In today’s competitive landscape, data science and analytics play a crucial role in driving innovation, improving customer experiences, and optimizing operations. As a leading food delivery company, Deliveroo relies on data-driven insights to enhance delivery efficiency, personalize customer recommendations, and drive business growth. If you’re preparing for a data science and analytics interview at Deliveroo, this comprehensive guide will equip you with the knowledge and insights needed to excel in the interview process.
Table of Contents
R and Python Interview Questions
Question: What is R, and why is it popular in data analysis?
Answer: R is a programming language and environment specifically designed for statistical computing and graphics. It’s popular in data analysis due to its extensive collection of packages for statistical modeling, data visualization, and machine learning. Additionally, its open-source nature and active community support make it a preferred choice for data scientists and analysts.
Question: Explain the difference between vectors and lists in R.
Answer: Vectors in R are one-dimensional arrays that can hold elements of the same data type, such as numeric, character, or logical values. Lists, on the other hand, can hold elements of different data types and are more flexible than vectors. Lists are created using the list() function and are commonly used for storing heterogeneous data structures.
Question: How would you read a CSV file into R and perform basic data manipulation?
Answer: Use the read.csv() function to read a CSV file into R as a data frame. Once loaded, you can perform basic data manipulation tasks such as subsetting rows and columns, filtering data based on conditions, summarizing data with functions like summary() or aggregate(), and creating new variables using vectorized operations.
Question: What is ggplot2, and how would you create a scatter plot using ggplot2 in R?
Answer: ggplot2 is a powerful data visualization package in R that allows users to create highly customizable plots with a layered grammar of graphics. To create a scatter plot using ggplot2, use the ggplot() function to specify the data and aesthetics (such as x and y variables), and then add the geom_point() layer to plot the points. Additional customization can be done using various ggplot2 functions for themes, labels, and scales.
Question: What are the key features of Python, and why is it widely used in data science?
Answer: Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. It’s widely used in data science due to its extensive libraries such as NumPy, Pandas, and sci-kit-learn, which provide robust tools for data manipulation, analysis, and machine learning. Additionally, Python’s syntax is intuitive and easy to learn, making it accessible to users with diverse backgrounds.
Question: Differentiate between Python lists and tuples.
Answer: Lists and tuples are both sequence data types in Python, but they have key differences. Lists are mutable, meaning their elements can be modified after creation, while tuples are immutable, meaning their elements cannot be changed. Lists are typically used for dynamic collections of items, while tuples are used for fixed collections or to represent immutable sequences.
Question: How would you read a CSV file into Python and perform basic data manipulation?
Answer: Use the panda’s library in Python to read a CSV file into a DataFrame using the pd.read_csv() function. Once loaded, you can perform basic data manipulation tasks such as filtering rows based on conditions, selecting columns, computing summary statistics with functions like describe() or groupby(), and creating new variables using vectorized operations.
Question: Explain the purpose of Matplotlib and how you would create a line plot using Matplotlib in Python.
Answer: Matplotlib is a widely used data visualization library in Python that allows users to create various types of plots, including line plots, scatter plots, and histograms. To create a line plot using Matplotlib, import the library (import matplotlib.pyplot as plt), specify the data for the x and y variables, and then use the plt.plot() function to plot the data points. Additional customization, such as adding labels, titles, and legends, can be done using Matplotlib functions.
Tableau and Looker Interview Questions
Question: What is Tableau, and how is it used in data visualization?
Answer: Tableau is a powerful data visualization tool that allows users to create interactive and insightful dashboards and reports from various data sources. It enables users to analyze data visually, uncover trends, and communicate insights effectively to stakeholders.
Question: Explain the difference between a worksheet and a dashboard in Tableau.
Answer: In Tableau, a worksheet is a single view that displays data analysis, such as a chart or graph. It represents a visualization of data from one or more data sources. A dashboard, on the other hand, is a collection of multiple worksheets and objects arranged on a single screen. Dashboards allow users to compare and analyze different aspects of data in a consolidated view.
Question: How would you create a calculated field in Tableau?
Answer: To create a calculated field in Tableau, navigate to the Data pane, right-click on the data source, and select “Create Calculated Field.” Then, enter the formula for the calculated field using Tableau’s calculation syntax, which supports mathematical operations, logical expressions, and functions. Once created, the calculated field can be used in visualizations like any other field.
Question: What are Tableau data extracts, and why are they used?
Answer: Tableau data extracts are local copies of data from a data source that are stored in Tableau’s proprietary format (.hyper or .tde). They are used to improve performance by reducing the amount of data transferred between Tableau and the data source. Extracts can also enable offline access to data and provide additional capabilities such as data blending and filtering.
