How to crack Data Science interview as a Fresher (Steps for preparation)

How to crack Data Science interview as a Fresher (Steps for preparation)

Being interviewed can boost your adrenaline levels. Hacked interviews require tremendous preparation and research. Moreover, in this scenario, I am appointed as a data scientist because only proper training and practice can yield good results on the important days.

If you are a budding data scientist, you must have the working knowledge,  understanding, or ability to work for multiple companies with  full skill set.

Read on to understand how to not only pass the interview, but also speed up a step-by-step approach to a specific area of ​​your set of skills, technical know-how, and great abilities in big data and machine learning. What makes data science special is that its applications and the resulting expectations vary widely across industries. Roles are interpreted differently by different companies. Some companies might call a PhD degree a statistician, others might mean an Excel master, and others might mean a versatile expert in artificial intelligence and machine learning.

Step 1: Read your job profile, specifically skills, tools, and skills. Some studies in the company are not negotiable if the job description is not self-explanatory or detailed. Please clearly state the  data processor position you are applying for. Interviews are usually a combination of aptitude analysis, technical knowledge and attitude analysis. Most organizations are currently testing candidates for basic topics to assess their suitability for the company. Traits such as language comprehension, analytical thinking, quantitative abilities, etc. can easily be clarified by reading  the same to improve skills.

Step 2 : Highlight these important and relevant concepts  before the interview. To test your technical knowledge on a subject, you will likely have a skill round or  assessment, case study that significantly evaluates your knowledge of statistics, programming, machine learning, etc. Languages ​​such as R, Python, SQL, Scala, and Tableau.

Step 3: Organize on the following basic topics:

Probability - Random Variables, Bayes' Theorem,  Probability Distributions

Statistical Models - Algorithms, Linear Regression, Nonparametric Models, Time Series

Machine learning, neural networks.

So, in essence, you'll be tested here either through case studies or a discussion of  your problem-solving skills. It is helpful to be able to identify issues in the presented scenarios and correlate them with the proposed solutions and business impact. In doing so, provide examples of case studies or research papers that support your proposed solution.

Step 4: You can come up with the skills and qualities you need, but since data science and its applications are unique, you should demonstrate a willingness to learn and the flexibility  to adapt to your current organization throughout the interview.

Step 5: Write a short resume and anticipate how you will connect your experience at the interview to the job.

Step 6: Do a data science project. There are many public domains available for this, especially if you are a beginner. It's also a good idea to attend MOOC - Massive Open Online Courses to familiarize yourself with the different applications and professional applications.

More recently, the role of a data scientist has been seen as one who can bridge the gaps between multiple business functions. So, while you are not expected or required  to be an expert in every way, you should be able to connect features, ideas, and propose solutions in different areas. To stand out in an interview, you must not only demonstrate your personal strengths and competencies in the field, but you must also appear like someone with sufficient management skills, be communicative and have the skills to get to the core. problem.