**Data Science Interview Questions and Answers, Data Science Interview Questions and Answers Freshers, Data Science Interview Questions and Answers, Data Science Interview Questions**

Before getting on to the **Data Science interview questions**, the student must know that the Data Science is a continuously varying field which needs the students as well as professionals to upgrade their skills with the new features and knowledge, to get fit for the jobs associated with Data Science . This post related to **Data Science Interview Questions and Answers, Data Science Interview Questions and Answers Freshers, Data Science Interview Questions and Answers, Data Science Interview Questions** will help you let out find all the solutions that are frequently asked in you upcoming Data Science interview.

Over thousands of vacancies available for the Data Science developers, experts must be acquaintance with all the component of Data Science technologies. This is necessary for the students in order to have in-depth knowledge of the subject so that they can have best employment opportunities in the future. Knowing every little detail about Data Science is the best approach to solve the problems linked with problem.

APTRON has spent hours and hours in researching about the** Data Science Interview Questions and Answers, Data Science Interview Questions and Answers Freshers, Data Science Interview Questions and Answers, Data Science Interview Questions** that you might encounter in your upcoming interview. All these questions will alone help you to crack the interview and make you the best among all your competitors.

First of all, let us tell you about how the Data Science technology is evolving in today’s world and how demanding it is in the upcoming years. In fact, according to one study, most of the companies and businesses have moved to the Data Science . Now, you cannot predict how huge the future is going to be for the people experienced in the related technologies.

Hence, if you are looking for boosting up your profile and securing your future, Data Science will help you in reaching the zenith of your career. Apart from this, you would also have a lot of opportunities as a fresher.

These questions alone are omnipotent. Read and re-read the questions and their solutions to get accustomed to what you will be asked in the interview. These Data Science interview questions and answers will also help you on your way to mastering the skills and will take you to the giant world where worldwide and local businesses, huge or medium, are picking up the best and quality Data Science professionals.

This ultimate list of best Data Science interview questions will ride you through the quick knowledge of the subject and topics like Manipulation using R, Machine Learning. This Data Science interview questions and answers can be your next gateway to your next job as a Data Science expert.

**These are very Basic Data Science Interview Questions and Answers for freshers and experienced both.**

Q1: Differentiate between Data Science , Machine Learning and AI.

**A1:**

Criteria |
Data Science |
Machine Learning |
Artificial Intelligence |

Defintion | Data Science is not exactly a subset of machine learning but it uses machine learning to analyse and make future predictions. | A subset of AI that focuses on narrow range of activities. | A wide term that focuses on applications ranging from Robotics to Text Analysis. |

Role | It can take on a busines role. | It is a purely technical role. | It is a combination of both business and technical aspects. |

Scope | Data Science is a broad term for diverse disciplines and is not merely about developing and training models. | Machine learning fits within the data science spectrum. | AI is a sub-field of computer science. |

AI | Loosely integrated | Machine learning is a sub field of AI and is tightly integrated. | A sub- field of computer science consisting of various task like planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work.. |

Q2: Python or R – Which one would you prefer for text analytics?

**A2:** The best possible answer for this would be Python because it has Pandas library that provides easy to use data structures and high performance data analysis tools.

Q3: What is logistic regression? Or State an example when you have used logistic regression recently.

**A3:** Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

Q4: What are Recommender Systems?

**A4:** A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

Q5: Why data cleaning plays a vital role in analysis?

**A5:** Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

Q6: Differentiate between univariate, bivariate and multivariate analysis.

**A6:** These are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can be referred to as univariate analysis.

If the analysis attempts to understand the difference between 2 variables at time as in a scatterplot, then it is referred to as bivariate analysis. For example, analysing the volume of sale and a spending can be considered as an example of bivariate analysis.

Analysis that deals with the study of more than two variables to understand the effect of variables on the responses is referred to as multivariate analysis.

Q7: What is Linear Regression?

**A7:** Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.

Q8: What is Interpolation and Extrapolation?

**A8:** Estimating a value from 2 known values from a list of values is Interpolation. Extrapolation is approximating a value by extending a known set of values or facts.

Q9: What is power analysis?

A9: An experimental design technique for determining the effect of a given sample size.

Q10: Compare SAS, R and Python programming?

**A10:** SAS: it is one of the most widely used analytics tools used by some of the biggest companies on earth. It has some of the best statistical functions, graphical user interface, but can come with a price tag and hence it cannot be readily adopted by smaller enterprises

R: The best part about R is that it is an Open Source tool and hence used generously by academia and the research community. It is a robust tool for statistical computation, graphical representation and reporting. Due to its open source nature it is always being updated with the latest features and then readily available to everybody.

