Freshers in data science

freshers in data science

Who is considered freshers in data science?

Students with 0 to 1 year of experience will be considered freshers in the data science world. Internship experience can help you to get past the freshers’ tag but by a little margin. Engineering students with data science majors will also be considered freshers!

Data science has seen an increase in popularity among IT professionals and students. The demand has gone up by so much so that the recent trend shows some interesting new behavior. The initial days of data science hiring were exclusive for experienced professionals or to masters or Ph.D. students. In the last two years, the adoption of AI has been skyrocketing and created a huge gap between demand and supply. Yes, the number of people who are learning data science is more but the quality hire is still a challenge. In case you are wondering what makes a great data scientist different from an average one, you can click here. Trying to cover this gap, companies are considering a major shift of hiring data science freshers nowadays and in this article, we are going to discuss a few tips for data science beginners and how they can select the right company. If you are a data science aspirant you can check our comprehensive advanced data science course here.

 Industry expectation

The data science industry is more mature now and the hiring process often includes a technical interview with a senior data scientist. Though requirements and hiring processes differ from company to company, here is the list of some common tests we have seen.

  1. Expect a Python test where you will be asked MCQ and OOPs design patterns. A lot of companies expect the candidates to know OOPs concepts and design patterns. For beginners who don’t know what design pattern, you can practice industry-level OOPS programming on websites like Geektrust. Creating classes is not enough. The model or program should be easy to maintain and scalable. Understand how abstraction and interface work. Package your code and don’t forget to unit test. The key here is to understand the fundamentals and write cleaner code. The interviewer will be confident to hire if they think you are not a drag to the team.
  2. Statistics and mathematics questions are the trickiest to answer. In most of the tests expect the questions to come from graphs and images. But if you are applying for companies where NLP and image processing research is involved expect questions from Bayes theorem, Bayesian networks, Cohen Kappa, probabilities and chain rules, Fourier Series, Linear equations, and SVD. Tougher the statistics questions, better the company, and the job role.
  3. Machine learning and deep learning questions can either be MCQ or project-based. Most of the companies are giving case studies with deadlines. In case you get complex projects, most likely you will be running after accuracy and the problem in hand. It is very important to follow some basic structuring before the final submission. Package your code in a virtual environment and don’t forget to use OOPs and design patterns. Sometimes these small tricks can give you a significant advantage over the competitors.
  4. The final round is expected to be a technical round with a data scientist where you will be mostly asked about fundamentals and projects. It is important to know that if you don’t know the answer to some questions, it is okay to say no rather than bluffing. If the candidate is showing internship experience, expect questions on position, project impact, and contribution. This round is common nowadays and can be expected as standard across the industry.

Current situation

With so many certification programs and with no real knowledge, certificates are worthless now. The industry is least bothered about certificates nowadays all thanks to fake online data science courses. Here are some useful facts if you want to pursue a data science course. Almost 40% of data science hiring now is for freshers including internship job openings. MNC companies have increased the package for freshers in data science to 8.5 lakhs from an average of 6.5 lakhs in India. Worldwide data science jobs will increase exponentially to a whopping 11.5 million by 2026! Overall demand for MNCs has increased 39% from 2021 to 2022. Finance continues to top the data science hiring industry with a massive 60% market share. 61% hirings in 2022 will be for bachelor degree holders in 2022 which is a 20% year-on-year growth.

Steps to follow as a fresher

Maintain projects and a strong GitHub profile! Almost all companies will ask for projects and internship experience before selecting. Get yourself good at Python and deployment and choose for offline sessions, if possible, for better engagement and practice. Keep your LinkedIn profile updated and connect to data scientists on daily basis. Be shameless to ask for jobs from HR. Competition nowadays is fierce and the more you are connected and active, the chances of selection will increase.

What are the right companies for freshers?

Always prefer AI startups over MNCs where you can learn and gain experience. In startups, you will not be confined to narrow projects. As freshers in data science, it is very important for candidates to know all aspects of development including model selection, model creation, packaging, API connections, cloud deployment, and testing the models. Try for companies with employees’ sizes of 10 to 30.

The next step

Once you enter as a data scientist to any company, there is a high chance you will forget the concepts you are not using on daily basis. Don’t stop practicing and learn daily. Keep reading research papers. I also recommend students to have broader goals like creating their own startup where they can create a larger impact on society. Best of luck in your endeavors. If you love learning from our blogs don’t forget to like and comment. Share this article with your friends for better reach. Thanks for reading.