Before proceeding to crack the road map of the data science interview first we should know what is data science? So in very simple words, data science is the study of data. It involves developing methods of reporting, storing and analyzing data to effectively extract useful information to make informed decisions. The goal of data science is to gain insights and knowledge from any type of data both structured and unstructured. Data science is being used across different industries already with the advancements in predictive modelling. Data scientists can help predict the outcome of disease given the historical data of the patients. With data science, banks can manage their resources efficiently and make smarter decisions through fraud detection. In the transport sector, data science has actively been used in the automation of self-driving cars. The applications are numerous and this is just the beginning. Now let's see why the data science and the analytics industry are growing so rapidly?
In 2019 the analytics industry has grown to 3.03 billion dollars in size and is expected to double in the upcoming 2025. Overall the analytic industry currently accounts for almost 21% of the whole IT industry in India. If you look at the adoption of data science in different industries you would see that finance and banking contribute to most of it. Overall the need for data science is going to double in the upcoming days as the amount of data is increasing day by day in every field.
As the field of data science is growing day by day the number of jobs for the youth is opening in an exponential increasing order. So the interested employee can move to this field very easily. The only required thing is knowledge. From organizations trying to meddle with petabytes of data, a data scientist’s role was to help them utilize this opportunity to find insights from this data pool. They will use their computer science, statistics, and mathematical skills to analyze, process, interpret and store data. It is not just about analytical skills, but a data scientist’s scope combines the best social skills to discover trends. Data scientists also leverage Machine Learning and AI, use their programming knowledge around Java, Python, SQL, Big data Hadoop, and data mining.
Roadmap to crack the interview: Assuming that you have a decent amount of knowledge in solving problems related to Data Science and machine learning now always remember that to qualifying the data science interview you need to follow some steps-
Resume Building: It should be a well-formatted resume. Everything that you did in the field of data science should be clearly mentioned. Mention your educational background. Make sure that you do lots of data science projects with proof. For this, you should have a Github account and you should provide the link of your GitHub account so that an interviewer can also go through the project. Each and everything should be clearly mentioned with respect to the repository that you create in Github.
Let's understand with an example that if you want to create a POC (proof of concept) project in machine learning, the first thing you will do is you should have the details of the data you gathered. And then you have to create each and every pipeline very clearly in your GitHub report because whenever the recruiter finds the link in your resume he would definitely go to check your repository. That will actually give the impression of your work done in the field of data science. Now here pipeline means features selection, feature engineering, model creation and part of model deployment. So make sure you did many POC projects that should be very helpful for you. You should also List down achievements like hackathons, boot camps and certifications that is very beneficial for you.
Basic knowledge of data science: Whenever you go for an interview you should have very clear knowledge about the designation you applied for. You have to be very clear about the basic concepts of data science such as statistics, programming languages, mathematics, Business logic and of course, machine learning algorithms. The interviewer can randomly ask the basic questions to test your knowledge and to see how passionate you are about this job that he/she is going to give to you. Learn to collect all the essential information related to the specific topics
Maintain Clear notes: Data science is a subject that is growing day by day exponentially so the knowledge that you learn every day should be noted. Anything that is new for you in this field you have to note down so at the time of the interview or else when you need you can give a quick revision to it. Make the notes in such a way that you can able to remind the things in a single reading short. Try to make notes in your own words that are very helpful to you. This habit is beneficial inside the industry also. You can revise the further concepts of data science anytime whenever you want.
Experience of data science projects: The most deciding part of the interview journey experiences on projects. You should have at least two or three POC projects with a very clear repository on GitHub. You must have projects in data cleaning, exploratory data analysis, data visualization and machine learning. What are the specific things that you did in your data science project you should mention over the repository?
If you have hands-on experience on data science projects it is very beneficial to you to grab the job because the recruiter is going to definitely ask about the projects, he also wants to know everything about the project from scratch. It also shows that yes you are able to take challenges that are coming to the path of successful projects. Working on projects also shows that you have knowledge about the different programming languages like python, R and SQL(structured query language). Python is commonly used in this field because it has various inbuilt libraries and modules. The recruiter can easily know that yes you also implement that kind of programming language so you are also a good programmer.
Be confident: Confidence is the act of trusting yourself. If you start showing that you trust yourself is critical because it can lead your interviewer to trust in you, as well. Aim to communicate to your interviewer that you know you can do this job well. The job you are applying for requires more confidence.
Try to answer the questions in such a way that is very impressive and also increase the desire of the recruiter to know more about the terms that you used. So use some specific and technical terms that you know.
For example- If you worked on a project eg: web scraping. The recruiter can ask you more about this project and the strategies toward building this project, how do you gather data on it? So you should be prepared to answer questions on what you feed in your resume.
Habitual to learn new things: Data Science is a field with constant evolution. Billions of things are discovered on a daily basis, so if you are passionate about becoming a data scientist it is very crucial to learn new things rapidly. Most of the interviewers frequently ask questions about the latest technologies and the latest things that the domain is working on. So make sure that you are aware of that kind of technology, news that is happening around space and the advancement of the domain.
Conclusion: Data science is one of the growing fields. It has become an important part of almost every sector. It provides the best solutions that help to fulfil the challenges of the ever-increasing demand and maintainable future. Thus, a data scientist must be capable of providing great solutions which meet the challenges of all the fields. To perform this, they should have proper resources and systems which help them to achieve their goal. If you want to become a Data Scientist with the best salary, then you need to be at the top of your game. To crack the interview of data science you need to be very confident and have a very strong and honest attitude towards your goal. You should be focused on specifics and sharpen your basic knowledge and initial skills in data science. Always try to learn new technologies related to it. Following these steps is greatly helpful to cracking the interview of data science.