Beginner’s Guide to Get a Data Science Internship

This article was published as a part of the Data Science Blogathon.

Introduction

Generally whoever is pursuing data science would want exposure, an opportunity in this field to feel right, motivated in moving forward, and becoming a renowned data scientist. One of the biggest and meaningful opportunities a student can get in this field is being opted as a Data scientist intern. There are other training and tasks that you can do to sharpen and strengthen your profile/resume but as I am a Data Scientist Intern starting from the month of February 2021, I will share with you my thoughts, the journey that I took; to at least get eligible for this post.

About my Phase

To start off, let me make this very clear that I am not some tech genius, who is on the computer coding from class 6 or 8 or even 11. I am an artist and have always been one, singing for the past 8 years, doing theatre for the past 7 years, dancing classical and lyrical, sketching, and all kind of creative skills similar to these most popular art forms. So definitely, I was behind most of my peers during the initial phase of my computer science engineering at my university (UPES). Throughout the whole first year, I was doing average not anything good enough.

Then comes the second year, the time when I started looking for things that interest me in the technical field. I shortlisted Mobile app development and artificial intelligence, Mobile app dev; because it seemed really cool, I thought I could make those apps that people will use in their daily lives on their phones, and Artificial intelligence because secretly I have been in love with human psychology for a long time so I used to study a lot about it on my own, and when I encountered that people have started putting together the functioning of a neuron in technology (Neural Network). I felt a chill down my spine from excitement. I gave both these fields their individual attention and time.

Seed is Sown

We all at some point in time face such dilemmas, a choice that we don’t know how to make, For me, the deciding factor became my unease feeling when I couldn’t satisfy the curiosity to know more about neural networks and was doing mobile app dev at that time. Once I realized that mobile app is not my priority looking at my excitement for AI, I took a U-turn from app dev and started my first course on Neural Network, I didn’t do any machine learning beforehand cause didn’t know much about it Therefore started straight from Neural Networks with Pytorch. Believe me, I really enjoyed learning about the theory on Neural nets and Deep learning in general but when it came to coding with Pytorch, I couldn’t understand any of its functioning, I had to memorize when, where and which functions to use while coding neural nets to satisfy myself that I know how to code a neural network.

My “ZONE”

 

Then came the Covid-19‘s lockdown (22nd March 2020), Wow! what a blessing for me. I already had this big flame lit up inside me to study Deep learning and when I got stuck at my PG in Dehradun due to lockdown, I created a routine so tight, a habit so consistent that I used to study and code for 12-14 hours a day. This was the first time in my life I was enjoying studying so much that all those hours seemed like nothing that could exhaust me, this perseverance was maintained till February of 2021 and things changed after I got my Intern as a Data Scientist for a wonderful Hospitality Start-Up: “Upswing Cognitive Hospitality Solutions”.

Here are the things that I did during my “Zone” (as I like to call it referencing the word from psychology) and think will help you make your skills in Data Science and Machine learning really sharp and useful.

1. Create a Map for yourself

As I told you before my start was off, I started with Deep learning where instead I should have picked a path from basics to Advance, this way your brain learns step by step, and things are understood concretely. So, take your time and discover different directions that are possible with Data Science, machine learning et.al. There will be plenty of paths possible but don’t get very particular with all of them; just segment things of your interest (in my case It was Deep learning with Data Science) into basics, Intermediate, Advanced.

Start with basic things and stay on track, first cover all the basic topics of your interest and then solve problems based on those topics without any help. First, you must feel comfortable with what you are currently doing and then make a shift in terms of the difficulty of topics.

2. Keep developing other skills besides this in parallel

Adding ‘Scientist’ at the back of ‘Data’ is not particularly something that you can do after learning few libraries in Python or R or any other data science supporting language. A data scientist must know how to integrate different technologies to achieve the final outcome of the problem. What I mean by this that, you should be familiar with Databases, Git, Github, Deployment related Tech, may it be basic web dev to host your application online or docker to make a container and deploy it on the cloud and stuff.

I am not asking you to learn everything, if your end goal is something different than all this, discover things that are required for your goal along with Data science concepts and coding skills. A must skill I believe every data scientist should focus on is writing, it’s a basic required skill for a data scientist to create a report at the end of a project for their stakeholders, and that report’s presentation is one of the most important steps inside the complete work cycle for a data scientist.

3. Don’t get stuck on one Medium

What I mean by this is everyone has their comfort zone in terms of how they learn things, be it be videos or books or etc. But confiding yourself with only one form of a medium can be restrictive. There are brilliant, absolutely piece of art kind of books available that you should be keen on reading even if you like studying from online videos. This flexibility will help you more than you can realize, reading research papers, blogs, and all.

For people who learn from reading, you can check out some great video courses mentioned below to visualize the concepts with such ease and fun.

