Data Scientist (Analyst) interview questions:
Successful individuals in the roles of Data Scientists, Managers, and Analysts excel in extracting actionable insights from the data generated by an organization. They possess a keen understanding of the data required for collection and follow a well-structured process for conducting effective data analyses and constructing predictive models.
The role of a Data Scientist focused on data analysis demands candidates with a solid foundation in key areas such as statistics, operations research, and machine learning. Additionally, they should have strong database skills, including proficiency in SQL, enabling them to retrieve, clean, and process data from a variety of sources. Multiple educational backgrounds can lead to this role, with candidates originating from mathematics, statistics, computer science, or engineering disciplines.
In this capacity, Data Scientists frequently work with scripting languages like R, Python, or MATLAB, with less emphasis on programming languages and general software engineering practices required for developing production-quality software. Some questions may need to be tailored toward more quantitative and statistical analysis topics. Moreover, this role often involves the presentation of analysis findings. Therefore, skills related to information visualization, such as knowledge of Tableau or D3.js, and effective communication abilities are highly valuable.
Role-specific questions:
Basic ideas in statistics, probability and machine learning
- Can you define what a confidence interval is and elaborate on its practical utility?
- Explain the distinction between statistical independence and correlation in the context of data analysis.
- Describe conditional probability and the significance of Bayes’ Theorem in practical applications.
- When employing optimization techniques like stochastic gradient descent to train a model, how can we ascertain if we are converging to a solution, and does convergence always guarantee the best possible solution?
- How do we determine whether we have gathered sufficient data to effectively train a model?
- Clarify the purpose of training, test, and validation data sets in data analysis, and explain how they are best utilized.
- Define clustering and provide an example of an algorithm used for clustering. How can we evaluate the quality of obtained clusters, and how do we estimate an appropriate number of clusters for a given dataset?
- Discuss the notion that correlation does not imply causation and its implications in data analysis.
- Distinguish between unsupervised and supervised learning in machine learning.
- Explain the differences between regression and classification in the context of machine learning.
- Elaborate on the concept of the bias-variance tradeoff in statistical models and its implications.
- Define over-fitting and its relationship to the bias-variance tradeoff. Discuss regularization and provide examples of its use in models.
- If you are faced with a binary classification problem where one class is significantly underrepresented, describe the challenge and how to train a model. What performance metrics should be used in this scenario?
- How many unique subsets can be generated from a collection of n distinct objects?
- What approach would you take to construct a data-driven recommender system, and what are the limitations associated with this approach?
Tools, visualization and presentation
- In which setting or environments do you typically perform your data analyses?
- Could you provide insights into your experience with working on data from databases, and are you well-versed in SQL?
- What data visualization tools have you utilized in your work, such as Tableau, D3.js, or R, and how proficient are you with them?
- Do you have a presentation readily available for sharing, for example, on platforms like SlideShare?
- Have you presented reports and findings directly to senior management during your prior roles, and can you elaborate on your experience in this regard?
- Are you at ease with public speaking, and have you ever delivered a technical presentation to a large audience?
Operational questions:
Data Analytics Interview Questions
- Outline the steps you typically take when developing a data-driven model to address a business problem. Provide an example, such as automatically classifying customer support emails by topic or sentiment, or predicting employee churn in a company.
- Explain various preprocessing steps you might carry out on data before using them to train a model and specify the circumstances in which these steps would be applied.
- Can you categorize certain models as simple and others as complex? Discuss the comparative strengths and weaknesses of choosing a more complex model over a simpler one.
- In what ways can models be combined to create model ensembles, and what are some advantages of employing this approach?
- Define dimensionality reduction and elucidate different methods for performing it. Highlight situations where and reasons why we might opt for dimensionality reduction.
