The world of data science is one where practical knowledge is given a much higher weightage over grades and rote learning. Recruiters look at the work experience and project portfolios of potential candidates while deciding the best fit for a job.
This makes it essential for freshers looking for data analyst roles to take up internships before applying for any job role. The first internship is a memorable affair for every data analyst.
As this is your first stint with the practical application of the academic world, it will be a wonderful experience. From preparing yourself to compete for internship roles in coveted organizations to settling in a work environment, there will be a lot of new learnings.
With a host of things happening simultaneously, it may seem overwhelming. It is natural for students looking for internship positions to be at a loss of how to steer one’s career forward. We have curated a list of things that you can expect from your data analyst internship program.
Getting the Internship
Having understood the importance of internships in one’s data analyst career, you would realize that there is intense competition for such positions. Given below are a few tips that will help you bag the internship opportunities of your dreams.
Work on your Resume & Cover Letter
All organizations will require you to upload your resume and cover letter at some point in the application process. The resume is the first impression of you, and you need to ensure that it is short and powerful.
Cut out the unnecessary bragging and have a professional summary, details of your coursework, volunteer, and extracurricular activities mentioned in addition to your educational information.
Prepare for The Interview
Most internship selections involve the interview process, and this may either be a face-to-face interview or a virtual one. Irrespective of the mode of questioning, make sure that you are well-prepared to impress the panel.
Lookup a list of common interview questions, come up with answers and practice the same in front of a mirror. That way, you can evaluate and correct your errors and improve your interview preparedness.
Send out Multiple Applications
The ideal course of action would be one where you apply for an internship, prepare hard, and bag the position. However, the real world is far from perfect, and you should expect some rejections to come your way.
Understand that the rejections are not a certification of your incapability, and the only way to rise above this is to apply for multiple internship opportunities.
During the Internship
At this stage, you should brace yourself for the fact that your learning curve is going to take an exponential ascent. While the first few days may be incredibly hectic, you should be prepared to learn something new every day.
Also, understand that the internship is an excellent opportunity for you to pick up on social and professional skills, and not all that you learn will be technical.
Understand the Business Problem
When you take up an internship, you will be assigned to a team that will be working on a project. If you are lucky, you will join the team in the initial days of project conceptualization. However, if that is not the case, feel free to ask questions and have a clear understanding of the business problem that you are working on.
Realize that no question is too dumb, and if you need any clarification, you should not hesitate to ask. The corporate world is not your classroom, and you cannot expect people to share your knowledge with you, asking for the same.
Even if you are a part of a project from the initial days, being a fresher, you might take longer to comprehend the problem, and there is no shame in asking for clarification.
Collect the Data Source
Most organizations expect their interns to work on data collection. The accuracy of data affects the business model and takes a toll on the overall project results. That is why you need to put your best foot forward and ask your supervisors for guidance on data sources.
Work on Data Pre-processing
If you are looking to create a good impression in the organization that you are interning for, you cannot only build a model on the data that you collected.
You will be expected to clean the data that you have collected and ensure that they are consistent and meet a stringent quality standard. Lack of proper pre-processing may lead you to build appropriate models on wrong data, and that is an utter wastage of your time and efforts.
Build Data Models
Depending on the project in question, you will be building models. This is an essential step as most academic programs involve access to textbooks and MOOCs that talk about the theoretical aspect of model building.
When you get down to creating hands-on data models, a steep learning curve can be expected. Most organizations have mentors or supervisors who guide interns at this stage. Make sure you are diligent and pick up the practical concepts that will be discussed with you.
Evaluate the Model
While mean absolute error (MAE), mean squared error (MSE), and the coefficient of determination (R2) is the most common metrics used to evaluate a model, the exact metric may vary depending on the industry and type of data in question.
While your internship may not require you to be a part of this stage, take proactive measures to understand this stage well.
A look at the entry level data analyst job description of any top MNC would reveal that the organization expects you to be comfortable in model building and its evaluation. An internship is a perfect opportunity for you to practice these skills, and it will give you an edge in the rat race for success.
Thus, you see that while you cannot expect a smooth ride, your first internship as a data analyst will improve your professional skills. The world of data science is a young one, and taking up an internship will help you ensure that your skillset and professional expectations are in alignment with that of the industry.
If you are someone with a background in data science and a passion for numbers, you can expect your first internship to reaffirm your passion for the field and lay the foundations of a fulfilling career ahead.