Whiteboard interviews can be intimidating. You’re put on the spot and asked to solve a technical problem without knowing anything about it beforehand. And if that wasn’t enough, you also have to explain your solution to your interviewer.

Still, whiteboard interviews are a crucial part of the hiring process. In the video below, Farish, a developer based in California, explains how whiteboard interviews allow you to showcase your ability to reason, explain, and solve a problem — all of which are skills you’ll need while working on a team with other developers. In the paragraphs below, we’ll provide 9 tips that’ll help you ace your whiteboard interviews. But first, we’ll explain what a whiteboard interview is.

What is a whiteboard interview?

During your whiteboard interview, also commonly referred to as a technical interview, your interviewer will present you with a coding problem and ask you to outline your solution on a whiteboard. In the video above, Farish explains how recruiters use these interviews to gauge your technical ability, communication skills, and approach to problem-solving.

The problem you’ll need to solve will likely be an algorithm, but it could also relate to system design or even one of the company’s real-world problems. You may even be asked to solve a problem that goes beyond the position’s requirements, but don’t panic. As Farish explains, your interviewer is more interested in the logic behind your solution than its accuracy.

While discussing technical and behavioral interviews in the tech industry, Christina Kopecky, a writer at CareerKarma, echoes a similar sentiment:

“Remember that whiteboarding is not about coming up with the best answer, but about talking through your approach to get to a workable solution. It probably will be a brute-force or naive solution, and that’s okay.”

9 tips on how to ace your whiteboard interview

To help you prepare for your upcoming whiteboard interviews, we’ve compiled a list of tips that’ll help you showcase your technical, communicative, and problem-solving skills. Not only will they make for a smoother interview, but they’ll also help put your mind at ease. When you’re relaxed, nervousness is less likely to get in the way of clear thinking and communication.

1. Practice whiteboarding

Interviews can be difficult. It’s hard enough to be in the spotlight, even without the added pressure of trying to impress your interviewer. To help you prepare, Farish recommends practicing whiteboarding in the days leading up to your interview.

This doesn’t mean that you need to go out and find complex problems and practice solving them. Instead, take the time to walk yourself through some of your daily activities as you complete them.

Practicing whiteboarding can help you become a better communicator. It’ll also help you get into the practice of explaining your thought processes and identifying any missing steps.

2. Prepare for the interview setting

You may interview in person, but the chance is just as high that you’ll do it over a videoconferencing platform like Zoom. Here are some tips for how to prepare for a remote whiteboard interview:

• Ask the interviewer which conferencing app you’ll be using and make sure you have downloaded it and are comfortable with its features.
• Choose a quiet, relaxed setting without distractions like your bedroom, an office, or even a side room reserved at a local library. A little background noise is unavoidable, but you want your interviewer to be able to hear you clearly.
• Get a notepad ready to take notes during the interview. You may want to write down things such as:
• The interviewer’s name
• Questions that come up during the interview that you may want to ask after the interview

3. Clarify the problem

Before starting your solution, Farish recommends taking the time to discuss the problem with your interviewer. Repeat the question aloud in your own words, and ask questions that clarify any assumptions you have about the solution.

Some candidates avoid asking questions because they fear it’ll negatively impact their chances of landing the job, but this isn’t the case. Your interviewer will be glad to see that you prefer to fully understand the problem before tackling a solution. Plus, you’ll also give yourself more time to think of an answer.

4. Define your inputs, outputs, and edge cases

After you’ve clarified the problem, it’s time to start finding a solution. Write the inputs or functions that’ll lead you to a solution, and explain what outputs you expect them to produce. You’ll also want to address any edge cases (uncommon or unexpected inputs that could break your functions) as you find them.

In the video above, Farish shares an example. Imagine that your task involved merging two sorted arrays to a new sorted output array. In this case, your edge cases would be duplicate numbers in the two input arrays.

5. Outline your solution

Outlining your solution with bits of pseudocode gives you the opportunity to communicate your thought process to your interviewer. Plus, your interviewer will be more likely to provide tips or hints if you’re already engaged in conversation. Again, ask questions if you find yourself stuck or confused.

