50 Data Science Intern Interview Questions With Answers

Data Science is one of the hottest fields today. It is a study that deals with statistics, machine learning, and analytics to extract insights from data. It is one of the most discussed topics among the IT circles.

The popularity of data science has grown over the years, and companies have started using data science techniques to increase their business.

Companies are collecting more data than ever, and as a result, there is an increasing demand for data scientists who can interpret and analyze that data.

Below are 50 Data Science Interview Questions for an Intern.

In this blog, we will learn about the following:

• 20 basic data science questions for interns
• 20 In-depth data science in-depth questions for interns
• 10 Bonus Questions to stand out as an intern
• How to crack a data science internship interview

20 Basic Data Science Questions for Interns

1 .How is data science different from traditional programming?

In traditional programming one has to create rules to translate input to output whereas in Data Science, the rules are automatically produced from the data.

2. What is boosting in data science?

Boosting is an assembled learning technique used to strengthen a weak learning model.In boosting, a random sample of data is selected, fitted with a model, and then trained sequentially.

3. What are the main steps in a data science project?

The main steps in a data science project are: Ask the right questions > collect data > prepare the data/explore the data > model data > evaluate the results > deploy model and solutions.

4. What is the cross-validation technique, and why it is used?

Cross-validation is a technique for evaluating machine learning models by splitting the dataset into training and validation sets. It is used for making better use of limited data, selection of model, training of parameters, etc.

5. What are the most common cross-validation techniques?

The most common cross-validation techniques are:

• K-fold cross-validation technique
• Repeated cross-validation
• Leave-one-out cross-validation

6. What are some machine learning algorithms?

Some common machine learning algorithms are:

• Decision Trees
• Random Forests
• Naive Bayes
• Neural networks

7. What is the difference between regression and classification models?

The main difference between the two is that regression analysis is used to classify the continuous values whereas classification models are used to classify the discrete values.

8. What is a p-value?

In data science, the p-value is used to evaluate the performance of machine learning models. The concept of p-value is used in statistical hypothesis testing to determine the significant results.

9. What is the difference between supervised, unsupervised, and reinforcement learning?

The difference is that supervised learning uses labeled data for research purposes, unsupervised learning finds patterns in unlabeled data, and reinforcement learning involves training agents

10. How can you communicate your findings in writing?

“As a data scientist I will need to write reports and other documents to convey my findings. I must have the skills that are necessary for the job. After extracting my findings I try to make the information is presented clearly so that it can be easily understood so that by reading it people can get the most out of the data.

11. What is your procedure for prioritizing tasks with large data sets?

I usually create a timeline for completing my task by setting deadlines. Initially, I go through the scope of the different tasks assigned and determine which tasks are more important to complete in order to achieve the outcome. With that I break down tasks into smaller and more manageable ways.

12. What is your experience with machine learning?

I have not much experience with machine learning but I had an online certification course where I got to know about well-known tools like Scikit-Learn, TensorFlow, and Keras, I also obtained useful experience in developing predictive models. I also learned how to assess model performance and optimize hyperparameters.

13. If you want to improve your customer retention rates. How would you analyze customer data to improve retention rates

In today's world information is considered as the costliest thing. At first, I would start by gathering information about the customer's purchasing habits, purchasing history, preferences, etc. After that, I will use descriptive analysis to understand the state of customer retention. This will help me to identify the problems and look for opportunities in solving them.

Next, I would use predictive analytics to forecast customer behavior. This customer behavior can be used to expect the needs and build strategies to increase customer loyalty. Lastly, prescriptive analytics must be used to optimize customer retention.

14. Are you comfortable working with SQL, and NoSQL?

Yes, I am comfortable working with both of these. I have experience and knowledge of both during my college days and I have also completed my academic projects using this(if done any).

15. Do you know how web scraping is done and what is the use of data extraction tools?

Yes, I know about Python libraries like BeautifulSoup, Scrapy, and Selenium for web scraping and automating data extraction from websites. I'm especially interested in this role because I know web scraping and data skills will allow me to provide immediate value by automating essential data collection and analysis tasks.

16. What skills could you gain through an internship with us (the organization)?

I'm eager to learn from your experienced data scientists on topics like stakeholder presentation, translating analytics into actionable recommendations, and project scoping. An internship with your team would also improve my ability to collaborate cross-functionally and develop commercial awareness.

