Get ready for your upcoming Data Science Intern virtual interview. Familiarize yourself with the necessary skills, anticipate potential questions that could be asked and practice answering them using our example responses.
Updated April 21, 2024
The STAR interview technique is a method used by interviewees to structure their responses to behavioral interview questions. STAR stands for:
Situation: Describe the context or background of the situation you were in.
Task: Explain the challenge or task you were faced with.
Action: Detail the specific actions you took to address the task or challenge.
Result: Share the results or outcomes of your actions.
This method provides a clear and concise way for interviewees to share meaningful experiences that demonstrate their skills and competencies.
Unbalanced datasets can skew results. Your strategies to deal with them show your readiness to handle real-world data scenarios.
Dos and don'ts: "Address how you deal with unbalanced datasets, talking about specific techniques. Make sure to emphasize the importance of a balanced dataset for accurate model predictions."
Suggested answer:
Situation: In one of my courses, I was given a significantly unbalanced dataset to work with.
Task: I was tasked to build a model that could perform well despite the unbalanced nature of the data.
Action: To handle this, I learned and implemented techniques like oversampling the minority class, undersampling the majority class, and using appropriate evaluation metrics like AUC-ROC instead of accuracy.
Result: My handling of the unbalanced dataset allowed for a well-performing model despite the data challenges, earning high marks for the project.
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What inspired you to pursue a career in data science?
Understanding your motivation to pursue data science helps interviewers gauge your passion and long-term commitment to this demanding field.
Dos and don'ts: "Talk about your passion for data science, what sparked your interest, and how it ties to your goals. Make it personal and genuine."
Suggested answer:
Situation: I was first exposed to data science during my sophomore year at university when I took a course in statistical methods. I was fascinated by the power of data and the insights it could provide.
Task: This course inspired me to explore the field further, leading to my decision to major in statistics and minor in computer science to gain a solid foundation in data science.
Action: I sought out internships and projects that would allow me to apply my academic knowledge to real-world data problems. For instance, I took part in a data hackathon where we had to analyze a large dataset and provide actionable insights.
Result: Through these experiences, my passion for data science grew. It was the thrill of finding patterns in data and the impact of data-driven decision making that inspired me to pursue a career in this field.
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Can you explain what a false positive and a false negative are?
Explaining false positives and negatives indicates your understanding of model evaluation metrics, which are critical in many data science applications.
Dos and don'ts: "Provide a clear, concise definition of false positives and negatives. Use an example to illustrate your point if you can."
Suggested answer:
Situation: In a recent project at university, I worked on a machine learning model to predict student dropout rates.
Task: The model was critical because false positives and false negatives had significant implications. A false positive would mean wrongly identifying a student as likely to drop out, causing unnecessary concern and intervention. A false negative, on the other hand, would mean failing to identify a student at risk of dropping out.
Action: I carefully calibrated the model to minimize both types of errors and used visual aids to explain these concepts to non-technical stakeholders.
Result: The project highlighted the importance of understanding false positives and negatives, and balancing them based on the real-world implications of each.
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What programming languages are you proficient in for data analysis and why?
Proficiency in programming languages for data analysis shows your technical readiness for the role.
Dos and don'ts: "Discuss your proficiency in relevant programming languages, but also explain why you prefer certain ones. Consider referencing specific projects to illustrate your experience."
Suggested answer:
Situation: In my data science coursework, I learned and used several programming languages, but Python stood out for its simplicity and versatility.
Task: During a semester project that involved cleaning, analyzing, and visualizing a complex dataset, I had to choose a language that would be efficient and easy to use.
Action: I chose Python because of its powerful data analysis libraries like pandas, numpy, and matplotlib. Moreover, sklearn made implementing machine learning models a breeze. For more interactive and complex visualizations, I used Plotly and Seaborn.
Result: The ease of use, readability, and vast supportive community makes Python my go-to language for data analysis. The project was a success, and my proficiency in Python contributed to that.
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Can you describe a time when you had to analyze a large dataset and how you approached it?
Analyzing large datasets is a common data science task. Your approach gives insights into your problem-solving skills.
Dos and don'ts: "Describe the situation where you analyzed a large dataset, focusing on your approach and techniques used. Be sure to highlight the challenges you faced and how you overcame them."
