As you read this guide, we assume you're already interested in data science and looking forward to cracking an interview in this field. A career in data science is highly in-demand and will continue to be in demand in the coming years. Since we'll be exploring 20 data science interview questions, for clarity, we've divided the questions into different sections, like data science interview questions for freshers, data science statistics interview questions, data science technical interview questions, and more.
First, we'll see basic questions or the questions that freshers will most likely be asked.
Data Science Interview Questions For Freshers
As the candidate is fresher, they are expected to have little practical exposure. It is necessary that you, as a candidate, are aware of the theoretical concepts. Mostly, the recruiter will ask about basic concepts to the freshers.
Q1. What is data science?
Until data is transformed into useful information, it has no purpose. Data scientists mine large databases of organized and unstructured data to find hidden patterns and derive practical knowledge. Data science is significant because of the countless applications it may be used for, from simple tasks such as requesting Siri or Alexa for suggestions to more involved ones like running a self-driving car. Computer science, artificial intelligence, statistics, inference, machine learning, algorithms, predictive analysis, and cutting-edge technologies are all included in the interdisciplinary field of data science.
Q2. What is the difference between data analytics and data science?
Since both these terms are related, the recruiter wants to ensure that the fresher candidate understands the difference. Below mentioned are points of difference that you can answer in the interview:
Data science is a broad term that consists of multiple concepts like data mining, machine learning, etc. On the other hand, data analytics is a subset of data science. The main objective of data science is to predict future events by evaluating past events and data. At the same time, data analytics aims to find immediate solutions for the current problem. Data science fuels innovation by providing insights and solutions to issues from the future. While data science involves predictive modeling, data analytics focuses on extracting current meaning from existing historical contexts.
Q3. What is the difference between data science and machine learning?
Data Science is a collection of machine learning methods, tools, and techniques that enables you to extract typically hidden patterns from provided raw data. In contrast, the computer science field, machine learning, deals with programming systems that automatically acquire knowledge and improve over time.
Q4. Do you know anything about data visualization?
Data visualization is the visual representation of the data and information. The textual data can be converted into graphs, charts, diagrams, and maps, which helps in the easy identification of trends and patterns. This form of data can be easily presented to non-technical individuals in the organization.
Q5. What is A/B testing?
A/B testing describes the tests where multiple versions of the same webpage are put side by side and shown to live users to see which performs better for a particular objective. You can even use A/B testing to evaluate your newsletters, popups, registration forms, apps, and more in addition to web pages.
Q6. What is data cleansing? Is it important to perform this step?
As you can guess from the name, data cleaning is the process of filtering data from incorrect values, missing values, duplicate values, etc. And, yes, it is essential to perform this step. The data experts collect are full of errors and can't be sent over for analysis purposes. Hence, it is essential to perform data cleansing to increase the accuracy of results.
Data Science Statistics Interview Questions
Statistics is an integral part of the data science field. Therefore, irrespective of your experience, the chances of recruiters asking questions related to statistics are high.
Q7. What is sampling? And what are the sampling methods?
When conducting research for a large group of data, a small section is selected to work upon, which is named sampling, so choosing a particular small set of data from a large data group is known as sampling. Now, there are two prevalent methods of sampling:
- Probability sampling: In this method, each piece of data has an equal opportunity to be selected as a sample. This implies that the model is chosen randomly.
- Non-probability sampling: In this method, the samples are selected by personal preference, convenience, and biases. This sampling method is less-reliable compared to the probability one.
Q8. Explain the meaning of Type 1 and Type 2 errors.
We make a Type I error when we dismiss the null hypothesis even when it is true. On the contrary, if we accept the null hypothesis even when it is wrong, we make a Type II error.
Type I and Type II error probabilities are represented by the α, β.respectively.
Q9. What is linear regression?
When the value of one variable is predicted based on the value of another variable, it is known as linear regression. Here, the variable whose value is being searched is the dependent variable, and the other one is the independent variable.
Q10. What is logistic regression?
Logistic regression gives the probability of happening of an event based on a bunch of independent variables. The possibility lies between 0 and 1.
Q11. What is an outlier?
An individual data point that significantly deviates from the average value of a set of statistics is called an outlier. Outliers can also be exceptions that are outside of specific demographic samples. Outliers can occasionally point to mistakes or inadequate sample collection techniques.
Q12. How to handle missing data?
Missing data can be handled in statistics in a variety of ways:
- Forecasting the values that are lacking
- Allocation of distinctive (individual) values
- Removal of the rows containing the blank data
- Imputation by the mean or median
- Using the missing value-supporting random forests
Q13. What is the p-value?
The probability value is often referred to as the P-value. It is described as the likelihood of receiving a result that is either more intense than the actual findings or the same as those observations. The P-value, or degree of marginal significance, measures how likely an event is to occur that is used in hypothesis testing.
Data Science Technical Interview Questions
Recruiters might ask you highly technical questions if you're experienced in this field. Most questions will be about machine learning, artificial intelligence, or any programming language like Python.
Q.14 What is machine learning?
Machine learning, as you might know, is a subset of AI that uses data to imitate a human brain. The machine learning model uses past data to make independent decisions and to decrease human dependence.
Q15. What is the difference between machine learning and artificial intelligence?
Both these terms are correlated to each other but are very different in concepts. The point of difference is:
- AI focuses on simulating the working of the human brain. On the other hand, ML is a subset of AI that allows algorithms to learn from past data.
- AI is a broader concept that consists of ML and deep learning. Machine learning is a smaller concept that consists of only deep learning.
- Examples of AI applications can be Siri and Alexa. On the other hand, examples of ML can be recommendation systems, fraud detection, etc.
Q16. What is Python? What are its applications?
A high-level, all-purpose programming language is Python. Python is a language for coding that can be used to make websites, internet applications, and desktop GUI programs. Python lets you focus on the application's core functionality while handling tedious programming tasks. Python has a wide range of applications like the development of software, web development, game program development, and many more.
Q17. What are supervised and unsupervised machine learning?
In the supervised learning method, the training data includes the label. These labels guide the model to produce an accurate solution. On the other hand, unsupervised machine learning learns from the unlabeled data. This method helps in discovering hidden trends and patterns.
Q.18 What are overfitting and underfitting?
When the machine learning algorithm tries to include all the data points—or more—present in the dataset, this is known as overfitting. As a result, the model tends to consider inaccurate information and noise from the dataset, which lowers its effectiveness and reliability. Underfitting occurs when the machine learning algorithm cannot recognize the data's underlying pattern. Feeding the training information can be stopped early to prevent overfitting in the model, but this may prevent the model from learning enough from the data used for training. As a result, it might be unable to identify the dominating trend in the data's best fit.
Q19. What, according to you, are the key features of Python?
Python is a well-liked programming language renowned for being straightforward and readable. Here are a few of Python's main attributes:
- Python is user-friendly for beginners and allows developers to create short and easy code to maintain.
- The extensive standard library with Python offers prepared-for-use modules and functions for various activities.
- Python is an open-source, free-of-cost programming language that anyone can use and access.
Q20. Why does a data scientist use Python for data cleaning?
Engineers and data scientists must transform vast data into useful ones. Malicious entries, outliners, incorrect values, unnecessary formatting, etc., are all removed during data cleaning. The most popular Python data cleaners include Matplotlib, Pandas, and others. These were the top 20 data science interview questions and answers. Let's continue to conclude this blog guide.
Although the task is challenging for data scientists, it is lucrative, with many job openings. You can go one step closer to your ideal job using these data science interview questions. So, get ready for the challenges of interviews and keep up with the details of data science.