17. If you are given any data projects to handle without any instructions. What would you do?

If I am assigned to any projects without any instructions the very first thing I would do is to ask questions. It is important for me to know my role in the project and if is there any deadline for the project. Once I get all my required information I can start off breaking the project into smaller tasks. This will help me to stay focused and organized.

18. What is a computational graph?

In data science a computational graph visually represents the sequence of data transformation and calculations.
It uses nodes for operations and edges for data flow, aiding in understanding and optimizing processes like machine learning and numerical analysis.

19. What is an artificial neural network(ANN)? What are the components?

An artificial neural network (ANN) in data science is a computer model inspired by the brain. It's made up of layers of connected nodes, like brain cells. Each connection has a weight, and nodes use activation functions to process data.

20. What are some tools used in data science?

Some tools used in data science are:
Jupyter Notebooks - An open-source web app for interactive data exploration and visualization in Python, R, and other languages. It allows the creation of sharable documents.
Spark - An open-source parallel processing framework for big data workloads and ETL. Often used with Python, Scala, or R.
TensorFlow - An end-to-end open-source platform for machine learning from Google
Apache Hadoop - An open-source framework for distributed storage and processing of massive datasets.

20 In-Depth Data Science Questions for Answers

21. What are the advantages and disadvantages of decision trees?

Advantages of decision trees:

• Decision trees are robust to outliers and accommodate missing values.
• They're versatile, requiring no assumptions about data distribution or linearity.
• Decision trees offer interpretability, aiding in understanding model decisions.
• They reveal feature importance, making them useful for insight generation.
• They're versatile, requiring no assumptions about data distribution or linearity.

Disadvantages of decision trees:

• Decision trees can overfit the training data.
• Complex trees with lots of branches may overfit more than simpler trees.
• The algorithms used to construct decision trees are greedy means they optimize locally at each node rather than looking at the bigger picture.

22. What is feature scaling and why is it important?

Feature scaling is a data preprocessing technique used to standardize the range of independent variables or features of data before feeding them into a machine learning algorithm. It's important because helps to prevent certain variables from dominating others due to vast differences in units or magnitude. It also reduces the chances of getting stuck in local optima.

23. Explain the difference between correlation and causation.

The differences between correlation and causation are:

24. Tell me about a time when you had to collaborate with others on a data science project.

I worked on an analytics project analyzing for a subscription company. I worked closely with the product team to understand their key questions and business objectives. Together, we identified the metrics and data sources required, including customer usage data, account details, and survey feedback.

Throughout the project, we met regularly to share progress, discuss any roadblocks, and align on the next steps. I presented our dashboard and model results to the product team to convey our findings.

25. How to handle missing or corrupt data in a dataset?

Some ways to handle missing or corrupt data are:

Deletion: It completely removes rows or columns containing missing values if the amount of missing data is small.
MICE (Multiple Imputation by Chained Equations): It performs multiple imputations using various models to create multiple complete datasets for analysis. Accounts for uncertainty.
Model adaptation: Using models like XGBoost and RNNs that allow for missing values inherently. Requires no imputation but algorithms must support missing data.
Regression imputation: Use regression models to predict missing values from other variables. Maintains relationships but requires more complex modeling.

26. What do you understand by over-fitting and under-fitting?

Overfitting refers to a model that models the training data too well, but fails to generalize to new data. It happens when a model is excessively complex relative to the amount and noisiness of the training data.

Underfitting refers to a machine learning model that is not complex enough to capture the underlying pattern in the training data.

27. What is the importance of data cleansing?

Importance of data cleansing are :
Enhanced Data Integrity: Clean data contributes to maintaining the integrity of the entire data ecosystem. When data is accurate and consistent, it can be more easily integrated with other datasets, leading to a holistic view of the situation.
Cost and Time Efficiency: Working with clean data reduces the need for repeated analysis, adjustments, and rework caused by errors. This saves both time and resources that would otherwise be spent on correcting errors or investigating unexpected results
Enhanced Data Visualization: Data visualization is an essential part of data exploration and communication. Clean data allows for accurate visual representations, making it easier to communicate findings to stakeholders effectively.
Accurate Analysis: Clean and accurate data is fundamental for generating reliable insights and making informed decisions. If the data used for analysis is riddled with errors, the results and conclusions will be misleading and could potentially lead to incorrect actions or strategies.

28. What is the use of Statistics in Data Science?

Statistics plays a fundamental role in data science as they provide methods for extracting insights, analyzing patterns, and making predictions from data. Some key use of data statistics are Descriptive Statistics Measures like mean, median, mode, variance, percentiles, etc.

