NCR Corporation Data Science Interview Questions and Answers


In the competitive landscape of the tech industry, landing a role in data science and analytics at a company like NCR Corporation can be a game-changer for your career. With its focus on innovative solutions in the fintech, retail, and hospitality sectors, NCR offers exciting opportunities for data professionals to make a significant impact.

Preparing for a data science and analytics interview at NCR requires a solid understanding of fundamental concepts, as well as the ability to apply them to real-world scenarios. To help you ace your interview, we’ve compiled a comprehensive guide featuring common interview questions and expert answers tailored specifically for NCR Corporation.

Table of Contents

Python Interview Questions

Question: Differentiate between Python 2 and Python 3.

Answer: Python 2 and Python 3 are both versions of the Python programming language, but Python 3 is not backward-compatible with Python 2. Python 3 introduces several new features and syntax changes to improve the language’s consistency and eliminate ambiguity. Python 2 is no longer supported since January 1, 2020.

Question: What are the key features of Python?

Answer: Key features of Python include simplicity, readability, versatility, dynamically-typed nature, automatic memory management, extensive standard library, and easy integration with other languages.

Question: Explain the difference between a list and a tuple in Python.

Answer: Lists and tuples are both sequences in Python, but lists are mutable (can be modified) whereas tuples are immutable (cannot be modified). Lists are defined using square brackets [ ], and tuples are defined using parentheses ( ).

Question: What is the purpose of the __init__ method in Python classes?

Answer: The __init__ method is a special method in Python classes that is automatically called when a new instance of the class is created. It is used to initialize the object’s attributes.

Question: Explain the concept of decorators in Python.

Answer: Decorators are a powerful feature in Python that allow you to modify the behavior of functions or methods. They are implemented using the @decorator_name syntax and typically take a function as input and return a new function that extends or modifies the behavior of the original function.

Question: What is the purpose of the yield keyword in Python?

Answer: The yield keyword is used in Python to create a generator function. When a function contains a yield statement, it becomes a generator function, which can yield multiple values one at a time instead of returning a single value. This allows for lazy evaluation and efficient memory usage.

Question: Explain the concept of duck typing in Python.

Answer: Duck typing is a dynamic typing technique used in Python where the type or class of an object is determined by its behavior (i.e., methods and properties) rather than its explicit type. If an object behaves like a certain type, it is considered to be of that type, regardless of its actual class.

Question: What are Python virtual environments, and why are they used?

Answer: Python virtual environments are isolated environments that contain their own Python interpreter and libraries. They are used to manage dependencies and avoid conflicts between different projects by allowing you to install project-specific dependencies in a separate environment.

Question: Explain the purpose of the map() function in Python.

Answer: The map() function in Python is used to apply a function to each item in an iterable (e.g., a list) and return an iterator that yields the results. It takes two arguments: the function to apply and the iterable to apply it to.

ML and Data Mining Interview Questions

Question: Explain the bias-variance tradeoff.

Answer: The bias-variance tradeoff is a fundamental concept in machine learning that describes the balance between the bias of the model and its variance. A high-bias model may oversimplify the data (underfitting), while a high-variance model may capture noise in the data (overfitting). Finding the right balance is crucial for building models that generalize well to unseen data.

Question: What is cross-validation, and why is it important?

Answer: Cross-validation is a technique used to assess the performance of a machine learning model by splitting the data into multiple subsets, training the model on some subsets, and evaluating it on the remaining subsets. It helps to estimate how well a model will generalize to new, unseen data and provides a more reliable performance metric than a single train-test split.

Question: Explain the concept of feature engineering.

Answer: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves selecting, extracting, and combining relevant features that capture meaningful information from the data, thereby enhancing the model’s predictive power.

Question: What are the different types of machine learning algorithms?

Answer: Machine learning algorithms can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning includes algorithms like linear regression, logistic regression, decision trees, and support vector machines. Unsupervised learning includes algorithms like clustering (e.g., k-means clustering) and dimensionality reduction (e.g., PCA). Reinforcement learning involves training agents to make sequential decisions through trial and error.

Question: What is overfitting, and how can it be prevented?

Answer: Overfitting occurs when a model learns the training data too well, capturing noise or irrelevant patterns that do not generalize to new data. To prevent overfitting, techniques such as cross-validation, regularization (e.g., L1 and L2 regularization), early stopping, and using simpler models can be employed.

Question: Explain the terms ‘precision’ and ‘recall’ in the context of classification models.

