HackerRank for Data Science
Let's find out if HackerRank coding challenges and their practice questions can help you prepare for your data science interview.
HackerRank projects, coding challenges, and practice questions for data science give you the ability to prepare for your next data science interview using either SQL or python. In return, your information and results are sent to hiring teams to identify and assess top data scientists. The data science challenges or questions on HackerRank assess data science skills such as data wrangling, building models, data visualization, and machine learning.
Solving HackerRank Data Science Coding Challenges
HackerRank coding challenges provide developers with an embedded Jupyter development environment and you can solve the data science problems inside the Jupyter IDE.
These coding challenges or tests might help in assessing strong data scientists. These challenges can help prepare you for your next data science interview. So, let’s take a look at these coding tests.
How to prepare for your next data science interview using HackerRank coding tests?
HackerRank's data science coding challenges encourage a particular style of programming expertise that is competitive programming. This is a type of programming where you are given a challenge (or a set of challenges) through questions and your time and accuracy are measured and posted on the leaderboard. This does not always translate into data science skills, especially the skills needed for interviews. However, doing these challenges is useful nonetheless. If you focus on being able to accurately complete the challenges, you should have no problem completing similar tasks in a typical work setting, and that is what interviewers will recognize. Furthermore, since you have access to the leaderboard at all times, you can use this to your advantage. All users with scores on the leaderboard have a direct link to their profile through their published username on the board. You can follow and/or message any user who has successfully completed the challenge you are struggling with and they might be willing to help out. Another reason to use this feature through the leaderboard is to expand your network of coding experts and gain valuable insights on programming.
Utilizing the discussions tab on HackerRank coding challenges is another valuable tool to use in preparation for your interview. If you are struggling with a particular question, you can always explain your problem on the discussions tab and chances are one of your peers will help you find the solution. Furthermore, you can read through discussions on any challenge (whether you think it’s easy or not) and just look at problems others are struggling with and other ways to approach the solution that your peers have used. A lot of times you are given a hypothetical problem on an interview and you need to explain your approach to the solution and reading through examples of that will help your versatility.
HackerRank supports assessing skills such as data wrangling, visualization, modeling, and machine learning. There are pre-built Data Science library questions on HackerRank to choose from and candidates can use an integrated Jupyter IDE to solve the problem. These questions are usually split into performing basic functions separately and building up on what you have previously learned. The Data Science library has several different modules and you can use the library to build both your coding skills by utilizing several languages or build your problem-solving skills through modules such as mathematics or statistics. It makes sense to use HackerRank to practice but the questions asked don’t always translate to what hiring managers would ask in interviews, so you’re left with a gap to fill.
Your skill set assessment depends on the profile you are looking for. With several languages and skill-boosting packages available for practice and testing, it is important to know which skills you would like to build up on. These skills could be data analysis, experimentation, machine learning methods, data visualization, and many more. For example, if you want to improve your skills for data science related jobs, you can go through separate packages on Python or R, Databases, Statistics and Artificial Intelligence. Therefore, using the skill assessment tools like HackerRank might be a good idea.
Although very useful for building up your skills from scratch and/or with little knowledge on the topic, when it comes to practicing and solving more difficult problems, you might find the platform to be insufficient. While HackerRank could help you assess the Python programming skills of a data science candidate, when it comes to complex data manipulation and algorithms, HackerRank is far behind the platforms like Kaggle. This is partly due to the way HackerRank challenges are structured where there is a time consideration on top of accuracy, as opposed to Kaggle where challenges are more complex, but only the prediction accuracy matters.
HackerRank Testing Environment
The best thing about HackerRank is that candidates can attempt a sample test on the platform before attempting the hiring company's actual Test. This will allow you to get familiar with their coding environment. But some people see this as an issue that you can write your code only in their environment, in a preselected language. Then it will test your code against use cases that you can't see. It is also correct that you don't have any way to debug your code. People find these tests a little annoying and unnecessarily time-consuming.
HackerRank provides an artificial environment to developers as well as data scientists. When you perform any HackerRank coding challenge you may be forced to use an unfamiliar version of Python with no standard modules. Working with pandas, for example, is a must-have when coding in python, especially for a data scientist.
What HackerRank Lacks in Comparison to Other Alternatives
Multiple Programming Languages
Most questions on HackerRank do not support both SQL and Python programming languages. SQL is almost always useful and required for interviews. Usually, data scientists have an option to use python as well, especially for any machine learning type of questions. So a platform you're testing your data science skills on should allow you to solve data science problems in both SQL and Python coding languages. There are both python and SQL questions, but they’re usually for practicing syntax and mastering concepts of the language. Data science interviews also test for your ability to manipulate and wrangle the data to generate insights. So it’s not enough to join or merge two tables together -- you should also be able to aggregate the data and answer real-world scenarios from different companies.
