Data Science Internship | Beginner's Guide to Getting a Data Science Internship

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


In general, anyone into data science will want exposure, an opportunity in this field to feel good, motivated to move on and become a renowned data scientist. One of the most important and meaningful opportunities a student can have in this field is to be chosen as a data scientist intern. There are other trainings and tasks you can take to sharpen and strengthen your profile / resume, but since I am a Data Scientist practitioner as of February 2021, I will share my thoughts with you, the trip i made; to at least be eligible for this post.

About my phase

Start, let me make this very clear that I am not a tech genius, that is in the computer coding from the class 6 u 8 or even 11. I am an artist and always have been, singing for the last 8 years, doing theater for the last 7 years, classical and lyrical dancing, drawing, and all kinds of creative abilities similar to these more popular art forms. So definitely, I was behind most of my peers during the initial phase of my computer engineering at my university (UPES). Throughout the first year, i didn't do anything good enough.

Then comes the second year, the moment I started looking for things that interest me in the technical field. I selected the development of mobile applications and artificial intelligence, Mobile application development; because it looked really cool, i thought i could make those apps that people will use in their daily life on their phones, and artificial intelligence because I've secretly been in love with human psychology for a long time, so i used to study a lot on on my own, and when I discovered that people have begun to put together the functioning of a neuron in technology (Neural Network). I felt a chill down my spine from the excitement. I gave these two fields your individual attention and time..

The seed is sown

We all face such dilemmas at some point, a choice we don't know how to make. For me, the deciding factor became my feeling of unease when I couldn't satisfy the curiosity to know more about neural networks and was doing mobile app developer at the time. Once I realized that the mobile application is not my priority seeing my enthusiasm for artificial intelligence, I took a U-turn from app development and started my first neural networks course, I didn't do any machine learning beforehand because I didn't know much. about it Therefore, started straight from Neural networks with Pytorch. Créame, I really enjoyed learning about neural network theory and deep learning in general, but when it came to coding with Pytorch, I could not understand anything of its operation, I had to memorize when, where and what functions to use while coding neural networks to make sure I know how to code a neural network.



Then came the Covid-19 lockdown (22 March 2020), Wow! what a blessing for me. I already had this big flame burning inside of me to study Deep Learning and when I got stuck on my PG in Dehradun due to crash, I created such a tight routine, such a consistent habit that I used to study and code to 12-14. hours a day. This was the first time in my life that I enjoyed studying so much that all those hours seemed like nothing that could exhaust me, this perseverance continued until February 2021 and things changed after i got my intern as a data scientist for wonderful hospitality. Start-Up: “Upswing Cognitive Hospitality Solutions”.

These are the things I did during my “Zone”(As I like to call it referring to the word psychology) and i think it will help you make your data science and machine learning skills really sharp and useful.

1. Create a map for yourself

Like I told you before it started, I started with deep learning where, However, should have chosen a path from basics to advance, in this way your brain learns step by step and things are understood in a concrete way. Therefore, take your time and discover different directions that are possible with data science, machine learning, etc. There will be many possible paths, but don't be too particular with all of them; only segment the things of your interest (in my case, it was deep learning with data science) in the basics, intermediate, advanced.

Start with the basics and stay focused, first cover all the basic topics of your interest and then solve problems based on those topics without any help. First, you should be comfortable with what you are currently doing and then make a change in terms of the difficulty of the topics.

2. Keep developing other skills besides this one in parallel

Adding 'Scientific’ at the back of ‘Data’ not particularly something you can do after learning few libraries in Python or R or any other data science support language. A data scientist must know how to integrate different technologies to achieve the end result of the problem. What I mean by this is that you must be familiar with databases, Git, Github, deployment related technology, it may be a basic web developer to host your application online or docker to create a container and deploy it in the cloud and so on.

I'm not asking you to learn everything, if your end goal is something other than all of this, discover the things that are required for your goal along with data science concepts and coding skills. One must-have skill that I think every data scientist should focus on is writing, is a basic skill required for a data scientist to create a report at the end of a project for their stakeholders, and submitting that report is one of the most important steps in the entire work cycle for a data scientist.

