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Preparation for interviews related to Data Science and machine learning can sometimes be hard and excruciating if you don’t know what exactly do you need for acing the interview process. Out of the billions of possible questions, you are always wondering what topic you might be quizzed on or what knowledge is expected from you by the interviewer.
Having participated and successfully cracked interviews from about five different companies has made me realize some of the good and relevant tips to succeed in these interviews as well as the shortcomings in my approach to improvise. I would love to share this experience and tips with all of you.
If you are trying to prepare every single aspect of Data Science, especially within a limited time duration, it might be a tough chore for you to successfully complete. Hence, you will need to pace yourself and plan accordingly.
The article provided below is a stepwise detailed illustration that will guide new Data Science aspirants and enthusiasts towards mastering the skills required to become a pro in Data Science in twelve months. Most of the topics and essential skills can be covered during this time frame.
In this article, our primary objective will be to focus on both the technical and practical aspects that are essential for an individual to crack a Data Science or machine learning interview. We will analyze the seven tips in detail to understand the best approaches for an interview session. While soft skills like effective communication, appropriate dressing, and other factors are essential too, these are more general terms and will not be covered, in this article.
Assuming that you have a decent amount of knowledge in solving problems related to Data Science and you have a basic understanding of all the essential topics in Data Science and machine learning, let us proceed to dive into your article. We will analyze the seven most essential tips to crack Data Science and machine learning interviews.
1. Hone Your Basics
Sharpening your basics and initial skills is extremely significant. Before you even move ahead to the actual preparation for the interview process, it is essential for you to hone your basics and master every elemental Data Science topic that you have come across. To be more specific on these elemental concepts, let us dwell on each of these aspects individually.
While working with Data Science, note some of the essential concepts that you will need to analyze to get you started with the overall interview procedure. Concepts of data, data mining, data visualizations, key concepts on specific libraries to help you in dealing with a wide array of projects and questions.
While working with mathematics for Data Science interview preparations, usually not a lot of mathematical terms or concepts are asked during the earlier stages of the interview. However, this fact also depends on the companies that choose to interview you and their specific requirements. A solid foundation of some of the crucial aspects of linear algebra, probability and statistics, dimensionality reductions, and concepts on activation functions and optimizers could be touched upon.
Moving on to coding, usually, most companies will allow you to choose any specific programming language that you are comfortable using. The languages you should mostly focus on for Data Science and machine learning are Python and SQL. While R is practiced in solving a lot of Data Science related tasks, I would highly recommend working on improving your Python and SQL skills initially. Python is the simplest and widely utilized language for solving a wide array of complex tasks related to Data Science and machine learning. SQL, on the other hand, finds its utility to construct large databases, which is a useful requirement for solving complicated tasks in Data Science.
While working on developing a code block to solve a particular problem, it is essential to make sure your approach to solving these questions is most relevant and appropriate towards the particular problem. During the first round of the interview process, you usually have a coding round of interviews. The article provided is an example of one such type of particular question that is asked during these interviews. Check out the following post to learn the solutions to some common types of pattern programming.
Finally, we will touch upon machine learning which is a crucial aspect of most interviews. In machine learning, try to touch and cover the essential topics of machine learning algorithms like decision trees, random forests, K-nearest neighbors (KNN), linear and logistic regression, clustering algorithms, and other signature algorithms. Make sure to touch upon the most useful python libraries that are required to solve complex machine learning tasks. These libraries include pandas, matplotlib, scikit-learn, TensorFlow, and other crucial libraries. Try to focus on an intuitive understanding and approach towards achieving the machine learning tasks and objectives for a higher rate of success.
2. Be Confident
“When you have a lot of confidence and you feel like nobody can beat you, it’s game over for everyone else.”
– Jason Day
Confidence is an integral part of any interview procedure that you choose to take up. So, what is so special about having confidence for a Data Science interview?
While confidence is an essential factor for almost any interview, whether it is Data Science or not, it is undeniable that you are confidence must be at an all- time peak to achieve successful results in a Data Science interview. The amount of complications and the pure vastness of the field in the subject of Data Science requires an enthusiast to deeply explore all these essential skills in detail.
