes second to the predictor, or independent variable. Understanding how the dependent variable varies in relation to the independent variable is the goal of linear regression. Simple linear regression is used when there is just one independent variable, and multiple linear regression is used when there are numerous independent variables.

3. Explain the confusion matrix.

A table called the confusion matrix is used to gauge a model's performance. In a 2 x 2 matrix, it tabulates the actual values and the anticipated values.

All records when both the actual and anticipated values are true are referred to as True Positive (d). These thus represent all of the actual positives. False Negative (c): This category is used to identify records where the actual values and predicted values are both true. False Positive (b): In this case, the predicted values are correct even though the actual values are false. True Negative (a): In this case, both the actual and anticipated values are false. Therefore, accurate values would essentially represent all of the true positives and negatives if you wanted to obtain the correct values. The confusion matrix functions in this manner.

4. Explain the terminology of true-positive rate and false-positive rate

True Positive Rate: The percentage of actual positives that are accurately identified in machine learning is determined by true-positive rates, which are also known as sensitivity or recall. The true Positive rate is calculated as True Positive (s).

False Positive rate: The possibility of inaccurately rejecting the null hypothesis of a given test is known as the false positive rate. The ratio of the number of negative events that were mistakenly classified as positive (false positives) to the total number of actual events is used to compute the false-positive rate. The formula for computing this is as follows: False-Positive Rate = False-Positives/Negatives is the formula.

5. What does data science bias mean?

When an algorithm is put to us, bias in data science can occur owing to the incapability of capturing completely the underlying trends in the data. 

In other words, because the input is too intricate for the algorithm to understand, it makes a model based on naive assumptions. Under fitting results in reduced accuracy as a result of this. The algorithms linear regression, logistic regression, etc., might result in substantial bias.

6. What exactly is dimension reduction?

The method of reducing the number of dimensions (fields) in a dataset involves starting with one that has a lot of dimensions and reducing it. To achieve this, a few fields or columns are removed from the dataset. But this is not carried out carelessly. In this method, the dimensions or fields are only removed after confirming that the remaining data will still be sufficient to briefly describe related data.

7. Why does DS use Python for data cleaning?

The colossal amounts of data collected require cleaning and transforming into a usable format for data scientists. Python modules like Pandas, NumPy Matplotlib, Keras, and SciPy are often used for data cleaning and analysis. These libraries are used to efficiently load, prepare, and analyze the data. The "Student" CSV file, for example, includes information about the students of a specific institute, such as their names, standards, addresses, phone numbers, grades, and other characteristics.

8. Why does data visualization use R?

With more than 12,000 packages available in open-source sources, R offers the best ecosystem for data analysis and visualization. You may simply solve your problems on many platforms like StackOverflow thanks to its strong community assistance.

Distributing the processes among several tasks and nodes, promotes distributed computing, improves data management and reduces the complexity and processing time of huge datasets.