Difference between a prepared student and unprepared student during data science interview
Ivy Dec 22, 2020 No Comments
Data Science has become one of the most interesting and most sought jobs in recent times. While the demand for the job has become quite competitive, the job seekers still find themselves in bewilderment as there is no right guide for cracking the data science interviews. In this article, we aim towards providing our readers with proper information about the data science interviews and how one should set proper goals and march towards their dream job. It is important to make a calculative preparation for someone who is prepared and also for someone who is not prepared in order to face the interview.
A person who is prepared for a data science interview has definitely got an advantage over someone who is not prepared for the interview. However, there is always a scope of improvement. Now, this totally depends upon the level of the interview and the job post one is applying for. For example, an aspirant targeting for a senior-level position is expected to prepare more for the interview than someone who is applying for an entry-level associate position. Here are some of the key points required for someone eyeing to ace a data science interview:
- Having Patience: Patience is the key. One who is patient enough to accept the situation and move forward is someone who is going to excel in many ways. There could be situations of hardships where a person could be less prepared than another aspirant, but it is completely fine as long as they are ready to understand the shortcomings and work towards making it better for the next time.
- Programming Skills: One who is ready to take on any data science interview must have good programming skills. The preferred programming languages could be anything ranging from Python, SQL, R, Spark, and HIVE. Nowadays, industries are moving on with Microsoft Excel and they are keen to use new technologies in order to analyze data. Python is known to be the most sought programming language by companies among all others. The ease of access and improved usability has enabled programmers to enter the field of analysis and do wonders. The best idea is to just pick a programming language and work on it to excel in it.
- Probability and Statistics: Let’s go back to the first level of high school mathematics. What we learned about probability, statistics as mean, median, mode, and the standard deviation is going to play a vital role in data science. Data Science, which means dealing with the science of data has become exposed to mathematics and statistics has a good role in it.
- Understanding the core: It is also important to know what exactly does one wants to pursue the career of data science. Whether one wants to become a machine learning specialist or if someone wants to become a data analyst, deep learning specialist or simply a data scientist, each has its own way of preparations.
- Creating a digital presence: As important as it is to learn about the concepts, it is also equally important to have a digital presence. It allows you to create a mark on the digital platform and showcase the data science skills. An online presence allows us to explore more and expose an aspirant to different competitions. In this way, an aspirant gains more confidence in creating a firm position in the field of data science. Data Science aspirants can create their profile on Kaggle/Github and help the community with ideas and projects. Kaggle is a great place to compete with others and level up the expertise in data science. GitHub is important as many recruiters look into a candidate’s GitHub profile to measure their potential before hiring them as a data scientist.
- Project Oriented: Creating projects related to data science is going to help in strengthening the concepts and acquiring more knowledge about the practical aspects related to data science. This not only helps in practising the data science concepts but also helps in showcasing one’s work to the world. More projects will help in creating a stronger impact on the CV and thus increase the chances of cracking any data science related interview.
- Maintaining a LinkedIn Profile: Time to get really professional. A good, updated LinkedIn profile can land data science aspirants into amazing data science jobs and help them get noticed. Most of the time, after doing all the above and posting them on LinkedIn makes them noticeable among the recruiters.
- Creating an interactive resume: It is not mandatory to have lots of stuff written on the CV. Instead, it is more important to have very specific skills that one would like the recruiter to see while wanting to get shortlisted as a data scientist. If someone is a fresher, they could certainly try to add some of the projects that helped them to enrich their knowledge on data science, whereas someone having relevant experience could always mention about the work performed in the previous organization.
Above all, it is also important to have clarity on the role one is applying to and the specific work they are expected to perform. Clear, concise thinking before applying helps to align the aspirant’s knowledge and capabilities with the recruiter’s demands. An interviewer looks into the ability of an aspirant to breakdown a problem statement and arrives at conclusions in smaller steps. The key solution for it is to emphasize the goal that one is going to achieve and then it becomes much easier to break the problem statement and approach the solution. We would also recommend knowing about the company in detail so that it becomes much easier to understand the requirements and if one shall be able to fulfil them. Once the person knows how he/she fits the role and help them in their requirements, it will be like completing a jigsaw puzzle.