The focus for HackerRank questions is on mastery of the syntax
This is where you can talk to others and get your questions answered. Other platforms like Leetcode and StrataScratch also have discussion forums that are valuable for all users of any level. StrataScratch allows you to talk to one of their team members to get professional help and advice. Leetcode allows you to use any of their scripting languages to submit solutions and talk about the solution for that particular scripting language.
Sometimes problem statements can be unclear and frustrate the user
Check out our post on LeetCode vs HackerRank vs StrataScratch for Data Science where we compared these three platforms.
Clear Problem Statements
HackerRank wraps its coding challenges in stories and makes the question less clear. Most of the time, companies like FAANG tend to ask their questions fairly directly. Other platforms like StrataScratch offer a closer proxy for that.
What makes data science coding questions difficult and more complex is that while the solution to the problem might not be complex, the questions typically are more wordy and involved than software developer interview questions. This is because there are many edge cases and real-world scenarios to consider when working with the data that are not always present in software developer questions. So companies like StrataScratch spend a lot of time curating their questions and answers to make sure users are not confused and that the solutions include edge cases.
Is HackerRank designed to prepare you for data science interviews?
HackerRank is designed for software engineers and no doubt it's a good tool for them to use in preparation for their technical interviews. However, when it comes to the field of data science, the platform is lacking some components. HackerRank wasn't designed to help data scientists prepare for their interviews or to improve their analytical skills. It's a technical hiring platform that helps businesses and companies evaluate software developers based on their skillset.
In order to go with the times, HackerRank is adding a component for data science on their platform. However, as you can see from the content the platform provides and the way challenges work, it is clear that much of the content is still aimed towards software developers. Even though there is a lot of overlap between the skillset of a software developer and the skillset of a data scientist, there are some very unique traits that a data scientist should have that software developers do not need.
As a data scientist, you require cross-disciplinary skills, so businesses need to find candidates beyond those traditional software development backgrounds. This is why we mentioned earlier that doing HackerRank challenges might be useful to build up your skills, but they will not necessarily help you land a job in data science.
In conclusion, HackerRank will help you build a lot of the necessary programming skills that you will utilize in your day-to-day activities, but when it comes to conducting interviews strictly from a data science standpoint, there are other alternatives that can help you better in landing that dream job.
Alternative Resources to HackerRank: A Resource Specifically Designed for Data Scientists
There are definitely better ways to test your data science skills. One of the best alternative ways that were built specifically for preparing data science interviews is StrataScratch. StrataScratch was built to offer you data science interview questions that test not only your technical implementation of SQL and Python but also test data science concepts such as implementing edge cases, manipulating the data, and creating metrics. As a data scientist, you are required to understand how to manipulate the data as well as how to implement solutions that compensate for edge cases and real-life scenarios.
Features that can help you with your data science interview
1. Real Data Science Questions from Real Companies
StrataScratch provides questions that would reflect concepts covered in data science rather than software engineering. There are over 500 data science questions for you to practice. The best thing about all of this is that StrataScratch sources these questions from real companies to give you the real interview environment.
2. Video Solution
StrataScratch provides modern solutions as well -- video solutions. It has over 50 video solutions on the platform to help in preparing data science interviews.
3. Ability to Solve Problems in SQL, Python, and R
You don't need to code in a pre-selected language. It allows you to solve data science problems in SQL, Python, and R languages. This platform also provides solutions in SQL and Python coding languages for each data science question.
4. Learn Alternative Approaches from Other User’s Solutions
You have the ability to explore other user's solutions. This allows you to learn alternative approaches to solve challenges. It gives you the opportunity to discuss and ask the StrataScratch community about any data science question and about your code.
HackerRank is a good tool, but their coding challenges are not enough to get you the data science job. Sure, you can test your coding skills there but can't get the testing environment you require to be a successful data scientist. If you want to get a data science job at Google, or Facebook, or any of the other big tech firms, you need a lot more than what HackerRank provides.
HackerRank is a great tool to help software engineers but this platform is not focused on the data scientists and their needs. On the other hand, StrataScratch can be the best alternative to HackerRank that is specifically designed for data scientists and their needs. HackerRank might help you to improve your programming skills but if your goal is to land a data science job, we recommend platforms like StrataScratch, a platform that shows how to get a data science job designed by an ex-FAANG data scientist.