Every task you perform in Data Science will require some amount of confidence. Failure is one of the most significant aspects of Data Science. While working on a multitude of problems, you will encounter many situations where you will feel blank and have no clue on what must be the required approach to achieve the particular task. Similarly, when you are asked a question in an interview, you might black out even with your knowledge of the answer. It is important to stay calm and remain confident under these circumstances. It will ultimately help in cracking these complex interviews. Check out one of my previous articles on the five most essential qualities of successful data scientists from the link provided below.
While confidence is essential, overconfidence can be a big problem. The key issues occur when you choose to not recollect and revisit the significant topics of Data Science before your interview preparation, and you assume you know everything. Or you just have a quick glance at all the topics without much focus. This factor can be extremely detrimental to your overall selection process. Hence, make it a point to stay confident during the interview process, but also make sure that you are well prepared and you have the right tools to conquer the interview process to achieve the best results.
3. Own Up To Your Resume
Data Science is one of those fields that does not compulsorily require you to have a degree (Although it is helpful for getting interviews in some companies). The best part about Data Science is that you can have your resume or portfolio speak for the accomplishments that you have managed to achieve during the time you have spent learning this subject.
One of the most significant aspects of Data Science is the quality of the projects that you have worked on during the duration of your study in the particular field. It is essential for every enthusiast of Data Science to continue working on numerous projects whenever they have the chance to do so. Apart from gain practical knowledge and theoretical understanding of the projects, you are also able to accomplish the objective of adding these skillful projects to your list of achievements in your resume and portfolio.
I would highly recommend checking out the following article link provided below. The list covers fifteen awesome Python and Data Science projects for 2021 and beyond. This guide should be useful for most beginners and Data Science enthusiasts to dwell more deeply into the wide array of projects that they can work on and add to their resume. I have divided the article into numerous sections covering the easy, intermediate, and advanced projects for users of all levels. Feel free to check it out to add more amazing projects to your resume and portfolio.
Ensure that you work and research extensively in the projects that you choose to add to your resume. You should have the ultimate and absolute knowledge of the particular project that you have showcased in your resume. Don’t add them just for the sake of adding more elements to your portfolio. You will be quizzed in detail about these topics, and failing to answer the questions related to them will be detrimental to your selection process. Hence, be wise and work on the projects with dedication before you add them to your resume.
4. Show Your Passion
Show that you are passionate about Data Science. Show your love for Data Science. With your newly fueled passion, you can achieve anything that the field of Data Science has to offer. Being passionate about Data Science is vital for achieving the ultimate success in this field.
But how can you showcase your passion for the subject in the limited time you have in your interviews?
While the primary objective must be to stay on point with answering the particular questions asked, you can always utilize this opportunity to show the amount of effort you have put into accomplishing the best results on a particular project that you have worked on effectively. The depth of your answer while explaining the particular project on your resume will show the interviewer the passion and dedication you have put in learning and understanding the core concepts.
You can also show your passion in your HR interview rounds when asked specific questions related to Data Science and your immense interest in the subject. Hence, your passion for Data Science will not only improve your results but will also show your employers the dedication you have towards achieving knowledge and perfection in this field.
It is ultimately important for every aspirant to figure out if they are truly passionate about the subject of Data Science or not. Data Science is one of those fields that require the individual to be passionate and have an extreme interest in the subject to achieve better results and productivity. The article provided below is a list of ten wrong reasons to pursue Data Science. I would recommend checking out the following post if you are still unsure about your position on becoming a future data scientist.
5. Focus On Specifics
Data Science is a vast subject. It is a humungous field and not something that you can complete finishing or revising in a single day or a few day’s time. Hence, it becomes essential for each Data Science enthusiast to start focusing on the most crucial topics that could potentially be asked in the current technical round of interviews.
Every company has its own specific technical skill requirements that are expected from the employees it plans to hires. A certain company could have a higher focus on Natural Language Processing tasks in comparison to other concepts. Some other companies might focus on an approach towards a more solid base foundation and knowledge on computer vision. Or it is possible that a company just expects you to have a solid base understanding of basic machine learning concepts.
