Question Overview: This question shows the candidate’s experience in working with unstructured data and their problem-solving skills.
Sample Answer: Recently, I worked on a project where I was tasked with helping the sales team improve its customer relationship management process. But the long waiting period required for detecting new and changed data led to some delays. Then to expedite the process, I utilized a combination of FastScan Data Discovery, NoSQL database, and Amazon's Simple Storage Service (S3) to collect and analyze the data for producing the desired results before the deadline.
Question Overview: This question assesses the candidate's ability to contribute to the company’s business objectives through their role.
Sample Answer: In my previous role, I was involved in a crucial prototyping project for developing user-centric software products for the company. To help the stakeholders finalize the best software products that the company could capitalize on, I conducted thorough customer & market requirement analysis, competitive research, and trends analysis. This allowed stakeholders to make an informed, data-backed decision about the product choice which went on to generate a revenue of 500 crores in the first quarter.
Question Overview: This question tests the candidate’s knowledge of machine learning, data classifications, and dataset relationship.
Sample Answer: Supervised machine learning is used to classify data and/or make predictions, while unsupervised learning is used to analyze relationships within datasets. The former is used in logistic regression, linear regression, decision trees, and support vector machine while the common uses for unsupervised learning include semantic clustering, market basket analysis, recommender systems, etc.
Question Overview: This question tests the candidate’s knowledge of regression analysis and model validation techniques.
Sample Answer: You can ensure that a regression model fits the data through graphical residual analysis. The residual plots from a fitted model provide information regarding the adequacy of different aspects of the model. R2 statistics is another numerical method for model validation that is also useful, but to a lesser extent than graphical methods. Model validation using graphic methods is advantageous over numerical methods because they are capable of illustrating a wide range of complex aspects of the model-data relationship. Most numerical methods for model validation focus on a particular aspect of the relationship between the model and the data and attempt to condense that information into a single descriptive number or test result.
Question Overview: This question tests the candidate’s knowledge of statistical techniques and their application at various stages of the data analysis process.
Sample Answer: There are many statistical techniques that can be used to analyze data and derive business recommendations. The first step is to identify the business problems and the data that will be used to analyze and solve the problem. Followed by that, the data needs to be cleaned and processed to ensure the accuracy of the analysis. The third step is to conduct exploratory data analysis (EDA) which includes summarizing the main characteristics of the data, such as central tendency, dispersion, and shape. This helps in identifying patterns, relationships, and trends in the data. Based on the EDA, appropriate statistical models can be selected to analyze the data. The choice of model depends on the type of data and the research question. After analyzing the data, the final step is to interpret the results in the context of the business problem. Business recommendations can be then developed based on the insights gained from the analysis.
Question Overview: This is an operational question asked by recruiters to learn more about how the candidate executes everyday tasks like wrangling and cleaning data.
Sample Answer: It is essential to perform both data wrangling and data cleaning before applying machine learning algorithms. By doing this, I will ensure that the data set fits my analysis, that their standard deviations meet the guidelines for the study, that their relationships are valid, and that the data is normalized and standardized. As a result, I eliminate outliers and variables that could potentially affect my results.
Question Overview: This question evaluates the candidate’s knowledge of data analysis and its benefits.
Sample Answer: One of the most important uses of data analysis is to drive growth and improve performance. To use data analysis to benefit the company, I typically follow a structured process that involves several steps. The first step is to collaborate with stakeholders to identify key business questions. I then gather and clean relevant data sources by using SQL query databases and tools like Excel or Python. Followed by this, I apply various statistical methods and visualization tools to identify trends, patterns, and insights by building predictive models or using dashboards to visualize key metrics. Finally, I communicate the findings to stakeholders to help them make informed, data-backed decisions and mitigate risks.
Question Overview: This question gives recruiters clarity on the candidate’s area of expertise and preferred way of conducting model performance.
Sample Answer: In my opinion, using the mean square error, or MSE, as a measure of a decision model's performance is flawed. The problem is that it weights larger errors more than smaller ones. Thus, the large deviations in the data are given greater weight. As a result, I prefer using mean absolute deviation, or MAE, which is a more robust model and provides a more accurate measure of a model's performance.
Question Overview: This question tests the candidate’s knowledge of logistic regression and related techniques.
Sample Answer: Logistic regression is a statistical technique used to model the relationship between a binary response variable (such as yes/no, 0/1) and one or more predictor variables. The logistic regression model estimates the relationship between the predictor variables and the outcome variable. This is done by fitting a logistic function to the observed data, which gives the probability of the outcome being in one category (e.g., "yes") versus another (e.g., "no") as a function of the predictor variables. Maximum likelihood estimation is used to find parameter values that will maximize the likelihood of the observed data given the model. After fitting, it can be utilized to make predictions on new data by plugging in values for predictors and calculating its associated probability of belonging to each category.