29. What do you mean by Normal Distribution?

The Normal Distribution, also known as Gaussian distribution, is a core concept in statistics. It describes a continuous probability pattern where most data clusters around the mean in a symmetrical, bell-shaped curve. This distribution is essential in modeling, simulations, and quality control.

30. Explain Star Schema.

Star schema is a database design used in data warehousing where data is organized into fact and dimension tables. It is widely used in data warehousing and business intelligence for analytical workloads. Star schema is considered a dimensional data model and is optimized for read-heavy reporting and analysis.

31. What are support vector machines(SVMs) and how do they work? What are the advantages of SVM?

Support vector machines are supervised learning models used for classification and regression tasks in data science. They analyze data and create optimal decision boundaries called hyperplanes that best separate different classes in the input variable space. SVMs maximize the margin around the hyperplane which enables clear separation between classes.

Some advantages of SVM are:

• SVMs can handle many features and are effective in high-dimensional spaces, making them very versatile.
• SVMs use a subset of training points called support vectors to represent the decision boundary, so they are memory efficient.
• SVMs are relatively robust to overfitting due to regularization and maximizing the margin between classes. This improves generalization.
• SVMs often have good accuracy compared to other algorithms due to maximizing the margin and regularization.
• It is flexible. Different kernel functions like linear, polynomial, and radial basis functions can be used to adapt SVMs to different problems.

32. What are some of the downsides of visualization?

Some potential downsides of data visualization are:
Can oversimplify complex data - Visualizations may gloss over nuances in large, multidimensional datasets. Reducing data to a simple visual can overgeneralize.
Can be misleading - Choices like colors, scales, chart types etc. can intentionally or accidentally highlight/downplay aspects of data, leading to biased interpretations.
Requires expertise - Creating effective, accurate visuals requires knowledge of visual design, statistics, perception, graphic design principles, etc.
A significant effort to create - Thoughtful visualization takes time and iteration to conceptualize, design, test, and refine. This overhead may not always be feasible.
Hard to visualize uncertainty - Visualizations can struggle to accurately convey uncertainty intervals, confidence levels, etc.

33. What do you mean by pruning in the decision tree?

Pruning in data science refers to the process of removing components from a machine learning model in order to reduce complexity and avoid overfitting. Pruning is commonly applied to decision trees. Pruning removes branches and nodes that are insignificant, problematic, or provide little classification power.

34. Difference between Type I Error and Type II Error?

Differences between Type I Error and Type II Error :

• Type I error occurs when the null hypothesis is incorrectly rejected, while Type II error occurs when the null hypothesis fails to be rejected when it should have been.
• Type I errors results in false positives, while Type II errors result in false negatives.
• Type I errors reject a true null hypothesis, whereas Type II errors fail to reject a false null hypothesis.

35. What are the important steps of data cleansing?

The important steps of data cleansing are:

1. Checking data quality and consistency, looking for issues like incorrect data formats, invalid values, outliers, etc. Standardize formats and coding.
2. Identifying and removing any duplicate records. Also removing irrelevant data.
3. Handling missing data and deciding how to deal with it
4. Correcting structural errors by fixing issues in how data is organized, like merging split fields or splitting concatenated ones.
5. Validating and verifying by checking any errors or consistencies. Also by visual checks, statistical analysis, etc to confirm clean data.

36. Explain principal component analysis (PCA). When is it used?

PCA is a statistical and dimensionality reduction technique that lets us simplify complex data while preserving essential information and patterns. PCA is used in the following areas:
Dimensionality Reduction - When we have a large set of variables or features and we want to reduce it to a smaller set that still captures most of the information.
Feature Extraction - PCA can be used to derive new features from your existing set of features. These new features are orthogonal and uncorrelated, which is useful for many algorithms.
Data Compression - PCA can be used to significantly compress high-dimensional data by projecting it onto a much lower dimensional space, which captures most of the variance.
Time Series Analysis - The time-based patterns and trends in data can be understood by applying PCA. It reveals the internal structure and relationships.

37. Explain the difference between a parametric and a nonparametric model. Give an example of each.

Parametric models make strong assumptions about the functional form of the relationship between variables. For example, linear regression assumes a linear relationship. Nonparametric models make very few assumptions and can fit a wider range of functions. An example is K-nearest neighbors, which make local predictions based on similar data points.