To explain my point in a more clear and concise manner, I will provide a simple example that could potentially occur in a real-life scenario. Assume you have a technical round where you are interviewed about your knowledge of computer vision. If you know this is the topic that will be covered in your interview process, learn to collect all the essential information related to the specific topics that you will be quizzed upon. Learn more about the computer vision library and other intricate details pertaining to this concept.
To know more about what topics are the best to focus on for every Data Science aspirant and enthusiast to cover all the essential concepts as fast as possible, check out the following article provided below that covers this topic in extensive detail. It covers a detailed approach to some of the concepts every individual must learn to ace and master Data Science as quickly as possible.
6. Strong Honest Attitude
An optimistic approach to Data Science and a strong, honest approach during these interview processes is optimal. Make sure to keep a straightforward approach and answer the questions in a concise and precise manner. Try to keep your solutions to the point without any additional irrelevant information.
If you don’t know the intricate details about a specific topic in Data Science, that is completely fine. If the topic is fairly advanced and you have prepared for that specific topic during your preparation time for the interview, it is better to acknowledge that you don’t have a clue about the particular topic to avoid speaking gibberish and catching yourself in trouble.
If you do have a small amount of knowledge in the particular topic about Data Science that is referenced in the interview, then make it a point to tell the interviewer that it isn’t the primary base of your knowledge. But you have not dwelled deeper into that particular concept.
Make sure your details are specific to the current aspects of the concept. Once you ensure your information is relevant, tell them with the utmost confidence, and make sure you say that is the knowledge you are limited with to avoid additional questions on the topic you are not comfortable with.
Try to keep your facts and information relevant to the specific questions. Keep your answers to the point. Don’t try to go overboard with the particular question. A topic of LSTMs should be explained precisely with all the important elements pertaining to the specific topic. Don’t touch other concepts like CNNs when you are not asked. Also, be straightforward in your approach towards Data Science.
7. Make It A Habit To Make Brief Notes
As discussed multiple times before, Data Science is a humungous subject. If you have an interview in a few days or in an upcoming week in the near future, and you are planning to finish everything in a single stretch, good luck! The chances of that happening are usually not high due to the large amounts of content matter to cover during that time duration.
However, there is one practice that can ultimately help you to gain productive results and better engagement. Taking notes and noting down all the essential concepts in your own words and in a way that you can remember every single concept with a single reading is extremely helpful. The pattern in which you choose to take down notes is crucial to have more success with this tip.
My suggestion would be to maintain a small book whenever you start preparing your study for Data Science. This book will be vital for you to revise every concept you ever need. You could choose to do this weekly or monthly, depending on the amount of confidence you have in your abilities and memory power. This particular book will act as one of the crucial aspects for you to develop a practice of the revision process.
Revising and keeping in touch with the subject of Data Science is an integral aspect of Data Science. Data Science is a field with constant evolution, and this consistent development and advancements in this subject will continue for the upcoming years. Hence, it is significant for every Data Science enthusiast who is passionate about the field to stay in touch with every concept as there are more to come in the near future. Messing up on the basics will give you a hard time in the future for learning new and intriguing topics.
“Believe in yourself! Have faith in your abilities! Without a humble but reasonable confidence in your own powers you cannot be successful or happy.”
— Norman Vincent Peale
Despite the wide array of concepts that you will need to cover in Data Science for any kind of interview that you plan to take up in the near future, it is undeniable that the tips provided in this article will help you to achieve this process with a higher success rate. I would highly recommend trying and testing out these tips for achieving better and effective results in your interview process.
An important point I would also like to touch upon in this article is it does not matter if you fail or succeed in an interview. As long as you know that you performed to your best potential, every interview should be treated as an experience level up and something that you seek to learn a lot. You will get accustomed to numerous interview environments helping you in developing a more straightforward approach to many types of problems.
Hence, always give your best and ensure that your primary objective in life or any interview process is to ultimately gain knowledge and work effectively to improve your wide array of skills. In Data Science and machine learning interviews, your projects, resume, skills, and effective communication will enable you to encounter any difficult situation and come out victorious from these circumstances.
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
Check out some of my other articles that you might enjoy reading!
Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!