Here are the coding questions that are most frequently asked in data science interviews!

Data science is an emerging field in the tech world. While interviewing for the position of a data scientist, the industry hiring experts frequently ask questions related to SQL, Python, computer science fundamentals, and also from other related domains. Hiring managers ask coding questions because data science is a highly technical field that requires collecting, cleaning, and processing data to ensure that useful insights can be drawn out of it. Coding skills can help data scientists collaborate with different stakeholders and projects and work closely with engineers to resolve complex issues. In this article, we list the top 10 coding questions frequently asked in data science interviews.

• When can you use a subquery in the WHERE clause?

Subqueries in the WHERE clause help qualify a column against rows. This is beneficial for retrieving information from different tables. For example, a subquery shows the number of departments on the third floor, whereas, the outer query recovers the names of employees who work on the third floor.

• How are data analysis libraries used in Python?

One of the many reasons why Python is a popular data science programming language is because of its extensive collection of data analysis libraries. These libraries include functions, tools, and methods for managing and analyzing data. There are also different libraries for performing other data functions like data visualization, and data mining.

• Describe JOIN

JOIN is an SQL operation performed to establish a connection between two or more database tables based on matching columns, thereby creating a relationship between tables. There are also different types of JOINs. Most complex queries in an SQL database management system involve JOIN commands.

• How is a negative index used in Python?

Negative indexes are used in Python to assess and index lists and arrays from the end, counting backward. This means that the index value of -1 gives the last element, and the index value of -2 depicts the second last element of an array. The negative starts from where the array ends.

• Are you familiar with data manipulation language?

Data manipulation refers to the process of adjusting data to make it organized and easier to read. Data manipulation language is the type of programming language that adjusts data by inserting, deleting, and modifying data in a database, such as to cleanse or map the data.

• Why do you think R is used in data visualization?

R is used in data visualization as it has several inbuilt functions and libraries that help in data visualizations. These libraries include ggplot2, leaflet, lattice, and others. R helps in exploratory data analysis as well as feature engineering. Also, customizing graphs is easier in R than in Python.

• What are the main advantages of using window functions in SQL?

The main advantage of using window functions over regular aggregate functions is that window functions do not cause rows to become grouped into a single output row. The row can retain its separate identities and, an aggregate value will be added to each row.

• Name the different built-in data types used in Python?

In Python, data types are used to classify and categorize data. The different in-built data types are Number, String, Tuple, Range, List, Set, and Dictionary.

• Discuss the decision tree algorithm.

A decision tree algorithm is a popular supervised machine learning algorithm, which is mainly used for regression and classification. It allows breaking down a dataset into smaller subsets. A decision tree algorithm can handle both categorical and numerical data.

• Explain eigenvalue and eigenvector.

Eigenvectors are for understanding linear transformations. data scientists have to calculate the eigenvectors for a covariance matrix or a correlation. Whereas, eigenvalues are the directions to use linear transformation acts by compressing, flipping, or stretching.