Question Overview: This question tests the candidate’s knowledge of feature selection methods and variables in machine learning.
Sample Answer: There are several feature selection methods that can be used depending on the dataset, the problem at hand, and the type of model being used. It is often necessary to try different methods and evaluate their performance to determine the most suitable one for a given problem. Some of the feature selection methods include filter, wrapper, embedded, Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and tree-based methods.
Question Overview: This question tests the candidate’s knowledge of dimensionality reduction in converting datasets.
Sample Answer: The transformation of a data set with many dimensions into data with few dimensions (fields) in order to communicate similar information succinctly is referred to as dimensionality reduction. The benefits of dimensionality reduction include improved data compression, reduced storage space and computation time, decreased computing time, and removal of redundant features.
Question Overview: This question tests the candidate’s knowledge of linear regression models and techniques to find mean square errors.
Sample Answer: RMSE is calculated by taking the square root of MSE. RMSE is known as Root Mean Square Deviation and it measures the average magnitude of the errors that are concerned with the deviations from the actual value. The target variable's square is used to measure MSE, while the target variable itself is used to measure RMSE. MSE effectively penalizes larger errors more severely because of the way it is constructed, just like the squared loss function from which it derives.
Question Overview: This question tests the candidate’s knowledge of the P value and its uses.
Sample Answer: In statistics, the P value refers to the probability of a specific statistical model that is equal to or more extreme than the observed results when the null hypothesis is true. Its significance lies in its use to evaluate the importance of observational data. There is always a chance that a correlation between two variables that researchers find to be apparent could simply be a coincidence. To ascertain whether the observed relationship could be the result of chance, a p-value calculation is used.
Question Overview: This question tests the candidate’s technical knowledge of the accuracy formula and its use in the confusion matrix.
Sample Answer: We can calculate accuracy by dividing the sum of true positives and negative positives by the sum of all entries of the confusion matrix or total observations.
Question Overview: This question tests the candidate's understanding of decision tree models and their ability to explain the advantages and disadvantages of using small or large decision trees.
Sample Answer: In my opinion, one large decision tree is usually more accurate than many small decision trees. Small decision trees can be limited in options and may not fit the problem well. On the other hand, a larger decision tree model with multiple options and a clear direction can provide a more accurate decision process model. I would aim to create a decision tree model that looks more like a forest with distinct branches to navigate through different scenarios. However, the size of the decision tree model should be appropriate to the data set and should be evaluated for overfitting.
Question Overview: The question seeks to understand how you present complex data with innovative ideas in a manner that is easy to understand and utilize.
Sample Answer: I always keep in mind the stakeholders' objectives when presenting my data analysis results. I often use visual representations of the data along with the actual numbers. In a recent study on the implementation of process improvement and the results it generated, I used a photo of the actual production line, and animated it. As the process improved, I increased the speed of the line, and reduced the production times, adding actual percentages of the time saved above the line. This helped the management team easily understand the results which were achieved, and they were able to utilize the information effectively.
Question Overview: This question aims to assess the candidate's knowledge and experience with machine learning methods.
Sample Answer: Since most of my work was focused on filtering spam within the email application we developed, I utilized the Classification methodology of machine learning, specifically the NaiveBayes algorithm. This allowed us to address the large data set we used for training and the multiple attributes we filtered for.
Question Overview: The interviewer is looking to assess your knowledge of linear analysis models and your ability to provide a balanced view of their strengths and weaknesses.
Sample Answer: Sure. A primary issue is that the model makes strong assumptions that may not apply to the specific data set. Secondly, the linear analysis model assumes normality between variables, minimal multicollinearity, homoscedasticity, and a linear relationship. Also, it cannot be used for binary or discrete outcomes. Despite these limitations, the linear analysis model can be very useful when used appropriately.
Question Overview: The interviewer is asking a technical question, seeking your knowledge and ability to compare different types of visual models used to analyze data. Your answer should be brief and to the point.
Sample Answer: So, boxplots and histograms are both used to illustrate a data distribution that communicates information in different ways. Histograms are bar charts that illustrate the frequency of a numerical variable's values. While boxplots do not illustrate the shape of the distribution, they enable us to view information such as the quartiles, the range, and outliers.
Question Overview: The question is about the data analytics software the candidate has experience with, and their ability to adapt to new software.
Sample Answer: I have experience working with Tableau, Statgraphics, and JMP Statistical Software. Additionally, I have worked with Salesforce Analytics Cloud and MATLAB in previous roles. I find it relatively easy to transition to new analytics software because of the similarities in features and user interfaces between the different packages.
