Data Science Interview Q&As: How To Nail Your First Interview

Getting ready for an interview isn't always a simple process. No matter how well you prepare for an interview, your interviewer can always throw you a question you didn't expect.

During a data science interview, the interviewer will pose questions covering a wide scope of subjects. These questions will require you to speak to your skills, experienced and the strategies you use at work each day. You may also need to discuss your soft skills and the ways you work with others.

In a joint effort with data scientists, industry specialists and our data science experts, we've assembled a list of general data science interview questions and answers to assist you with your planning while pursuing a data science job.

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Technical Data Science Interview QAS

1. What is data analysis?

Data analysis is characterized as a procedure of cleaning, modifying and modeling data to find valuable data, information that can inform future business operations. Data professionals use data analysis to identify useful trends, patterns and conclusions in sometimes complicated data sets.

2. What are the different kinds of data analysis?

The different kinds of data analysis are dependent on business and technology. They include:

• Statistical Analysis
• Text Analysis
• Predictive Analysis
• Diagnostic Analysis
• Prescriptive Analysis

3. What is sampling? Explain the different sampling methods.

Data sampling is a statistical analysis procedure used to choose, control and analyze a representative subset of data points. It helps data analysts recognize patterns and trends in larger data sets to be analyzed.

4. Identify the difference between a Type I and Type II error.

A Type I error occurs when the null theory is valid or true, but is dismissed. A Type II error happens when the null theory is invalid but is not dismissed.

5. R or Python– Which programming language do you prefer for text analysis?

The programming language, or languages, you use should be familiar to you. If we look at this question through the eyes of the recruiter, they’d want you to vote for Python. The reason behind this is the popularity of Python, since it has pre-built libraries like Pandas that offer operations and data structures along with analytics tools.

6. Which technique is used to predict categorical responses?

The most popular technique for classifying data sets during the data mining process is a classification technique.

7. Tell us about Recommender Systems.

Recommender Systems are information filtering systems that can predict the ratings or preferences that a client would provide for a product. These are generally utilized in motion pictures, social tags, news, products, research articles and music.

8. Why is data cleaning important?

Data cleaning is an important step in data analysis. It involves changing data into a format that data scientists or data analysts can work with. As the quantity of data expands, the time it takes to clean the data increases exponentially because of the volume of data produced in these sources.

9. What is the difference between WHERE and HAVING statements in SQL?

Adding a WHERE statement to a query permits you to set a condition which you can use to determine what part of the data you need to fetch from the database.

HAVING is a statement often implemented with GROUP BY statement. It refines the output from records that don't fulfill a specific condition. HAVING statements need to be used with the GROUP BY and ORDER BY statements.

10. Explain selection bias.

Selection bias happens when example data assembled and arranged for modeling has qualities that are not representative of the valid, future populace of cases the model will see. Active selection bias happens when a subset of the data is systematically (i.e., non-arbitrarily) barred from the analysis.

11. Briefly define linear regression.

In this statistical analysis method, the score of a second factor X is used to predict the score of a variable Y. The variable X is called the predictor variable and Y is called the criterion variable.

12. Explain extrapolation and interpolation.

Extrapolation is estimating value by expanding a known set of values or even facts. Assessing a value from two known qualities from a set of values is known as interpolation.

13. Define power analysis.

A power analysis is a method to estimate the minimum sample size needed for an experiment, given the desired significance level, effect size, and statistical power.

14. Define precision and recall. Where do we use it?

Recall portrays the percentage of true positives depicted as positive by the model. Precision depicts the percentage of positive predictions. Precision and recall are used in confusion matrix and f1-value estimation.

15. What are overfitting and underfitting?

In overfitting, the model portrays noise or random error rather than the underlying relationship. This condition occurs when a model is unpredictable and complex, for example, having a greater number of parameters comparative with the number of observations. An over-fitted model is unfit because it has a poor performance when it comes to prediction as it goes overboard to minor changes in the training data.

Underfitting happens when a statistical model or an AI algorithm can't catch the fundamental format of the data. Underfitting would occur, for instance, when fitting non-linear data to a linear model. Like the overfitting model, the underfitting model also has poor performance when it comes to prediction.

16. Identify the steps that create a decision tree.

A decision tree has a flowchart-like structure. It is incredibly simple to draw, flexible and can be applied to various issues. The four steps that are common when constructing a decision tree are:

1. Start constructing the tree with the starting state, possibly an idea or question.

2. Next, add the branches. When you have an idea or a question, it fans out into one or various branches.

3. Then, add the leaves. The leaf is the state reached when you have followed a branch. The decision tree can end here if you have limited possibilities.

4. Repeat Steps 2 and 3. The starting points in this step are the leafs, the branches will stem out of the leafs. The decision tree will conclude when all the possibilities are included in the tree.

17. What are the two key components of the Hadoop framework?

The Hadoop Distributed File System (HDFS) and MapReduce are the two main components of the Hadoop framework.

18. Briefly explain how MapReduce works.

MapReduce empowers big datasets to use cloud sources and other equipment. It accommodates clear sociability and fault forbearance at the product level. Hadoop MapReduce first performs planning, which includes chunking big data into pieces to make another set of data.

19. What is Collaborative filtering?

Collaborative filtering uses a large portion of the recommender systems to discover trends or patterns or data by collaborating viewpoints, various data sources and multiple agents.

20. What are Cluster and Systematic Sampling?

Cluster sampling is a method that comes into play when it gets hard to consider the target population expanded over a wide territory and random sampling can't be applied while cluster sampling is a probability test where each sampling unit is an assortment or cluster of components. Systematic sampling is a procedure of statistical nature where components are chosen from an ordered sampling frame. In this kind of sampling, the list is advanced in a clockwise way so when you arrive at the endpoint of the list, the method is started from the start once more. The equal probability method is an example of model systematic sampling.

21. State the difference between supervised and unsupervised learning?

Supervised learning is a learning wherein we supervise or train the machine with the help of data which is very much labeled that implies a few data is as of now labeled with the right answer. From that point forward, the machine is furnished with another set of examples (data) and delivers the right result from labeled data.

Unsupervised learning is learning where professionals use data that is neither labeled nor named and permitting the algo to follow up on that data without any direction. The task of a machine in this sort of learning is to aggregate unsorted data as per likenesses, trends, and contrasts with no earlier training of data.

22. What are the different steps involved in an analytics project?

• Come to terms with the problem, keeping in mind the context.
• Explore data.
• Transform the data by identifying outliers, modifying variables, treating missing values, and so forth for modeling.
• After data planning, run the model, examine the outcome, and change the methodology. This is an iterative step until the most ideal or desired result is accomplished.
• Validate the model utilizing another data set.
• Start executing the model and monitor the outcome to track the performance of the model.

General Data Science Interview FAQs

In this section, we will discuss some general interview questions that have their role play in your recruitment. The answers will be based on your passion for data science. We have answered some of the questions for you.

23. Why do you want to become a data scientist?

A data scientist’s main responsibility is to take data and use it to assist organizations with settling on better business choices. I have a thing for numbers hence I’m good at maths and stats. I picked this job since it includes the skills I'm good at like collecting data and carrying out market research, and I discover data and statistical analysis fascinating.

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