4. Socialize Yourself

This step is particularly related to increasing your chances to get Intern positions or even jobs. We can only do so much with our time, if we brand ourselves through our work and social relations, we increase our chances exponentially of getting spotted and offered an opportunity.

This same thing happened to me, In my 5th Semester, I scored 96 in the Python End semester Exam, so when the company reached out to some of the faculties at my university, I was recommended by my python teacher to the in charged teacher and she took a shot with me, after that I gave my interview and got selected as the Intern.

5. Learn Beyond Common

Keep your researcher side active while learning the concepts, Data Science, Machine Learning, and Deep learning are having extensive research going on continuously in every corner of the world. So, keep a broad mind and learn things beyond the steps of the data science work cycle. I am saying this because no knowledge that you gain goes to waste and integration of your knowledge from different stages and dimensions of your life makes you who you are today, along with that it gives you a unique identity and thought process. So, Utilize it.

I am mentioning a couple of things that I learned alongside:

1. Responsible AI (Ethics in AI)

2. How people perceive different kinds of Visualization (Visualization Wheel Dimensions)

6. Learn from the Best resources

• Video Courses:

  • Youtube Channel, freeCodeCamp

  • Coursera Courses :

    • IBM Data Science Professional Certificate

    • Applied Data Science from Univerity of Michigan

    • DeepLearning.ai courses if you are interested in Deep learning

    • Data Science A-Z on Udemy by Kirill Eremenko

    • Applied Data Science by IBM

      • For more recommendations, you can contact me on LinkedIn

  • Data Camp: My favorite resource for Data science. Explore it to your hearts’ content, you will love doing and learning data science on DataCamp.

• Reading Courses:

  • Practical Statistics for Data Scientists 50+ Essential Concepts using R and Python by Peter Bruce, Andrew Bruce, Peter Gedeck

  • Python Data Science Handbook by Jake VanderPlas published by O’Reilly

  • The Art of Statistics Learning From Data by David Spiegelhalter

  • The visual Display of Quantitative Information by Edward R. Tufte

  • Data Mining Practical machine learning tools and techniques by Ian H. Witten & Eibe Frank

7. Take Proper Notes

This point is self-explanatory, You can’t possibly remember every single thing that you read, learn or study. So to make your personal Search engine (Brain) more efficient and faster taking notes properly is the best way. You will feel more powerful psychologically whenever you see your notes, they depict your hard work, progress and so much knowledge that you have gained till now.

8. Conquer in Steps

You need to feel satisfied with yourself now and then, to keep yourself pushing forward and not letting the flame of learning sincerely vanish. I have seen a lot of people getting scared or tired or just uninterested in work hard after some time. According to my views, This usually happens when you feel that you haven’t reached the goal and you keep on walking without appreciating where you are standing right now, how far have you come through your dedication and hard work.

Therefore Try to set small goals and once you clear them, be proud because you are the best version of yourself right now, not giving up and walking forward with happiness and satisfaction in mind.

9. Contribute to Communities

Just like you are studying from many wonderful resources, why not contribute after a certain point of knowledge and become one yourself for even a single person. The act of sharing knowledge is not good to keep the flow of fresh knowledge alive, but also to make name for yourself. These contributions will give you such importance that nothing else could. Psychologically you will feel really powerful and that would reflect more on your upcoming work. It keeps the learning process strong and sharpens your overall image as a Data scientist or anything else.

Few examples of such communities are, Kaggle, Paperspace, Analytics Vidya, Medium, etc.

10. If possible find a Mentor

Well, this one is particularly not an easy task, but it is an extension to the earlier step of “learning from Best resources”. When you have someone (an expert / or even a more experienced person than you) it drives you in the most optimized direction for your learning, you wander less and grasp more. The best way is to reach out to as many people as possible on LinkedIn (Don’t beg them or irritate them, just be clear and straightforward with what help you need from them).

11. Believe in Yourself

I am mentioning the MOST Important step in the END because even if you understood all the above-mentioned steps except this, you could possibly fail or get lost in so many things that I would definitely not want for you. So, however long it takes, if you are clearing your daily, weekly goals, expanding your network of people,

YOU WILL DO IT, CAUSE IF NOT YOU THEN WHO? IT WILL BE YOU! Believe that!

That was the END of this article, I hope you learned something or the other for your OWN Journey. Share it with me anytime through LinkedIn.

Gargeya Sharma

B.Tech Computer Science (3rd Year)
Specialized in Data Science and Deep Learning
Data Scientist Intern at Upswing Cognitive Hospitality Solutions
For more info, check out my GitHub Home Page

LinkedIn        GitHub

Blog Cover Photo by Mantas Hesthaven on Unsplash

Zone Photo by Paul Skorupskas on Unsplash

Gargeya