Answer: Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. It measures the accuracy of positive predictions. Recall, on the other hand, is the ratio of true positive predictions to the total number of actual positive instances in the data. It measures the ability of the model to capture all positive instances.

Question: What is the purpose of clustering algorithms in data mining?

Answer: Clustering algorithms are used in data mining to group similar data points together based on their characteristics or attributes. The goal is to discover hidden patterns or structures within the data and identify natural groupings or clusters without prior knowledge of the labels.

Question: Explain the difference between association rule mining and sequence mining.

Answer: Association rule mining is a data mining technique used to discover interesting relationships or associations between variables in large datasets. It identifies patterns where one set of items tends to co-occur with another set of items. Sequence mining, on the other hand, focuses on discovering temporal patterns or sequences of events in sequential data, such as time series or transactional data.

SQL Interview Questions

Question: Differentiate between SQL and NoSQL databases.

Answer: SQL databases, also known as relational databases, store data in tables with rows and columns and use structured query language (SQL) for data manipulation. NoSQL databases, on the other hand, are non-relational databases that can store data in various formats such as key-value pairs, documents, graphs, or wide-column stores. NoSQL databases offer more flexibility and scalability for handling unstructured or semi-structured data compared to traditional SQL databases.

Question: What are the different types of SQL joins?

Answer: SQL joins are used to combine rows from two or more tables based on a related column between them. The main types of SQL joins are:

  • Inner Join: Returns rows that have matching values in both tables.
  • Left Outer Join (or Left Join): Returns all rows from the left table and matching rows from the right table.
  • Right Outer Join (or Right Join): Returns all rows from the right table and matching rows from the left table.
  • Full Outer Join: Returns all rows when there is a match in either the left or right table.

Question: Explain the difference between the WHERE and HAVING clauses in SQL.

Answer: The WHERE clause is used to filter rows based on a specified condition in a SELECT, UPDATE, or DELETE statement. It is applied before the aggregation in SQL queries. The HAVING clause, on the other hand, is used to filter groups of rows returned by a GROUP BY clause based on a specified condition. It is applied after the aggregation in SQL queries.

Question: What is the purpose of the GROUP BY clause in SQL?

Answer: The GROUP BY clause is used to group rows that have the same values into summary rows, typically to perform aggregation functions such as SUM, AVG, COUNT, MIN, or MAX on each group. It is commonly used in combination with aggregate functions to summarize data based on specified criteria.

Question: Explain the concept of normalization in database design.

Answer: Normalization is the process of organizing data in a database to reduce redundancy and dependency by breaking down large tables into smaller, related tables. It involves dividing a database into multiple tables and defining relationships between them to ensure data integrity and minimize anomalies such as insertion, update, and deletion anomalies. Normalization is typically achieved through a series of normal forms, such as First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and so on.

Question: What are SQL indexes, and why are they important?

Answer: SQL indexes are data structures used to improve the speed of data retrieval operations (e.g., SELECT statements) by providing quick access to specific rows in a table. They are created on one or more columns of a table and allow the database management system to locate rows efficiently without scanning the entire table. Indexes are important for optimizing query performance, especially for tables with large amounts of data.

Question: Explain the difference between a primary key and a foreign key in SQL.

Answer: A primary key is a column or a combination of columns that uniquely identifies each row in a table. It ensures data integrity and enforces entity integrity constraints by preventing duplicate or null values. A foreign key, on the other hand, is a column or a set of columns in a table that establishes a relationship with the primary key or a unique key in another table. It enforces referential integrity constraints by maintaining consistency between related tables.

Question: What is a subquery, and how is it different from a regular query?

Answer: A subquery, also known as a nested query or inner query, is a query nested within another query (the outer query). It is used to retrieve data based on the results of another query, acting as a temporary table or dataset. Subqueries can be used in various SQL clauses such as SELECT, INSERT, UPDATE, or DELETE to filter, manipulate, or aggregate data. Unlike regular queries, subqueries are enclosed within parentheses and executed before the outer query.

Behavioral Questions

Que: Define your work

Que: What are the various projects that have you implemented in your current org?

Que: How is your project related to a business problem and how you have solved it

Que: Why you are looking out for a change

Que: What motivates you to join our company

Que: Why looking out for a change


By mastering these key concepts and demonstrating a strong problem-solving mindset, you’ll be well-equipped to excel in your data science and analytics interview at NCR Corporation. Remember to showcase your passion for leveraging data-driven insights to drive business value and make a positive impact on NCR’s mission to shape the future of commerce.

Best of luck with your interview preparations, and we hope to see you contributing to NCR’s success story in the exciting field of data science and analytics!


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