Suggested answer:
Situation: During an internship, I was tasked with analyzing a dataset containing millions of records related to customer transactions.
Task: The challenge was to derive insights that could help improve our marketing strategy.
Action: I used a combination of SQL for data extraction, Python for data cleaning and transformation, and Tableau for data visualization. Using clustering algorithms, I was able to segment customers based on their buying behavior.
Result: The insights derived from this analysis significantly improved the targeting of our marketing campaigns, leading to an increase in customer engagement and sales.
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How do you handle missing or inconsistent data in a dataset?
Handling missing or inconsistent data is crucial to ensuring accurate analysis. This checks your understanding of data cleaning and preparation techniques.
Dos and don'ts: "Explain the methods you use for dealing with missing or inconsistent data. If possible, mention a project where you had to handle this situation."
Suggested answer:
Situation: During an analytics project at my university, I was handed a dataset that had numerous missing and inconsistent entries.
Task: My task was to clean this dataset to ensure it was ready for analysis.
Action: Using Python's pandas library, I dealt with missing values either by interpolation or using central tendency measures, based on the context. For categorical inconsistencies, I standardised the entries using appropriate mapping.
Result: My proactive handling of the missing and inconsistent data resulted in a reliable dataset, which led to more accurate and meaningful insights during the analysis stage.
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Describe a project or situation where you had to use machine learning techniques.
Detailing a project where you used machine learning techniques validates your theoretical knowledge with practical application.
Dos and don'ts: "When describing your use of machine learning techniques, focus on a specific project. Explain what you did, the challenges, and the outcomes."
Suggested answer:
Situation: For my final semester project, I worked on a recommendation system for an online bookstore.
Task: The goal was to provide relevant recommendations to users based on their past behaviour and that of similar users.
Action: I used machine learning techniques, specifically collaborative filtering, to accomplish this. Using Python's Scikit-Learn, I built a model that predicted a user's interest in various books based on their past behaviour and that of similar users.
Result: The model was successful, improving the bookstore's user engagement and average session duration during testing.
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Explain what cross-validation is and why it's important.
Cross-validation is key to model building. Your understanding of it shows your knowledge of model validation techniques.
Dos and don'ts: "Define cross-validation and explain its importance in simple terms. You could use an example to illustrate your point."
Suggested answer:
Situation: When I was first learning about machine learning, I understood the importance of validating the model's performance.
Task: I had to ensure that the model's performance was not only based on the training data but also held on unseen data.
Action: I learned about cross-validation techniques like K-Fold Cross-Validation and implemented them in my projects. This approach provided a better estimate of the model performance as it was tested on multiple subsets of the data.
Result: Using cross-validation, I could ensure that my models were not overfitting to the training data and had a good generalisation performance. It boosted my confidence in the models' performance when making predictions on unseen data.
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How would you handle a situation where your analysis contradicts business intuition?
Contradicting business intuition tests your communication and problem-solving skills.
Dos and don'ts: "Discuss how you would reconcile data analysis with business intuition. Emphasize communication, understanding different perspectives, and reliance on data-driven insights."
Suggested answer:
Situation: During a project at my university, my analysis suggested a product line should be discontinued, which was contrary to the business intuition of the project's stakeholders.
Task: I was tasked to validate my analysis and persuade the stakeholders of its credibility.
Action: I double-checked my work to ensure there were no errors. I then presented my findings along with a clear explanation of the analysis methodology and why the data suggested this course of action. I also laid out potential consequences of ignoring the data-driven advice.
Result: After a thorough discussion and re-evaluation, the stakeholders understood the logic behind the analysis and agreed to run a limited-scale trial of the suggested action, which eventually proved to be beneficial.
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How do you ensure the validity of your data analysis?
Ensuring validity of data analysis is integral to data science. This reveals your knowledge of error-checking and validation practices.
Dos and don'ts: "Describe how you check and confirm your analysis results for accuracy. This could involve discussing processes like data cleaning, validation, and error-checking."
Suggested answer:
Situation: During a university project, I was working with a dataset that was crucial for drawing key insights.
Task: The task was to ensure the validity of the data analysis conducted on this dataset.
Action: I ensured the validity of my data analysis by double-checking the algorithms and statistical methods applied, validating assumptions, and cross-verifying results with established benchmarks. I also used cross-validation to ensure the model's generalizability on unseen data.