3. Don't get stuck in the middle

What I mean by this is that everyone has their comfort zone in terms of how they learn things., be it videos or books, etc. But relying on just one form of medium can be restrictive.. There are bright books, absolutely available artworks that you should be interested in reading, even if you like to study from online videos. This flexibility will help you more than you imagine, reading research articles, blogs and everything.

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

4. Socialize

This step is particularly related to increasing your chances of landing internships or even jobs. We can only do so much with our time, and mark us through our work and social relationships, we exponentially increase our chances of being detected and offered an opportunity.

The same thing happened to me, in my fifth semester, I got a score of 96 on the Python end-of-semester exam, so when the company approached some of the faculties of my university, my python teacher recommended me to the teacher in charge and she took a picture with me, after that I gave my interview and was selected as an intern.

5. Learn beyond the ordinary

Keep your investigative side active While learning the concepts, data science, machine learning and deep learning are continuously conducting extensive research in every corner of the world. Therefore, keep a broad mind and learn things beyond the steps of the data science work cycle. I say this because no knowledge you acquire is wasted and the integration of your knowledge from different stages and dimensions of your life makes you who you are today., plus it gives you a unique identity and thought process. Then, use it.

I mention a couple of things that I learned next:

  1. Responsible AI (ethics in AI)

  2. How people perceive different types of visualization (display wheel dimensions)

6. Learn from the best resources

  • video courses:

    • Youtube Channel, freeCodeCamp

    • Coursera Courses:

      • IBM Data Science Professional Certificate

      • Applied Data Science from University of Michigan

      • courses if you are interested in deep learning

      • Data Science AZ en Udemy por Kirill Eremenko

      • IBM Applied Data Science

    • Data Camp: my favorite resource for data science. Explore it to your heart's content, you will love doing and learning data science at DataCamp.

  • Reading courses:

    • Practical statistics for data scientists More than 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 Presentation of Quantitative Information by Edward R. Tufte

    • Data Mining Practical Machine Learning Tools and Techniques by Ian H. Witten y Yew Frank

7. Take proper notes

This point is self explanatory. You can't remember everything you read, do you learn or study. So to do your personal search engine (brain) more efficient and faster taking notes correctly is the best way. You will feel more powerful psychologically every time you see your notes., they represent your hard work, progress and so much knowledge that you have acquired so far.

8. Conquest in steps

You need to feel satisfied with yourself from time to time, to move on and not sincerely let the flame of learning fade. I've seen a lot of people scared or tired or just disinterested in working hard after a while. According to my point of view, This usually happens when you feel that you have not reached the goal and you keep walking without appreciating where you are standing at the moment, how far have you come with your dedication and hard work.

Therefore, try set small goals and once you exceed them, be proud because you are the best version of yourself right now, you don't give up and move on with happiness and satisfaction in mind.

9. Contribute to communities

Like you are studying with many wonderful resources, Why not contribute after a certain point of knowledge and become yourself for one person? The act of sharing knowledge it is not good to keep the flow of new knowledge alive, but also make a name for yourself. These contributions will give you so much importance that nothing else could. Psychologically you will feel really powerful and that would be reflected more in his next job. Keeps the learning process strong and sharpens your overall image as a data scientist or whatever.

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

10. If possible, find a mentor

Good, This is not an easy task, but it is an extension of the previous step of “learn from the best resources”. When you have someone (an expert / or even a person with more experience than you), takes you in the most optimized direction for your learning, you wander less and catch more. The best way is to reach as many people as possible in LinkedIn (Do not beg or irritate them, just be clear and direct with the help you need from them).

11. Believe in yourself

I am mentioning the MOST Important step in the END because even if I understood all the steps mentioned above except this one, you could possibly fail or get lost in so many things that you definitely wouldn't want for yourself. Then, no matter how long it takes, if you are clearing your daily and weekly goals, expanding your network of people,


That was the END of this article, I hope you have learned something for your OWN journey. Share it with me anytime via LinkedIn.

Gargeya Sharma

B.Tech Computer Science (31st year)
Specialized in data science and deep learning
Data Scientist Intern at Upswing Cognitive Hospitality Solutions
For more information, check out my GitHub home page

LinkedIn GitHub

Blog cover photo by Mantas Hesthaven about Unsplash

Zone photo by Paul Skorupskas about Unsplash

The media shown in this article is not the property of DataPeaker and is used at the author's discretion.

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