6. Know what your acronyms mean

Programming languages, terms, and their abbreviations may feel like a complex alphabet soup sometimes, but you need to know what they mean.

For example, REST stands for Representational State Transfer. You’re most likely used to simply saying “REST” in conversations with other developers. You don’t want to be caught off-guard when your interviewer asks you to expound on the implications of “state transfer,” so understanding the acronyms and their definitions is a big help.

7. Write down and talk through your approach to the solution

Once you have an idea of how to solve the problem, write it out in a way that’s easily understood, even to someone who doesn’t know the problem itself. This is beneficial because your interviewer may take a screenshot or picture of your approach to share it with someone else.

It also helps to explain your approach aloud — even if that means saying exactly what you’re writing down. This helps you:

• Show your presentation skills
• Get your thoughts together as you begin your answer
• Catch any errors that you may not recognize until you hear them said out loud

Remember the power of communication in a whiteboard interview. Writing down and talking through your thoughts allows the interviewer to check off a very important box: You can communicate. This may be more important than how well you’ve memorized the syntaxes of the languages in which you code.

Once you’ve finished explaining your approach, Farish recommends discussing your solution’s time complexity or Big O Notation. In another post, we take a closer look at why you should explain your approach during whiteboard interviews.

8. Don’t get flustered if you forget an element of syntax

If you can’t remember exactly how to write a command, it’s better to acknowledge that and include a symbol or other annotation in its place. This shows that you know what the code can do and how it works in the context of your solution. In this way, your knowledge of logic and problem-solving can still shine through.

9. Don’t say “I don’t know”

Taking extra time to think of a practical approach to a question is infinitely better than saying “I don’t know.” Also, if you’re asked a factual question you don’t know the answer to, it would be better to say “I can’t recall that off the top of my head,” because “I don’t know” sounds like you’re giving up. “I can’t recall,” tells them that if you had your notes from class or could Google it, you’d know the answer in a few seconds.

Preparing for your whiteboard interview

With the tips listed above, you’ll be better able to accurately showcase your technical, communicative, and problem-solving ability during your next whiteboard interview. Still, some whiteboard interviews are designed to test your proficiency with specific programming languages. To help you prepare for those, check out any of the courses below:

Data science is a growing field with a booming job market. Every day, companies look for new ways to use their data, so the need for data professionals has never been greater.

Both Data Scientists and Data Engineers rank highly in LinkedIn’s list of the top 15 emerging jobs in the U.S. But what’s the difference between the two? Because of data science’s wide range of applications and the nebulous responsibilities and titles of data professionals that vary between companies, the distinction can be hard to discern.

To help you understand the difference (and clarify your potential career path), we’ll explore both data science and data engineering in the paragraphs below. Then, we’ll show you how to break into these emergent fields.

What is a Data Engineer?

Without Data Engineers, a Data Scientist’s job would be much harder. Data Engineers work in the background designing the databases and data stores that hold a business’s data cache. They also build the pipelines that transform this data into formats that are more useful for Data Scientists.

Data Engineers often deal with raw data that comes from analytics and tracking tools, IoT devices that output sensor data, sales data from e-commerce sites, and more. This data could have errors, misconfigured data points, and information that only applies to the data systems. There could also be a lot of it to deal with, and the data doesn’t stop coming in most industries.

It’s up to a Data Engineer to design and create an architecture that supports retrieving the data from all these sources and storing it in an easy-to-use format. To do so, they need to be skilled with databases, programming languages like SQL, ETL (Extract, Transform, Load) tools, and other data processing tools.

This job can be complex because it’s not as simple as moving the data around. Errors and misconfigured data must be either removed or fixed. Sometimes system-specific codes in the data have to be looked up in another system to make sense in the final dataset. Or one dataset may have to be merged with another. Finally, the results can be delivered to Data Scientists or Data Analysts who use it to provide business insights.

What is a Data Scientist?

