Make the Most of A Data Science Internship to Kick-start Your Career as a Data Scientist

Data Science

Date : 09/20/2022

Data Science

Date : 09/20/2022

Make the Most of A Data Science Internship to Kick-start Your Career as a Data Scientist

Explore the power of data science internships! Unlock valuable insights and details on data scienec internship. Empower your future today.

Aravind Chandramouli

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Aravind Chandramouli
Head AI COE, Tredence

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Make the Most of A Data Science Internship to Kick-start Your Career as a Data Scientist

Table of contents

Make the Most of A Data Science Internship to Kick-start Your Career as a Data Scientist

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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.

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 visualizations 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 highquality 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 are no Employers 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 prioritize 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.

Aravind Chandramouli

AUTHOR - FOLLOW
Aravind Chandramouli
Head AI COE, Tredence

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