20+ Data Science Engineer Interview Questions and Answers

DAG stands for Directed Acyclic Graph. It is a finite directed graph with no cycles.

• DAG is a collection of nodes connected by edges where each edge goes from one node to another, but no cycles are allowed.

• In the context of Spark, a DAG represents the sequence of transformations that need to be applied to the input data to get the final output.

• When a Spark job is submitted, Spark creates a DAG of the transformations specified in the code. This DAG is optimized and executed by ...read more

A binary semaphore is a synchronization primitive that can have only two values: 0 and 1.

• It is used to control access to a shared resource by multiple threads or processes.

• When the semaphore value is 0, it means the resource is currently being used and other threads/processes must wait.

• When the semaphore value is 1, it means the resource is available and can be used by a thread/process.

• Binary semaphores are often used to implement mutual exclusion and prevent race conditions....read more

I would communicate openly with the client, provide updates on the progress, and discuss potential solutions to meet the deadline.

• Communicate proactively with the client about the delay

• Provide regular updates on the progress of the task

• Discuss potential solutions to meet the deadline, such as reallocating resources or extending the timeline

• Apologize for the delay and take responsibility for the situation

• Ensure that the client understands the reasons for the delay and the step...read more

RDD stands for Resilient Distributed Dataset and is the fundamental data structure of Apache Spark.

• RDD is a distributed collection of objects that can be operated on in parallel.

• DataFrames and Datasets are higher-level abstractions built on top of RDDs.

• RDDs are more low-level and offer more control over data processing compared to DataFrames and Datasets.

ML stands for machine learning, a subset of artificial intelligence that focuses on developing algorithms to make predictions or decisions based on data. Regression and classification are two types of supervised learning techniques in ML.

• ML is a subset of AI that uses algorithms to make predictions or decisions based on data

• Regression is a type of supervised learning used to predict continuous values, such as predicting house prices based on features like size and location

• Cla...read more

The static keyword in Java is used to create variables and methods that belong to the class itself, rather than an instance of the class.

• Static variables are shared among all instances of a class.

• Static methods can be called without creating an object of the class.

• Static blocks are used to initialize static variables.

• Static nested classes do not require an instance of the outer class to be instantiated.

Linear regression combines independent variables to create a predictive model.

• Linear regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables.

• It combines the independent variables by estimating the coefficients that best fit the data and create a linear equation.

• The equation represents the relationship between the independent variables and the dependent variable.

• The coefficients determine the slope...read more

XGBoost was chosen for its high performance, scalability, and ability to handle complex datasets.

• XGBoost is known for its speed and performance, making it ideal for large datasets and complex tasks.

• It uses a gradient boosting framework which helps in reducing errors and improving accuracy.

• XGBoost has built-in regularization techniques to prevent overfitting and improve generalization.

• It supports parallel processing and can handle missing values in the dataset effectively.

• XGBo...read more

Partitioning is the process of dividing data into smaller chunks for better organization and processing in distributed systems.

• Partitioning helps in distributing data across multiple nodes for parallel processing.

• Coalesce is used to reduce the number of partitions without shuffling data, while repartition is used to increase the number of partitions by shuffling data.

• Example: coalesce(5) will merge partitions into 5 partitions, repartition(10) will create 10 partitions by shu...read more

I am passionate about leveraging data to drive business decisions and solve complex problems.

• I have a strong background in data analysis and machine learning, making me well-suited for this role.

• Vericast's reputation for innovation and commitment to utilizing data-driven strategies aligns with my career goals.

• I am excited about the opportunity to work with a talented team of data scientists and engineers at Vericast.

Python code to calculate correlation between two features

• Import pandas library

• Use df.corr() method to calculate correlation between two features

• Specify the two features as arguments to the corr() method

Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications.

• Kubernetes provides a platform for automating deployment, scaling, and operations of application containers across clusters of hosts.

• It groups containers that make up an application into logical units for easy management and discovery.

• Kubernetes architecture consists of a master node that manages the cluster and worker nodes that run t...read more

pep8 is a style guide for Python code.

• pep8 provides guidelines for formatting, naming, and organizing Python code.

• It helps to improve code readability and maintainability.

