# Data Science Online Coding Tests

Create and send data science coding tests in 60 seconds, even if you’re not techy.
Hand over qualified data science candidates for your engineering team to interview.

## About our data science coding tests

We offer your team data science coding tests that are both realistic and practical, providing an accurate evaluation of candidates’ data science skills. With tools like CoderPad Screen, you can create data science coding tests in just one minute, even if you don’t have a technical background.

Our tests are specifically designed to encompass crucial concepts such as object-oriented programming, data structures, algorithms, and more. They are carefully curated by our industry experts, ensuring their relevance to real-world scenarios and their effectiveness in assessing candidates’ abilities.

For hiring managers who want to delve deeper, there is an option to customize the tests and include their own coding exercises.

• Recommended duration: 30-77 min
• Average number of questions: 24
• Type of exercises: Multiple choice, coding exercises
• Test levels: Junior, Senior, Expert

## Data science sample questions

### Example question 1: Basic data science comprehension

Which of the following matrices will leave an image unchanged after a convolution operation

### Example question 2: Fix the algorithm

The cosine similarity and euclidean distance functions are incorrect. Please fix the body of the two functions `cosine()` and `euclidean()`:

``````# Use print(var) or cat("Messages...") to debug your solution.

# Helper functions
norm <- function(x) sqrt(sum(x^2))
dot <- function(x, y) sum(x*y)

cosine <- function(vector1, vector2) {
# Fix me
norm(vector1) * norm(vector2) / dot(vector1, vector2)

}

euclidean <- function(vector1, vector2) {
# Fix me
sqrt(norm(vector1 - vector2))

}```Code language: R (r)```

## Explore Code Playback

Obtain a deeper understanding of the problem solving thought process of your developer applicants.

• Witness their proficiency in building algorithms and evaluate their approach to solving problems with them.
• Jot down feedback from different segments of the interview to discuss in subsequent interview rounds.
• Pay attention to detect and prevent any distasteful actions.

## How to test data science skills to hire for your team?

Relying only on resumes may not accurately paint a broad picture of a data science developer’s skills and abilities, and self-reported skills may be untrustworthy.

Here are five ways to assess JavaScript developers’ coding skills:

• Review their work portfolio. This gives you a deeper insight into the types of data science projects they have worked on and their level of proficiency in the language.
• Explore their GitHub account to evaluate their Python and R code quality, projects, and involvement in data science-related projects.
• Ask about their use of Stack Overflow or data science communities in their day-to-day development to understand their knowledge level and participation in the data science development community.
• Employ the use of practical Python and R focused programming exercises for Python and R developers to allow them assess and improve their coding skills respectively.
• Conduct live coding interviews focused on Python and R where candidates can demonstrate their coding skills, problem-solving abilities, and knowledge of Python and R concepts respectively.

💡 To dig deeper > 5 ways to test developers’ skills before hiring

## How to approach initial candidate screening effectively?

Some concerns recruiters share about conducting coding tests on candidates early on in the interview process is that it can scare potential candidates and discourage applications. However, proper management of the situation such as clear outlining of the interview process and why it is important will enable the candidates understand the purpose and feel at ease.

Here are some guidelines for you to ensure a positive candidate experience during early testing:

1. Job Ad: Ensure that the advertisement contains every step and expectation involved in the hiring process. By doing so, the candidate does not feel singled out and can prepare properly for the interview. It is also advisable to communicate time constraints involved in technical interviews beforehand.
1. Highlight Equal Opportunities and Diversity: Emphasize your organization’s stand on providing equal opportunities and promoting diversity through the testing process. It should be clear that the main goal of the testing is to properly assess the candidate skills irrespective of background.
1. Concise Assessment: Choose quality over quantity by keeping the assessment brief and focused on the skills required for the job. This is to eliminate any time wasting and ensure the candidates can attempt the exercise hitch-free. Ideally, aim for a test that can be completed within an hour or less without compromising any requirements.
1. Relevance: Eliminate redundancies by ensuring the tests oncentrate solely on the skills that are directly applicable to the role. Demonstrating a pragmatic approach by focusing on skills that have a practical impact on day-to-day tasks is appreciated by developers.
2. Feedback and Results: At the end of testing rounds, provide candidates with feedback on their performance. Using a tool like CoderPad Screen, which automatically generates a brief report and sends it to candidates, you can enhance their experience. This way, candidates who underperform will understand the reasons they weren’t selected for the next stage.

We had no unified approach or tool to assess technical ability. Today, CoderPad Screen enables us to test against a wide selection of languages and is continually updated.

CoderPad code interview all