Data science, being among the hottest career trends of this century, has been attracting eyes for all the right reasons. As leading review websites like Glassdoor and Harvard Business Review have mentioned it among the top jobs of the 21st century, the demand for data science courses has witnessed a surge in recent times.
In a world that revolves around data, it won’t be wrong to state that Data Science and Artificial Intelligence are running the world. Companies are looking forward to using innovative ways of using data to improve their customer service, productivity, and efficiency of their operations. These practices have made professionals look for data-centric courses and learn new skills to fit the desired roles.
To become a data scientist, you must keep yourself prepared with in-depth knowledge in the area. In case you’re looking for a course to start your career, our Master Data Science Course can help. Furthermore, you should have an idea of some frequently asked questions so that you can represent yourself as a technically proficient candidate in front of your interviewer.
Here’s a list of 30 questions related to data science you can expect in your next interview:
Q1. What is Data Science?
Ans. Data Science is a combination of algorithms, tools, and machine learning technique which helps you to find common hidden patterns from the given raw data.
Q2. What are the differences between supervised and unsupervised learning?
|Supervised Learning||Unsupervised Learning|
|Uses known and labeled data as input||Uses unlabeled data as input|
|Supervised learning has a feedback mechanism||Unsupervised learning has no feedback mechanism|
|The most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine||The most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm|
Q3. What is the difference between data science and big data?
Ans. Data science is a field applicable to any data size. Big data refers to the large amount of data that cannot be analyzed by traditional methods.
Q4. How do you check for data quality?
Ans. Some of the definitions used to check for data quality are:
Q5. What is Hadoop, and why should I care?
Ans. Hadoop is an open-source processing framework that manages data processing and storage for big data applications running on pooled systems.
Apache Hadoop is a collection of open-source utility software that makes it easy to use a network of multiple computers to solve problems involving large amounts of data and computation. It provides a software framework for distributed storage and big data processing using the MapReduce programming model.
Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packets of code to nodes to process the data in parallel. This allows the data set to be processed faster and more efficiently than if conventional supercomputing architecture were used.
Q6. What is ‘fsck’?
Ans. ‘fsck ‘ abbreviation for ‘ file system check.’ It is a type of command that searches for possible errors in the file. fsck generates a summary report, which lists the file system’s overall health and sends it to the Hadoop distributed file system.
Q7. Which is better – good data or good models?
Ans. This might be one of the frequently asked data science interview questions.
The answer to this question is very subjective and depends on the specific case. Big companies prefer good data; it is the foundation of any successful business. On the other hand, good models couldn’t be created without good data.
Based on your personal preference, you will probably choose no right or wrong answer (unless the company requires one specifically).
Q8. What are Recommender Systems?
Ans. A recommender system is a subclass of an information filtering system. It is used to predict how users would score particular objects (movies, music, merchandise, etc.). Recommender systems filter large volumes of information based on the data provided by a user and other factors, and they take care of the user’s preference and interest.
Q9. What is logistic regression in Data Science?
Ans. Logistic Regression is also called as the logit model. It is a method to forecast the binary outcome from a linear combination of predictor variables.
Q10. Name three types of biases that can occur during sampling
Ans. In the sampling process, there are three types of biases, which are:
- Selection bias
- Under coverage bias
- Survivorship bias
Q11. Discuss Decision Tree algorithm
Ans. A decision tree is a popular supervised machine learning algorithm. It is mainly used for Regression and Classification. It allows breaks down a dataset into smaller subsets. The decision tree can able to handle both categorical and numerical data.
Q12. What is Selection Bias?
Ans. Selection bias is a kind of error that occurs when the researcher decides who is going to be studied. It is usually associated with research where the selection of participants isn’t random. It is sometimes referred to as the selection effect. It is the distortion of statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may not be accurate.
The types of selection bias include:
Sampling Bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
Time Interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants) discounting trial subjects/tests that did not run to completion
Q13. What are recommender systems?
Ans. A recommender system predicts what a user would rate a specific product based on their preferences. It can be split into two different areas:
As an example, Last.fm recommends tracks that other users with similar interests play often. This is also commonly seen on Amazon after making a purchase; customers may notice the following message accompanied by product recommendations: “Users who bought this also bought…”
As an example: Pandora uses the properties of a song to recommend music with similar properties. Here, we look at content, instead of looking at who else is listening to music.
Q14. What is the goal of A/B Testing?
Ans. It is a hypothesis testing for a randomized experiment with two variables A and B.
The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads
An example of this could be identifying the click-through rate for a banner ad.
Q15. How can you select k for k-means?