• Examples of guidelines include using 4 spaces for indentation, limiting line length to 79 characters, and using snake_case for variable names.

• pep8 is not mandatory, but following its guidelines is considered good practice in the Python community.

Window functions in MySQL and pandas are used for performing calculations across a set of rows related to the current row.

• In MySQL, window functions can be used with OVER() clause to perform calculations like ranking, cumulative sum, moving average, etc.

• In pandas, window functions can be applied using the rolling() and expanding() methods to calculate statistics over a specified window of rows.

• Example: In MySQL, to calculate a moving average using window function - SELECT val...read more

Select model based on problem type, data size, interpretability, and performance metrics.

• Identify problem type (classification, regression, clustering, etc.)

• Consider data size and complexity

• Evaluate interpretability vs. performance trade-off

• Choose models based on performance metrics (accuracy, precision, recall, etc.)

• Use cross-validation to compare and select the best model

Developed a recommendation system for an e-commerce website

• Used collaborative filtering to recommend products to users

• Implemented the system using Python and Apache Spark

• Evaluated the system's performance using precision and recall metrics

• Improved the system's performance by incorporating user feedback

Comment is for code readability, docstring is for documentation

• Comments are used to explain code and make it more readable

• Docstrings are used to document functions, classes, and modules

• Comments start with #, docstrings are enclosed in triple quotes

• Docstrings can be accessed using __doc__ attribute

Handling imbalanced data involves techniques like resampling, using different algorithms, and adjusting class weights.

• Use resampling techniques like oversampling or undersampling to balance the data

• Utilize algorithms that are robust to imbalanced data, such as Random Forest or XGBoost

• Adjust class weights in the model to give more importance to minority class

Outliers can be handled by removing, transforming, or imputing them based on the context of the data.

• Identify outliers using statistical methods like Z-score, IQR, or visualization techniques.

• Remove outliers if they are due to data entry errors or measurement errors.

• Transform skewed data using log transformation or winsorization to reduce the impact of outliers.

• Impute outliers with the median or mean if they are valid data points but extreme.

• Use robust statistical methods lik...read more

Fact is a statement that can be proven true or false, while figure is a numerical value or statistic.

• Fact is a statement that can be verified or proven true or false.

• Figure is a numerical value or statistic.

• Facts are objective and can be verified through evidence or research.

• Figures are quantitative data used to represent information.

• Example: 'The sky is blue' is a fact, while 'The average temperature is 25 degrees Celsius' is a figure.

Software development life cycle is a process of planning, designing, developing, testing, deploying, and maintaining software.

• SDLC is a framework that helps in the development of software.

• It consists of several phases such as planning, designing, developing, testing, deploying, and maintaining software.

• Each phase has its own set of activities and deliverables.

• The goal of SDLC is to produce high-quality software that meets the customer's requirements.

• Examples of SDLC models in...read more

Data modelling is the process of creating a visual representation of data to understand its structure, relationships, and patterns.

• Data modelling involves identifying entities, attributes, and relationships in a dataset.

• It helps in organizing data in a way that is easy to understand and analyze.

• Common data modelling techniques include Entity-Relationship (ER) diagrams and UML diagrams.

• Data modelling is essential for database design, data analysis, and machine learning.

• Example...read more

TFIDF is a technique to quantify the importance of a word in a document. CNN is a deep learning algorithm commonly used for image recognition.

• TFIDF stands for Term Frequency-Inverse Document Frequency and is used to evaluate the importance of a word in a document relative to a collection of documents.

• TFIDF is calculated by multiplying the term frequency (number of times a word appears in a document) by the inverse document frequency (logarithm of the total number of documents...read more

Developed a machine learning model to predict customer churn for a telecommunications company.

• Used historical customer data to train the model

• Applied various classification algorithms such as logistic regression and random forest

• Evaluated the model's performance using metrics like accuracy and AUC-ROC

• Implemented the model in a production environment for real-time predictions

I have worked on projects involving predictive modeling, natural language processing, and machine learning algorithms.

• Developed a predictive model to forecast customer churn for a telecom company

• Implemented sentiment analysis using NLP techniques on social media data

• Utilized machine learning algorithms to classify spam emails