Introduction
Internships and apprenticeships are two of the most popular methods of learning on the job and gaining crucial skills. The concept of an apprentice started dying out once masters of various sciences and crafts were replaced by employers. Eventually, internships took over from the 20th century as a standardised approach for gaining domain experience.
Internships are officially commissioned by the company and also feature documentation or certificates that justify an intern’s time at the company. Internships are essential for gaining hands-on experience and are one of the best methods of adapting to job roles that you like. For instance, if you complete an internship as a Data Scientist or a data analyst, then you will become prepared to function in any of the respective professions with ease. Internships also make other companies more likely to hire you, assuming that you are already trained in the job role.
Another factor we must take into consideration is the tools and technologies that are associated with sectors such as Data Science. There are many software and methodologies which are domain-specific and are not necessarily covered in degree programmes. Thus, individuals can use the time during their internship to acquire these necessary skills. Yes, there are online courses that teach these skills but an internship allows companies to believe that you do not need any further technical training (unless a company is using different technologies). There are programmes such as Data Science Immersive Bootcamp that can provide you with real-world job training or internships as well as teach you all the necessary skills.
There are also various soft skills that internships help you pick up. Internships are the best approach for gaining experience for freshers. Even if you are an exceptional student, being an intern first is recommended as it is sometimes a requirement for top MNCs that are dealing with domains such as Data Science.
Benefits of Data Science Internships
Joining and then completing an internship in Data Science can help you in a lot of ways along your path to becoming a Data Scientist, a data analyst or a data engineer. Internships serve as proof of your accomplishments and your foundational abilities. Through your internship and the projects you have worked on, employers can find out your capabilities and how well you fit inside a Data Science process or a pipeline. Also, without an internship, it is almost impossible to get jobs as a Data Scientist.
Let us check out some of the main reasons why internships are essential for Data Science jobs.
Crucial Skills
The best thing about internships is that interns are not expected to know much when they join and are able to learn while being on the job. Unlike degree programmes and courses, internship roles require interns to carry out many tasks that help them gain practical experience. This helps one acquire enough knowledge about all the necessary tools, technologies, techniques and methodologies.
Formal education mostly covers foundational topics and not specialised tools and skills. Thus, one will always learn something new while being an intern. Generic curriculum from formal education generally features outdated technology while companies operating on the ground adapt to modern practices for meeting business requirements.
For example, you might have been taught Python for programming and Excel for foundational analytics during your education. However, the company you are working for requires you to use Microsoft Power BI, Azure and various libraries for Python such as Matplotlib. Nowadays, Power BI caters to various operational and strategic requirements of a company. By learning other technologies from your internship, you will seem more alluring to employers who are also using the same systems or tools.
Similarly, you can learn skills related to data pre-processing, data mining, data warehousing and tools associated with cloud computing, artificial intelligence and machine learning.
Experience and Domain Knowledge
One can gain crucial experience with the help of internships. From the daily tasks during internships, interns can learn important domain information that will help them become better employees in the future. With enough exposure during your internship, you can even become a domain expert. For example, you might become excellent in noise removal or visualisation just by carrying out these job responsibilities during your internship.
With more experience and knowledge, you will also feel more confident, essentially reflecting your skills through high-quality work. Also, you will be adding value to the company you are working for, providing you with enough job satisfaction. Many companies have specialised training facilities and internal resources that interns can use for growing.
Employability
Freshers find it hard to get good jobs due to not having any experience. A lot of companies assume that it will take additional time and money to train freshers and thus, many of them prefer freshers who have completed an internship. Companies expect interns to already have domain knowledge and an understanding of how Data Science processes function.
This enables people who have completed their internships to become more employable. Let us take an example where there are two candidates where one has completed an internship and the other has not. In this kind of situation, it is almost guaranteed that a company will choose a candidate who has completed an internship. Many internships also convert into full-time jobs upon the completion of the intern period.
Networking and Career Prospects
By joining an internship, you can grow your network and become acquainted with more people working in the field who wish to be involved in the future. This can help you learn the opinions of other professionals in the domain. You can also identify the future of the sector better with the help of other senior resources. Other professionals working in Data Science can help better guide you in terms of upskilling yourself and your career. For instance, domain experts might recommend a tool such as SAS to upskill yourself in.
For Data Science, growing your network is crucial to stay updated about the latest market trends and innovations in technology. There are also many great career prospects you might find out about after joining an internship. Many companies also refer their interns to their partners or to other organisations.
Conclusion
Internships help you gain valuable guidance that you will not be able to find during your formal education. College professors and programme instructors also provide important advice and suggestions in terms of your career, but internships allow you to take the wheel into your own hands and experience the domain of Data Science on your own.
An internship is like a journey that allows you to gain skills without any compulsory cost. As a matter of fact, many internships even compensate interns, allowing them to learn while they earn. Finally, without an internship, it might be extremely hard to get a good data scientist job, if not impossible. Bootcamps such as Data Science Immersive Bootcamp by Analytics Vidhya can solve this problem by offering a 100% job guarantee. Read more about a comprehensive comparison among Data Science bootcamps vs degree vs online courses here.