Ans. We use the elbow method to select k for k-means clustering. The idea of the elbow method is to run k-means clustering on the data set where ‘k’ is the number of clusters.
Within the sum of squares (WSS), it is defined as the sum of the squared distance between each member of the cluster and its centroid.
Q16. Please explain the role of data cleaning in data analysis.
Ans. Data cleaning can be a daunting task due to the fact that with the increase in the number of data sources, the time required for cleaning the data increases at an exponential rate.
This is due to the vast volume of data generated by additional sources. Also, data cleaning can solely take up to 80% of the total time required for carrying out a data analysis task.
Nevertheless, there are several reasons for using data cleaning in data analysis. Two of the most important ones are:
- Cleaning data from different sources helps in transforming the data into a format that is easy to work with
- Data cleaning increases the accuracy of a machine learning model
Q17. What is Ensemble Learning?
Ans. The ensemble is a method of combining a diverse set of learners together to improvise on the stability and predictive power of the model. Two types of Ensemble learning methods are:
Bagging method helps you to implement similar learners on small sample populations. It helps you to make nearer predictions.
Boosting is an iterative method which allows you to adjust the weight of an observation depends upon the last classification. Boosting decreases the bias error and helps you to build strong predictive models.
Q18. Explain Eigenvalue and Eigenvector
Ans. Eigenvectors are for understanding linear transformations. Data scientist need to calculate the eigenvectors for a covariance matrix or correlation. Eigenvalues are the directions along using specific linear transformation acts by compressing, flipping, or stretching.
Q19. Define the term cross-validation
Ans. Cross-validation is a validation technique for evaluating how the outcomes of statistical analysis will generalize for an Independent dataset. This method is used in backgrounds where the objective is forecast, and one needs to estimate how accurately a model will accomplish.
Q20. Explain the steps for a Data analytics project
Ans. The following are important steps involved in an analytics project:
- Understand the Business problem
- Explore the data and study it carefully.
- Prepare the data for modeling by finding missing values and transforming variables.
- Start running the model and analyze the Big data result.
- Validate the model with new data set.
- Implement the model and track the result to analyze the performance of the model for a specific period.
Q21. What are Interpolation and Extrapolation?
Ans. Interpolation – This is the method to guess data points between data sets. It is a prediction between the given data points.
Extrapolation – This is the method to guess data point beyond data sets. It is a prediction beyond given data points.
Q22. How much data is enough to get a valid outcome?
Ans. All the businesses are different and measured in different ways. Thus, you never have enough data and there will be no right answer. The amount of data required depends on the methods you use to have an excellent chance of obtaining vital results.
Q23. What is the difference between ‘expected value’ and ‘average value’?
Ans. When it comes to functionality, there is no difference between the two. However, they are used in different situations.
An expected value usually reflects random variables, while the average value reflects the population sample.
Q24. What happens if two users access the same HDFS file at the same time?
Ans. This is a bit of a tricky question. The answer itself is not complicated, but it is easy to confuse by the similarity of programs’ reactions.
When the first user is accessing the file, the second user’s inputs will be rejected because HDFS NameNode supports exclusive write.
Q25. What is power analysis?
Ans. Power analysis allows the determination of the sample size required to detect an effect of a given size with a given degree of confidence.
Q26. What are the feature vectors?
Ans. A feature vector is an n-dimensional vector of numerical features that represent an object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics (called features) of an object in a mathematical way that’s easy to analyze.
Q27. What are the steps in making a decision tree?
- 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 into two sets.
- Apply the split to the input data (divide step).
- Re-apply steps one and two to the divided data.
- Stop when you meet any stopping criteria.
- This step is called pruning. Clean up the tree if you went too far doing splits.
Q28. Explain the difference between Data Science and Data Analytics
Ans. Data Scientists need to slice data to extract valuable insights that a data analyst can apply to real-world business scenarios. The main difference between the two is that the data scientists have more technical knowledge then business analyst. Moreover, they don’t need an understanding of the business required for data visualization.
Q29. Explain p-value?
Ans. When you conduct a hypothesis test in statistics, a p-value allows you to determine the strength of your results. It is a numerical number between 0 and 1. Based on the value it will help you to denote the strength of the specific result.
Q30. Define the term deep learning
Ans. Deep Learning is a subtype of machine learning. It is concerned with algorithms inspired by the structure called artificial neural networks (ANN).
In A Nutshell
Data science is a vast field that will only grow in the coming years with increased areas of application and enormous career opportunities. While exploring every nook and corner won’t be possible, these interview questions will surely help you have an edge over others.
No dream is unachievable if you work in the right direction. Along with these questions, you can also consider joining our (course name with link) to gain more knowledge of the concepts and applications of data science. Happy hunting!