Data Scientists wrangle big data. They collect and analyze large sets of both structured and unstructured data. Most come from a variety of backgrounds since the skills needed to become a Data Scientist go beyond programming or computer science skills. A Data Scientist must have technical skills, but they must also know both mathematics and statistics.

It’s a Data Scientist’s job to discover the questions they need to ask to make a business grow. For example, what type of revenue increase would a company have if they added a new product line? After asking this, they would look at the data they have and see if they can pull the answer from it. If the data isn’t available, they may work with a Data Engineer to set up a pipeline to retrieve it.

Once a Data Scientist has their data, they prepare it so it can be used to create predictive and prescriptive machine learning models. To do this, they may have to transform and clean the data even more, and they also may have to research their industry further to determine which machine learning models and methods will work the best for the information they’re trying to generate.

Once Data Scientists have gathered the insights they need, they will have to turn these insights into a story they can present to stakeholders. Once those results are accepted, they have to automate the process they used to generate and deliver reports to these stakeholders regularly.

What’s the difference between data science and data engineering?

Now that you know what both a Data Scientist and Data Engineer do daily, it is easier to see the difference between the two disciplines. The key differences are:

• Data Engineers collect, move, and transform data into pipelines for Data Scientists, while Data Scientists prepare this data for machine learning and use it to create machine learning models.
• The final result of a data engineering process is data that is easy to use and process, while the final results of data science are reports and insights that are presented to business stakeholders.
• Data Engineers use programming languages to move, transform, and clean data, while Data Scientists use programming languages to create machine learning models.

While we draw a line between data engineering and data science in this article, this line is usually blurry in the real world. So whichever way you chose to go, it doesn’t hurt to know both disciplines.

Getting started with data science and data engineering

Data is the new gold, especially in the business world. Because of this, choosing either data science or data engineering as a career path means you will be in demand in the job market. After going over the details of each job, you should have a better idea of which job will be the most rewarding for you.

If you’re leaning more towards the Data Scientist role, then our Data Scientist Career Path is for you. It’s a beginner-friendly course that will teach you how to become a data-driven decision-maker.

But that’s not all we have for future Data Scientists. Building a Machine Learning Model with Python will introduce you to machine learning, another tool in the Data Scientist toolbox. For even more, check out our data science course catalog.

If you’re interested in data engineering, you’ll need to Learn SQL so you can query databases effectively. After that, Learn Python to start building pipelines for your data and create your own databases from scratch with our Design Databases with PostgreSQL Skill Path.

Whichever you choose, we wish you luck on your journey into the world of data.

While pursuing a career as a Data Engineer, one of the biggest hurdles you’ll face is the interview process. You can think of an interview as a verbal skills test in which your interviewer asks questions about your technical knowledge and problem-solving ability.

To do well in your Data Engineer interview, you’ll need to be well-prepared — and we’re here to help. Below, we’ll explore some of the skills and knowledge you’ll need to excel in your new career and some of the most commonly asked Data Engineer interview questions.

Data Engineer skills

Before applying to your new job as a Data Engineer, you’ll need to acquire key skills. The good news is that you don’t need to undertake a traditional degree program. Instead, you can take online courses related to data engineering or data science to fill the gaps in your knowledge.

To become a Data Engineer, you’ll need to be familiar with certain programming languages. Most Data Engineers will have a good grasp of Python. Depending on their specific role, they might also have experience with SQL (to access databases), R, or other languages. Frameworks like Vue.js or Flask are also a plus.

After you’ve learned the technologies and skills you’ll need for the role, you’ll want to practice. Even experienced programmers will often participate in larger open-source coding projects to stay current, learn new skills, and improve their ability to work with someone else’s code.

You can also practice your skills by undertaking projects that’ll help you gain practical experience and brush up on the skills you’ll need to discuss during your interview. If you need help finding data-driven project ideas, check out our Data Scientist Career Path.

Technical Data Engineer interview questions

As we explained earlier, many of your interviewers’ questions will be designed to assess your proficiency with the technical knowledge and skills required for data engineering. You might even be asked a question you weren’t expecting, but it’s important that you don’t panic or guess. Instead, explain that you don’t know the answer and outline how you’d approach finding a solution.