Question Overview: This is a technical question asking for both the definition of the term and an explanation of how you use it in your work as a data scientist.
Sample Answer: As a data scientist, I use cross-validation to assess how well the analysis model I am using will perform on a new and independent dataset. A typical way to use cross-validation is to split the data into two sets. You then use one data set to build the model and the second one to test your analysis. This helps to improve the accuracy of and my trust in the results of the analysis.
Question Overview: The interviewer wants to know if you are familiar with data cleansing, its significance, and how it affects the quality of the results in a data science project.
Sample Answer: Data cleansing is the process of identifying and correcting or removing errors and inconsistencies in a dataset to improve its quality. As a data scientist, it's important to clean data before analyzing it to ensure accuracy in the results. This process includes handling missing values, identifying and correcting duplicate or incorrect data entries, and standardizing data formats. For example, in a recent project, I cleaned a large dataset by removing duplicate values, correcting incorrect entries, and replacing missing data with the median or mean values. This led to more accurate results and improved the overall quality of the analysis.
Question Overview: This is a technical question that requires you to compare three programming languages and their uses in data science. It is an opportunity for you to showcase your technical knowledge and expertise in data science.
Sample Answer: SAS is a proprietary language used primarily for data analysis and visualization. R is an open-source language that offers a wide range of statistical and graphical techniques, making it very useful for data exploration and visualization. Python, on the other hand, is a general-purpose programming language with a strong focus on data analysis and machine learning. It offers a vast range of libraries, including Pandas and NumPy, which make it easy to work with large data sets. In terms of their use in data science, R is great for exploratory data analysis and visualization, SAS is ideal for statistical modeling and analysis, and Python is widely used for machine learning and deep learning.
Question Overview: The interviewer wants to know if the candidate has worked on projects that require text analytics, and how they used their skills to achieve project goals.
Sample Answer: In my previous role, I worked on a project where we analyzed customer feedback from different social media platforms. We used text analytics to classify the feedback into categories such as positive, negative, or neutral. This helped us to identify areas where we could improve our product and customer service.
Question Overview: This question tests the candidate’s knowledge of collaborative filtering and machine learning.
Sample Answer: A recommendation engine can be generated by utilizing collaborative filtering which explains user behaviors and their purchasing patterns in terms of ratings, selection, and other factors. This way, by using the preferences of other users as the basis, the engine predicts what might appeal to a particular user.
Question Overview: This is a behavioral interview question that aims to understand your motivation and interest in the field of data science.
Sample Answer: I was always fascinated by numbers and their ability to reveal hidden patterns and insights. During my undergraduate studies, I took a course in data analysis, and that's when I discovered the power of data science in solving complex business problems. I find it gratifying to use data-driven insights to drive decision-making and bring value to the organization. The potential to make an impact through data analysis and the constant learning opportunities in this field keep me motivated to continue working in data science.
Question Overview: This question is asked to determine your familiarity with different data visualization tools and your experience using them. The interviewer is interested in understanding which tools you prefer to use and your reasoning behind your choice.
Sample Answer: I have experience using Tableau, Power BI, and QlikView. Although I have used all three tools, my favorite tool to use is Tableau. The reason I prefer Tableau is that it has a wide range of visualization options, is user-friendly, and has robust data connection options. Additionally, it provides more advanced features for data blending and joining, which I have found to be useful for complex data analysis tasks.
Question Overview: This is a behavioral question that aims to understand how a data scientist stays up-to-date with the latest trends and developments in the field of data science. The interviewer wants to see if the candidate is proactive in learning and growing as a data scientist.
Sample Answer: I stay up-to-date with the latest developments and advancements in the field of data science by attending industry conferences, following relevant publications, and participating in online data science communities. I prioritize those developments that align with the organization's goals and objectives, as well as those that have the potential to improve the efficiency and accuracy of our data-driven processes.
Question Overview: The interviewer wants to know if you're familiar with decision trees and how you utilize them in your role as a data scientist.
Sample Answer: A decision tree is a graphical representation that breaks down decisions into a series of questions and possible outcomes. It's commonly used in classification tasks where the output is a discrete class. In my previous job, we used decision trees to predict customer churn. We first trained the model using historical data, and then used it to predict the probability of churn for new customers. Decision trees are easy to interpret and can handle a mix of categorical and continuous variables, but they can be prone to overfitting and are sensitive to small changes in the data.
Question Overview: This question tests the candidate's knowledge of linear regression and their ability to explain the necessary assumptions for it.
Sample Answer: Some of the key assumptions for linear regression are linearity, independence, homoscedasticity, normality, and no multicollinearity. These assumptions ensure the model is unbiased, consistent, and efficient in estimating the relationship between variables.