38. What is regularization and why is it useful for training machine learning models?

Regularization adds a penalty term to the model objective function to shrink coefficients toward zero. This reduces model complexity and helps prevent overfitting. Common regularization techniques include L1 and L2 normalization of coefficients as well as dropout layers for neural networks.

39. Explain what resampling methods are and their use in model validation.

Resampling methods are techniques used to validate and estimate the performance of statistical models. Some common resampling methods used in model validation are:
Bootstrapping: Here random samples are drawn with replacements from the original dataset to create bootstrap samples. The model is fit on these bootstrap samples and validated on the original dataset.
Jackknifing: Similar to bootstrapping, but each bootstrap sample leaves out a different subset of data points.
Cross-validation: In cross-validation the original sample is partitioned into complementary subsets, performing the analysis on one subset. Common types are k-fold cross-validation and leave-one-out cross-validation.

10 Bonus Questions to Stand Out As An Intern

40. What is overfitting and how can you prevent it?

Overfitting happens when a model fits the training data too closely, negatively impacting its ability to generalize to new data.
Regularization techniques like L1/L2 regularization, dropout, early stopping, and cross-validation can help prevent overfitting.

41. What evaluation metrics would you use for a classification model?

For classification model metrics I would use are Accuracy, precision, recall, F1 score, AUC-ROC, and confusion matrix which provide key insights into model performance for classification.

42. How does Naive Bayes classification work?

Naive Bayes uses Bayes Theorem to calculate the probability of each class given the features, under the assumption the features are independent given the class. The class with the highest probability is predicted.

43. How do random forests improve on decision tree models?

Random forests build many decision trees on bootstrapped samples and average their results to reduce variance while tuning hyper params reduces bias.

44. Why is model interpretability important?

Interpretability builds trust by explaining model predictions. For regulated use cases, interpretability helps ensure fairness and ethics. Techniques like LIME and SHAP add insight.

45. Why is feature scaling useful?

Feature scaling through normalization or standardization puts all features on the same scale so that large-value features do not dominate. This improves model convergence.

46. Describe the steps for building and deploying a machine-learning model in production.

The steps for building a machine learning model in production are:

1. Understand business requirements
2. Preprocess and clean data
3. Split data for training and evaluation
4. Train and optimize models
5. Evaluate models on test data
6. Perform error analysis to refine
7. Deploy the model by integrating it into production systems
8. Monitor model performance to detect concept drift

47. Explain the concept of ensemble methods.

Ensemble methods combine multiple weaker models together to create an overall stronger model. Popular examples are random forests, gradient boosting, and model stacking.

48. Why is feature engineering important?

Feature engineering creates informative input features to improve model performance. Useful techniques include normalization to standardize scales, dimensionality reduction to decrease noise, interactions to model synergies between features, and binning and clustering to group-related values.

49. What is selection bias and how can you address it?

Selection bias occurs when sample data isn't representative of the population intended for inference. Techniques like rebalancing classes, oversampling, and synthetic data generation help.

50. If you are given a confusion matrix using it how can you calculate accuracy?

A confusion matrix is used to evaluate the performance of a classification model. It allows us to calculate various metrics like accuracy, precision, recall, etc. To calculate accuracy using a confusion matrix I will divide the number of total correct predictions by the total number of predictions.

Accuracy=(TP+TN)/(TP+TN+FP+FN), Where
TP = Number of true positives
TN = Number of true negatives
FP = Number of false positives
FN = Number of false negatives

How to crack a data science internship interview?

Internships are organized learning experiences provided by companies for a set duration. They bridge classroom knowledge with real-world application, helping us understand practical situations.

Engaging in a Data Science Internship can significantly enhance your resume, setting you apart and reinforcing your skills.

Some tips to crack a data science internship interview are:

• Be confident: Being confident in an internship is the first step in every interview. Being ready to face any questions with confidence is the key to success in cracking an interview.
• Be passionate: Showing up passion for data science can make the interviewer fascinated by your interest in data science. Along with answering questions, you can also use this opportunity to show effort if you have worked on any particular project related to data science
• Have an honest attitude: Honesty is something that the interviewer looks for. Make sure you make a straightforward approach to answering the questions in a precise and concise manner.
• Brush up your skills: By brushing up means knowing the basics about maths and fundamental statistics and also the basics about regression clustering hypothesis testing as they are part of data science.
• Stay up-to-date: Stay up-to-date on the latest data science tools and techniques. Also, show your interest in discussing current trends ongoing in data science.
• Build your resume: Build your resume in such a way that your resume stands out from others and provides examples that showcase your skills.