To help you prepare for your upcoming interviews, we’ve compiled a list of some of the most common technical Data Engineer interview questions:

• Do you have any experience with ETL frameworks?
• Have you designed a data system using Hadoop?
• Can you describe what Hadoop frameworks are and how to use them?
• What’s the difference between a NoSQL database and a relational database?
• Can you define data modeling?
• What type of data is stored in NameNode?
• What functions are a part of Secondary NameNode?
• What command would you use to view the structure of the database in MySQL?
• How would you use MySQL to search for a string in a particular column?

Scan through the job posting you applied to and see if you can narrow down the questions you’ll use to practice. For example, if a job uses MySQL but doesn’t mention anything about Hadoop, you can skip the Hadoop questions for now.

Conceptual Data Engineer interview questions

Along with the technical questions above, you’ll probably be asked about more general concepts and ideas in your interview. These questions help your interviewer see how good you are at communicating and how well you can work with a team.

The answers to the conceptual questions likely won’t require as much memorization or review as the technical questions. Instead, you can practice these once or twice to make sure you have an appropriate example, process, or fact.

Here are some of the conceptual questions you might be asked in your Data Engineer interview:

• How would you define data engineering?
• Have you ever used an existing database for a new and exciting purpose?
• What programming languages are you comfortable using?
• Can you name three skills you think are most important for a Data Engineer and why?
• In your new job as a Data Engineer, where would you start when developing a new product?
• What qualities do you think make a good Data Engineer?
• Can you tell us about a time that you used your data engineering skills to solve a problem?
• How would you bring value to our organization?

Preparing for your Data Engineer interview

Don’t try to cram for your interview the night before. You’ll want to have a few days (at least) to review questions, practice your answers and even brush up on your coding skills. If you know any Data Engineers, ask them to help you rehearse.

You should also spend a little time on the company’s website and do a couple of Google searches to see where they might have appeared in the news recently. You’ll want to know as much as you can about the company so that you can tie your skills back to their products, services, and clients when they ask you questions.

We’ve said it before, but we’ll say it again — keep practicing. Many interviews involve in-depth questions about specific programming languages as hiring managers need to know you can utilize their tech stacks. To help you prepare, we’ve created a course designed to help you pass the technical interview with Python. Once you finish that, explore our other programming courses to fill in any gaps in your knowledge.

Product Managers play a central role in bringing solutions to life by managing the successful development and launch of a product. They often serve as a liaison between the company’s goals as outlined by executives and the engineers who use those goals to create effective products.

In other words, a Product Manager plays a pivotal role that includes:

• Maintaining a high level of focus
• Stakeholder management
• Product success

While a Product Manager may not get involved in specific coding decisions, they maintain a high-level perspective and encourage developers to move the product in the right direction.

In the video below, Pat DePuydt, a Web Developer from Washington D.C., takes a closer look at a Product Manager’s role. Read on (or watch the video) to learn more about their responsibilities and required skills.

Product Manager skills

To help you better understand a Product Manager’s duties, Pat provides an example:

Imagine a DevOps team tasked with building a web app users can use to book appointments. The team’s Product Manager will need to engage with various stakeholders (e.g., devs, executives, etc.) throughout the ideation, creation, and testing of the product. For example, they may be in charge of ensuring:

• The app has a user interface that suits the needs of end-users from a functional perspective.
• The app’s color scheme and layout are aesthetically pleasing for end-users.
• The appointments users book with the app properly interface with an internal database, an API, or a customer relationship management (CRM) system.
• The app functions as designed, meeting the expectations of all stakeholders.

To fulfill the responsibilities outlined above, a Product Manager needs to have a balance of technical, interpersonal, and conceptual skills to guide their team in the right direction. Let’s take a closer look at these skills and how they manifest in a Product Manager’s day-to-day.

Maintaining a focus on high-level objectives

Keeping high-level objectives in mind is essential to ensure the end product is both effective and aligned with the organization’s goals. An app can work very well and even be appreciated by end-users, but it still wouldn’t get the job done if it didn’t help advance organizational objectives.