Question: What is Looker, and how does it differ from traditional BI tools?
Answer: Looker is a modern business intelligence (BI) and data analytics platform that provides data exploration, visualization, and collaboration capabilities. Unlike traditional BI tools, Looker operates on a modeling layer called LookML, which abstracts complex SQL queries and allows for centralized data modeling and governance. Looker’s data-driven approach enables self-service analytics and empowers users to explore data intuitively.
Question: Explain the concept of LookML and its role in Looker.
Answer: LookML is Looker’s modeling language used to define data models and transformations. It allows users to describe the relationships between different data tables, define dimensions and measures, and create reusable business logic for analysis. LookML facilitates data governance and consistency across reports and dashboards, making it easier to maintain and scale analytics solutions.
Question: How would you create a new Look (report) in Looker?
Answer: To create a new Look in Looker, navigate to the Explore page, select the appropriate data model, and choose the fields you want to include in the report. Customize the visualization type and formatting options as needed. Once satisfied with the Look, save it to a specific folder or dashboard for easy access by other users.
Question: What are Looker dashboards, and how do they enhance data visualization and analysis?
Answer: Looker dashboards are customizable collections of Looks, visualizations, and filters that provide a consolidated view of key metrics and insights. They enable users to monitor performance, track trends, and explore data interactively in real time. Dashboards in Looker facilitate collaboration and decision-making by providing a centralized platform for sharing actionable insights across teams.
SQL Interview Questions
Question: What is SQL, and what are its main components?
Answer: SQL (Structured Query Language) is a standardized programming language used for managing relational databases. Its main components include Data Definition Language (DDL) for defining and modifying database structures, Data Manipulation Language (DML) for querying and modifying data, Data Control Language (DCL) for controlling access and permissions, and Data Query Language (DQL) for retrieving data from databases.
Question: Differentiate between SQL’s INNER JOIN and LEFT JOIN.
Answer: INNER JOIN returns only the rows where there is a match in both tables being joined, based on the specified join condition. LEFT JOIN, on the other hand, returns all rows from the left table and the matched rows from the right table. If there is no match, NULL values are returned for the columns from the right table.
Question: Explain 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 aggregate functions (such as SUM, AVG, COUNT) on the grouped data. It divides the result set into groups based on one or more columns, allowing for the analysis and summarization of data at various levels of granularity.
Question: What is a subquery in SQL, and how is it different from a join?
Answer: A subquery is a query nested within another query and is enclosed within parentheses. It is used to return data that will be used in the main query as a condition or criteria. Unlike a join, which combines columns from two or more tables based on a related column between them, a subquery operates on a single table and can be used to filter or manipulate data before it’s returned by the main query.
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, typically applied to individual rows before the grouping operation. The HAVING clause, on the other hand, is used to filter groups based on a specified condition, typically applied after the grouping operation. While the WHERE clause filters individual rows, the HAVING clause filters groups of rows based on aggregated values.
Question: What is a primary key in SQL, and why is it important?
Answer: A primary key is a unique identifier for each record in a table. It ensures that each row in a table is uniquely identified and provides a way to enforce data integrity by preventing duplicate records. Primary keys are essential for data management, indexing, and establishing relationships between tables in a relational database.
Question: Explain the difference between SQL’s UNION and UNION ALL operators.
Answer: The UNION operator is used to combine the results of two or more SELECT statements into a single result set, removing duplicate rows. The UNION ALL operator, on the other hand, combines the results of SELECT statements, including all rows from all SELECT statements, including duplicates. UNION ALL is faster than UNION but does not remove duplicate rows.
Question: What is normalization in SQL, and why is it important in database design?
Answer: Normalization is the process of organizing data in a database to reduce redundancy and dependency. It involves breaking down large tables into smaller, related tables and establishing relationships between them. Normalization ensures data integrity, reduces data redundancy, and improves database efficiency by minimizing storage space and improving query performance.
Behavioral Interview Questions
- Walk me through your CV
- Why Deliveroo?
- What is your biggest strength?
- Your experience with data analysis.
- What did you do in your previous job and why are you looking for a new job
- Why would you like to work as a Data Scientist?
- How do you handle negative reviews?
- Describe a time when you took a significant risk and it didn’t pay off.
- How do you approach a new project?
- When you’re available for a call?
Conclusion
Preparing for a data science and analytics interview at Deliveroo requires a combination of technical expertise, problem-solving abilities, and cultural alignment with the company’s values and objectives. By mastering both the technical and behavioral aspects of the interview process and showcasing your passion for data-driven innovation and customer-centricity, you’ll position yourself as a strong candidate capable of driving meaningful impact at Deliveroo. Good luck!