Result: My meticulous approach to data validity instilled confidence in my findings and the insights derived from the project were well received.
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Can you explain the difference between supervised and unsupervised learning?
Distinguishing between supervised and unsupervised learning reflects your understanding of machine learning techniques.
Dos and don'ts: "Explain the difference between supervised and unsupervised learning, using examples for clarity. Focus on the situation where each method would be appropriate."
Suggested answer:
Situation: In my machine learning class, I was asked to work on a project where I had to choose between supervised and unsupervised learning.
Task: My task was to decide the appropriate learning method and justify my choice.
Action: I went with supervised learning as the data had clear labels, and we were interested in prediction accuracy. I explained to my professor that in supervised learning, the model learns on a labeled dataset providing accurate outcomes, whereas unsupervised learning is more about discovering hidden patterns.
Result: My professor was impressed with my understanding of the fundamental differences and the thoughtful application to our project.
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How would you clean a messy dataset?
Cleaning a messy dataset is a common task in data science. Your approach provides insight into your data cleaning skills.
Dos and don'ts: "Discuss your approach to cleaning a messy dataset. Mention specific techniques and tools that you use."
Suggested answer:
Situation: During a summer research project, I encountered a dataset with several inconsistencies and missing values.
Task: My task was to clean the dataset in preparation for analysis.
Action: I used a variety of methods, including interpolation for missing numerical data, categorical imputation for categorical data, and deletion for values that couldn't be reliably replaced. For inconsistencies, I standardized and cleaned the data using Python scripts.
Result: As a result, the cleaned dataset was comprehensive, reliable, and ready for effective analysis.
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Can you describe your experience with data visualization and some tools you use?
Experience with data visualization tools is crucial as data science often involves presenting data in a digestible format.
Dos and don'ts: "Talk about your experience with data visualization, highlighting any specific tools you prefer. Mention any projects where your data visualization skills played a crucial role."
Suggested answer:
Situation: In my Data Visualization class, I had the opportunity to work with a variety of tools to present data in an understandable and appealing way.
Task: My task was to choose an appropriate data visualization tool and use it to effectively present data from a course project.
Action: I chose to use Tableau due to its intuitive interface and powerful visualization capabilities. I created dashboards to present key findings from the project, using various chart types to display different data points.
Result: My professor and classmates were impressed by the clarity and detail of my visualizations, and I received an A for the project.
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What's your understanding of deep learning?
Understanding deep learning indicates your familiarity with advanced machine learning techniques, showing your potential for growth in the field.
Dos and don'ts: "Discuss deep learning in simple terms. If you've had any experience with deep learning, talk about it."
Suggested answer:
Situation: During my course on Neural Networks, I was introduced to the concept of deep learning.
Task: The task was to grasp the concept, including its differences from traditional machine learning, and implement a simple deep learning model.
Action: I studied additional resources to deepen my understanding of deep learning. I learned that it's a subset of machine learning that uses neural networks with several hidden layers, making it highly effective for complex tasks like image recognition, natural language processing, and more. I implemented a simple image classification model using a convolutional neural network in TensorFlow.
Result: I was able to achieve a good accuracy rate with my model, demonstrating my understanding of deep learning.
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How do you keep up-to-date with the latest data science developments and techniques?
Keeping up-to-date with latest developments in data science displays your passion and dedication to continuous learning in this rapidly evolving field.
Dos and don'ts: "Demonstrate your commitment to staying updated with the field. This could involve mentioning blogs, podcasts, courses, or books you engage with. Make sure to emphasize why continuous learning is important in data science."
Suggested answer:
Situation: As an aspiring data science professional, it's important for me to keep up-to-date with the latest developments and techniques in the field.
Task: My task was to find a way to consistently update my knowledge and skills in data science.
Action: I have subscribed to multiple data science-related blogs, forums, and newsletters such as KDnuggets, Medium's Towards Data Science, and Data Science Central. I also participate in Kaggle competitions to practically apply newly learned techniques. Additionally, I've set up Google Scholar alerts for the latest research papers in my areas of interest within data science.
Result: By staying current with the latest trends and techniques, I've been able to continually enhance my skill set, stay competitive, and bring innovative ideas to the projects I work on.
