For someone aspiring to enter the field of Data Science, internships are a good place to start.
| Photo Credit: Freepik
Data Science is one of the fastest-growing professions, worldwide. As the data pile gets larger every day, there is a need for data scientists to classify, analyse and add value to the information obtained. If you are looking to enter this domain, internships are a good place to start.
Search for internships that:
Allow you to apply your academic learning to solve business problems. This helps you understand how you may use Data Science to tackle real-world challenges.
Teach you problem-solving techniques and help you understand the importance of effective communication.
ADVERTISEMENT
Help you evaluate if this is the right career option for you.
An in-demand skill set across industries, Data Science is also a complex and ever-evolving field, making it difficult to know where to start. Therefore, think about whether you prefer working in a start-up environment or a larger company with a more established team. Startups can offer more hands-on experience, but larger companies may provide more structure and support.
Consider the type of problem statements you want to work on. Some internships focus on specific areas such as natural language processing or computer vision, whereas, others allow you to explore different domains such as retail, manufacturing, or consumer goods.
Focus on the quality of the people you will be working with and the opportunities that will improve your learning curve
As an intern, you will design and implement data-driven solutions to real-world problems, collaborate closely with analysts and engineers to design and build scalable data pipelines, algorithms, and models, and be responsible for data wrangling, exploratory data analysis, and creating visualisations to communicate your findings.
Evaluating success
When assessing the internship’s effectiveness, there are some crucial elements to consider. First, did the intern have the opportunity to work on interesting and impactful projects? Second, did he/she receive adequate support and mentorship from experienced data scientists? Third, did he/she gain a deep understanding of concepts and tools? If all three answers are yes, then, the internship was successful. Of course, an intern’s performance also plays a role.
Career growth
By working with data daily, interns develop the required skills and knowledge. Additionally, it allows networking with and learning from experienced professionals. Doing multiple high-quality internships will prepare one to handle the job’s rigour and expectations and also enable students identify their areas of strength.
Quantity or quality?
How many internships should you do before starting the job hunt? Unfortunately, there is no clear answer. Some employers are impressed by the number while others may consider the quality of internships. So, what should you prioritise?
Employers are looking for interns who can hit the ground running. Those who have prior experience, either through internships or coursework, are more likely to be able to do this. So, aspirants should prioritise quality over quantity. Employers also value interns who have a breadth of experience in distinctive and diverse fields and industries. In addition, one should also demonstrate that they are adaptable and have a well-rounded skillset.
The writer is the Head-AI COE, Tredence
This is a Premium article available exclusively to our subscribers. To read 250+ such premium articles every
month
You have exhausted your free article limit.
Please support quality journalism.
You have exhausted your free article limit.
Please support quality journalism.
The Hindu operates by its editorial values to provide you quality journalism.
You have read {{data.cm.views}} out of {{data.cm.maxViews}} free articles.