Python: Python is a powerful open source programming language that is easy to learn, works well with most other tools and technologies. The best part about Python is that it has innumerable libraries and community created modules making it very robust. It has functions for statistical operation, model building and more.

Q11: Explain the various benefits of R language?

**A11:** The R programming language includes a set of software suite that is used for graphical representation, statistical computing, data manipulation and calculation.

Some of the highlights of R programming environment include the following:

• An extensive collection of tools for data analysis

• Operators for performing calculations on matrix and array

• Data analysis technique for graphical representation

• A highly developed yet simple and effective programming language

• It extensively supports machine learning applications

• It acts as a connecting link between various software, tools and datasets

• Create high quality reproducible analysis that is flexible and powerful

• Provides a robust package ecosystem for diverse needs

• It is useful when you have to solve a data-oriented problem

Q12: What are the two main components of the Hadoop Framework?

**A11:** HDFS and YARN are basically the two major components of Hadoop framework.

- HDFS- Stands for Hadoop Distributed File System. It is the distributed database working on top of Hadoop. It is capable of storing and retrieving bulk of datasets in no time.
- YARN- Stands for Yet Another Resource Negotiator. It allocates resources dynamically and handles the workloads.

Q13: What is logistic regression?

**A13: **It is a statistical technique or a model in order to analyze a dataset and predict the binary outcome. The outcome has to be a binary outcome that is either zero or one or a yes or no. Random Forest is an important technique which is used to do classification, regression and other tasks on data.

Q14: Why data cleansing is important in data analysis?

**A14: **With data coming in from multiple sources it is important to ensure that data is good enough for analysis. This is where data cleansing becomes extremely vital. Data cleansing extensively deals with the process of detecting and correcting of data records, ensuring that data is complete and accurate and the components of data that are irrelevant are deleted or modified as per the needs. This process can be deployed in concurrence with data wrangling or batch processing.

Once the data is cleaned it confirms with the rules of the data sets in the system. Data cleansing is an essential part of the data science because the data can be prone to error due to human negligence, corruption during transmission or storage among other things. Data cleansing takes a huge chunk of time and effort of a Data Scientist because of the multiple sources from which data emanates and the speed at which it comes.

Q15: What is Interpolation and Extrapolation?

**A15:** The terms of interpolation and extrapolation are extremely important in any statistical analysis. Extrapolation is the determination or estimation using a known set of values or facts by extending it and taking it to an area or region that is unknown. It is the technique of inferring something using data that is available.

Interpolation on the other hand is the method of determining a certain value which falls between a certain set of values or the sequence of values. This is especially useful when you have data at the two extremities of a certain region but you don’t have enough data points at the specific point. This is when you deploy interpolation to determine the value that you need.

Q16: What are feature vectors?

**A16:** A feature vector is an n-dimensional vector of numerical features that represent some object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics, called features, of an object in a mathematical, easily analyzable way.

Q17: Explain the steps in making a decision tree.

**A17:**

- Take the entire data set as input.
- Look for a split that maximizes the separation of the classes. A split is any test that divides the data in two sets.
- Apply the split to the input data (divide step).
- Re-apply steps 1 to 2 to the divided data.
- Stop when you meet some stopping criteria.
- This step is called pruning. Clean up the tree if you went too far doing splits.

Q18: What is root cause analysis?

**A18:** Root cause analysis was initially developed to analyze industrial accidents, but is now widely used in other areas. It is a problem solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from reoccurring.

Q19: What is logistic regression?

**A19:** Logistic Regression is also referred as the logit model. It is a technique to forecast the binary outcome from a linear combination of predictor variables.

Q20: What are Recommender Systems?

**A20:** Recommender systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.

Q21: Explain cross-validation.

**A21:** It is a model validation technique for evaluating how the outcomes of a statistical analysis will generalize to an independent data set. It is mainly used in backgrounds where the objective is forecast and one wants to estimate how accurately a model will accomplish in practice. The goal of cross-validation is to term a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and gain insight on how the model will generalize to an independent data set.

Q22: What is Collaborative Filtering?

**A22:** The process of filtering used by most recommender systems to find patterns and information by collaborating perspectives, numerous data sources and several agents.

Q23: Do gradient descent methods at all times converge to a similar point?

**A23:** No, they do not because in some cases they reach a local minima or a local optima point. You would not reach the global optima point. This is governed by the data and the starting conditions.

Q24: What is the goal of A/B Testing?

**A24:** This is a statistical hypothesis testing for randomized experiment with two variables A and B. The objective of A/B testing is to detect any changes to a web page to maximize or increase the outcome of a strategy.

