I began applying for internships in the first year of my bachelors. In the third semester, I got a volunteering role at an NGO for analysing their YouTube Dashboard and focused on improving their engagement using data. It also wasnt exactly "data science" and was unpaid but a good start for me. Before I landed my next role, I upskilled with my 100DaysofMachineLearning. I found time for writing blogs , contributing to open source , doing assignments and projects.
I received many DMs on various platforms about all the skills on my resume. I decided to address this in one blog for once and for all. The skills you need are advanced python , SQL , Github , PowerBI and statistics. Shocker. But how do you prove that you have these skills?
How does a recruiter differentiate between you and every other person applying with the same list of skills. You need to understand that so you learn the right way.
Focus on tailoring and personalising the tips and tricks that I will share here.
2024 Internship Landscapes
Once more, I would like to mention that this is according to the experience that I have had regarding data science internships. If you had a different experience then please feel free to comment.
You need to get the right skills and the right resume before you start applying. I am not a fan of spam applying for every and any internship that comes my way. I apply if I meet the more than 50 percent of the requirements.
Once your resume is shortlisted , the next round would most likely be an assignment.
Almost all internships I have applied to on various platforms followed this structure :
Resume Shortlisting -> Assignment Round (3-5days) -> Interview on the Assignment (on the day of submission , or immediately after that) -> General Technical Interview -> HR Round -> CONGRATS YOU'RE HIRED.
Some highly precious internships also do a technical screening round before they give you the assignment. You need to focus on completing assignments in limited time to get an internship. That is the goal.
The mindset
Now, that you know how to internship process works, you need to be clear on some values.
Here are some general bullet points:
Do not sacrifice college grades for internships. Alot of applications have CGPA criteria and college preferences.
Dont chase certificates for impressing recruiters. Learn from a course for knowledge.
You can learn anything from YouTube and Github. Consistency beats talent. It is always consistency.
Learning a skill standalone is first then you need to know how it works relative to other skills. You know SQL and you know Python. But thats of use only if you know how they are combined in a real life project.
Memorisation is not the point. The point is to know why we are doing something. You can always look up the syntax later.
Work for 4 months first on upskilling then focus on internships and getting hired. Dont be impatient. Assignments are a huge time investment as you re doing unpaid projects for someone.
Learn three skills back to back and then use them in one project. Keep repeating this in sets.
The quality of your skills is decided by the quality of your projects. I will write another blog for how to select your flagship project and how to present them in interviews.
The Skills
A programming language
Python and R are the industry standard. I still dont think I know Python. Everybody acts like Python is the easiest thing on Earth but I think I will only be confident in Python when I think in it. There are plenty of resources, find one that starts with syntax , important modules and fundamental data science libraries like Pandas , Numpy , Matplotlib. You can pratice Pandas on Leetcode and Kaggle.
My recommendations are Corey Schafuer, RealPython Blog and FreeCodeCamp.
Databases
Learn SQL and practice and practice and practice. Database fundamentals are very important for interview rounds. After RDBMS,also explore No-SQL databases.
Resources : CodeBasics , W3Schools
PowerBI/Tableau
Learn statistical analysis and then learn PowerBI. Dashboard designing is a very common internship responsibility. Learn upto DAX and do not forget about intergrating it with Python.
Resources : DataCamp , Pawan Lalwani , Alex the Analyst ,
Basic Web App related skills
Streamlit is a very popular library for creating web apps in python. HTML CSS are a basic requirement any computer science student should be familiar with. Learn frameworks like Flask and Django. Learn enough for your projects.
Resouces : FreeCodeCamp , Corey Schafuer,
Github and Kaggle
Understanding version control concepts and how to use Git/GitHub for managing code repositories and collaborating with team members is definitely a plus. Kaggle is a complete platform dedicated to data science competitions and learning. There are ranks that you can work your way up and add value to your experience section.
Machine Learning Fundamentals
Machine learning—it's the heart and soul of data science. Start with the basics and work your way up. There are plenty of internships that get you can land without machine learning, but you will inventually move towards this. Machine Learning along with domain knowlege is a lethal combination.
That's a wrap
and thats it for your first ever internship. If you are unable to get a role despite knowing these skills then you need to focus on finetuning other aspects of job applications like resume , portfolio projects and communication skills.
If you would like to add something to this feel free to comment so everyone else can also learn. If you take away something from this blog dont forget liking and sharing it.