Say a company’s executives decided to create an application that facilitated faster online ordering for a restaurant’s takeout menu. There are many factors the Product Manager would have to keep in mind throughout the app’s development, including:

• Ease of use by customers
• An accurate selection of menu items
• Convenient payment options
• Delivery options
• Extra elements that may make the user experience more enjoyable, such as the ability to leave notes for the driver or enticing images of menu items

But, if the DevOps team started to get too heavily involved in making the menu as comprehensive as possible, the objective of designing “faster” online ordering may be compromised. The Product Manager would have to recognize this pitfall, identify why it’s happening, discuss possible solutions with their team, and report on this hiccup in the process to C-level executives.

Making ideas come to life

In the video above, Pat goes on to explain how a Product Manager needs to be able to take an idea and envision the technologies, systems, and procedures required to make it happen. This requires discernment. A Product Manager shouldn’t agree to take on the development of every single project without carefully considering its feasibility.

This requires an in-depth understanding of the capabilities, processes, and individual skill sets of those on the DevOps team. It also necessitates a deep familiarity with the technologies available to the team and a general understanding of how they work and what they can do.

An adequate depth of understanding requires a degree of technical fluency. While discussing a Project Manager’s role in our forums, Richie W., one of our Senior Product Managers, explains how they need to understand:

• How APIs work
• How microservices work
• Systems design
• The difference between client vs. server
• Databases
• Tech debt

Richie goes on to explain how Product Managers need to be familiar with SQL because “a large part of the job is understanding data and having the ability to query on your own.” Check out our Learn SQL course if you want to learn how to use the programming language to query databases and manipulate data.

Lastly, if you’re looking to become a Product Manager in the tech industry, Richie suggests building a few apps yourself:

“The best way to understand technology is to build something simple. I’d recommend you go through Codecademy’s web development path, for instance, and work through one of the web projects.”

Learning how to build an app will give you a better understanding of your team’s dependencies and processes. Take your first steps into web development by learning how to build a website.

Guiding the development process

A Product Manager has to make decisions that improve the quality of the end product and the speed with which it gets developed, ideally without significantly sacrificing one for the other. This requires them to understand how long different processes take to complete. They also need to understand each stage of the development process, including how long they’ll take.

Then, as the process unfolds, they need to check in with developers to see if things are shaping up the way they should. At times, this may involve connecting with the team lead to see if there are any roadblocks the team needs to huddle around to overcome.

Ensuring a minimum viable product

A minimum viable product (MVP) is a product that consists of only its most essential features, without any bells or whistles. Basically, a Product Manager needs to ensure that a product does what it’s supposed to by the project’s deadline. There are always additional features that can help polish a product, but they’re not always worth the time they’d take to develop. Plus, as Pat explains, if a product’s core utility doesn’t resonate with its audience, no amount of polishing will make it a success.

Measuring success

Another critical element of guiding the development process is understanding how the target market will receive the product. This involves collecting data on both the market and the product’s technical performance.

One of the best opportunities to gather data about a product is when it is tested and fails. Failures often yield insights that are more helpful than those derived from successful test runs.

To pull insights from tests and measure the success of a product, a Product Manager needs to know:

• Which data sets to gather
• How to get the appropriate data during the testing phase
• How to present the data to the DevOps team
• How to use the data to improve the next iteration of the design

Factoring in competition

A Product Manager must also consider the competition while plotting the course of a project. The objective is to keep an eye out for products that can accomplish the same thing yours does but better, faster, or cheaper. The Product Manager then has to make sure their product can compete with their competitors.

How to become a Product Manager

In sum, Product Managers play a critical role in the development process — serving as a guide, coach, source of ideas, and more. Still, if you’re considering a career as a Product Manager in the tech industry, you’ll need a degree of technical knowledge and skills to properly manage your DevOps teams.

To start building the technical skills you’ll need in your career as a Product Manager, check out our Code Foundations Skill Path. We’ll walk you through the basics of computer science and programming as you learn how to code with popular languages like Python and JavaScript. After that, continue building your skills with any of our programming courses and tutorials.