Q25: What is Unsupervised learning?

**A25:** Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.

Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models

E.g. In the same example, a fruit clustering will categorize as “fruits with soft skin and lots of dimples”, “fruits with shiny hard skin” and “elongated yellow fruits”.

Q26: What is logistic regression? State an example when you have used logistic regression recently.

**A26:** Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables.

For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

Q27: What are Recommender Systems?

**A27:** Recommender Systems are a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

Examples include movie recommenders in IMDB, Netflix & BookMyShow, product recommenders in e-commerce sites like Amazon, eBay & Flipkart, YouTube video recommendations and game recommendations in Xbox.

Q28: How can outlier values be treated?

**A28:** Outlier values can be identified by using univariate or any other graphical analysis method. If the number of outlier values is few then they can be assessed individually but for large number of outliers the values can be substituted with either the 99th or the 1st percentile values.

All extreme values are not outlier values. The most common ways to treat outlier values

1. To change the value and bring in within a range

2. To just remove the value.

Q29: What are various steps involved in an analytics project?

**A29:** The following are the various steps involved in an analytics project:

- Understand the business problem
- Explore the data and become familiar with it.
- Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.
- After data preparation, start running the model, analyse the result and tweak the approach. This is an iterative step till the best possible outcome is achieved.
- Validate the model using a new data set.
- Start implementing the model and track the result to analyse the performance of the model over the period of time.

Q30: During analysis, how do you treat missing values?

**A30:** The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights.

If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored. Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.

If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If you have a distribution of data coming, for normal distribution give the mean value.

If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.

Q31: How do you control for biases?

**A31:**

- Choose a representative sample, preferably by a random method
- Choose an adequate size of sample
- Identify all confounding factors if possible
- Identify sources of bias and include them as additional predictors in statistical analyses
- Use randomization: by randomly recruiting or assigning subjects in a study, all our experimental groups have an equal chance of being influenced by the same bias

Notes:

– Randomization: in randomized control trials, research participants are assigned by chance, rather than by choice to either the experimental group or the control group.

– Random sampling: obtaining data that is representative of the population of interest

Q32: What are confounding variables?

**A32:**

- Extraneous variable in a statistical model that correlates directly or inversely with both the dependent and the independent variable
- A spurious relationship is a perceived relationship between an independent variable and a dependent variable that has been estimated incorrectly
- The estimate fails to account for the confounding factor
- See Question 18 about root cause analysis

Q33: What is A/B testing?

**A33:**

• Two-sample hypothesis testing

• Randomized experiments with two variants: A and B

• A: control; B: variation

• User-experience design: identify changes to web pages that increase clicks on a banner

• Current website: control; NULL hypothesis

• New version: variation; alternative hypothesis

Q34: Examples of NoSQL architecture

**A34:**

- Key-value: in a key-value NoSQL database, all of the data within consists of an indexed key and a value. Cassandra, DynamoDB
- Column-based: designed for storing data tables as sections of columns of data rather than as rows of data. HBase, SAP HANA
- Document Database: map a key to some document that contains structured information. The key is used to retrieve the document. MongoDB, CouchDB
- Graph Database: designed for data whose relations are well-represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Polyglot Neo4J

Q35: Provide examples of machine-to-machine communications

**A35:** Telemedicine

– Heart patients wear specialized monitor which gather information regarding heart state

– The collected data is sent to an electronic implanted device which sends back electric shocks to the patient for correcting incorrect rhythms

Product restocking

– Vending machines are capable of messaging the distributor whenever an item is running out of stock

**Data Science Conclusion Interview FAQs**

We know the list of

However, you will be asked with the questions in the interview related to the above mentioned questions. Preparing and understanding all the concept of Data Science technology will help you strengthen the other little information around the topic.

After preparing these interview questions, we recommend you to go for a mock interview before facing the real one. You can take the help of your friend or a Data Science expert to find the loop holes in your skills and knowledge. Moreover, this will also allow you in practicing and improving the communication skill which plays a vital role in getting placed and grabbing high salaries.

Remember, in the interview, the company or the business or you can say the examiner often checks your basic knowledge of the subject. If your basics is covered and strengthened, you can have the job of your dream. The industry experts understand that if the foundation of the student is already made up, it is easy for the company to educate the employ towards advance skills. If there are no basics, there is no meaning of having learnt the subject.

Therefore, it’s never too late to edge all the basics of any technology. If you think that you’ve not acquired the enough skills, you can join our upcoming batch of Data Science Training in Noida. We are one of the best institute for Data Science in noida which provide advance learning in the field of Data Science Course. We’ve highly qualified professionals working with us and promise top quality education to the students.

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