This article was published as a part of the Data Science Blogathon.
Introduction
In just a year of learning data science, I have received multiple job offers and opportunities from all over the world. As for now, I’m currently doing an internship with one of the largest data analytics companies in the country.
In this article, I am going to tell you how I did it. I am going to clearly explain the steps you can take to make yourself stand out from other applicants, and land any data science job you want.
Step-by-step Process
Step 1: Create Data Science Projects
You may have heard this advice over a hundred times, and it is a no-brainer. Create data science projects to put on your resume to make it stand out from others.
When you create and showcase projects you are passionate about, it shows that your interest in the subject goes beyond the classroom.
Anyone can do an online course. But not everyone will take an interest in building projects based on what they learned from the course. That takes a lot of dedication, hours of coding, and staring at a computer screen.
This initiative that you take to go the extra mile will make you stand out from other job applicants, and you will already be one step closer to getting the job you want.
What kind of projects should you showcase?
Before telling you the type of projects you should showcase, I am going to give you a few examples of projects you should never put on your resume.
These include — the Titanic Challenge on Kaggle, the Iris Flower Classification Dataset, and the Boston House Pricing Dataset.
All these datasets are incredibly common, and there are hundreds of available solutions and tutorials out there already.
What you should do instead, is to create projects you are passionate about. Pick a topic you are interested in, and create a data science or analytics project around it. Tell a data story around your project, and show how passionate you really are about what you do.
For example I like watching movies. A lot. I decided to create a movie recommender system that would allow a user to enter a movie name. The system then provided similar movie suggestions to the user. As a part of the same project, I also combined a variety of datasets and made a movie dashboard, which allowed users to find movie ratings and reviews easily.
In one project, I displayed the following different skills — data collection, data cleaning, data analysis, data visualization, and machine learning.
If you are looking to enter the data science job field, I suggest you do the same. Portray your skill in not just one, but many different areas. This is because data science jobs often require you to have skills in data cleaning and analysis as well.
Step 2: Build Your Portfolio
After you create data science projects in your area of interest, you need one place to showcase all your projects.
I suggest creating your own portfolio website to do this. You can write a brief explanation of each project you created and put in colorful pictures. You can also add links to your Github repository.
This is a lot better than just putting up links to your code or Github page.
A large majority of recruiters are non-technical people and don’t understand the codes that sit in your Jupyter notebook.
When you create a website showcasing your projects, explain it in layman and non-technical terms. This way, anybody who comes across your profile will understand what you do, and what your projects are about.
In your portfolio site, you should also add sections talking about yourself, your educational background, skills, and job experience.
When you display all of this along with your projects in one site, it looks a lot better than a regular resume with a Github repository link that would look. It also will look like you put in a lot more effort and time into building your portfolio, which will immediately make you stand out from other job applicants.
Step 3: Communication is Key
Make sure to communicate all the work you’ve done in a clear and concise way.
Your portfolio is a place to tell your story, write about your experiences, and showcase your passion. Do it in a way that is clear, and easy to understand by anyone who comes across it.
Tell stories about your data science projects. If you have an interesting story about a project or any exciting data findings, make sure to highlight them.
Sometimes, even failed projects can make interesting stories.
Did you try creating a data science project but was unable to complete it because the data was flawed?
Tell a story about it.
People like stories, and it will make the work you do seem so much more interesting.
Another way to communicate the work you do is through blogging. There are many data science publications on the Internet that allow you to share your work and accept data science stories.
You can write about your experience, your data science journey, or your data science projects. You can publish these stories, and slowly build your network as people read your work.
You can also link your articles to your portfolio site, which is what I usually do. I have had many people reach out to me with career opportunities through my writing.
Enjoy what you do!
As long as you are passionate about the work you do, you will definitely land a job in the field. Passion and creativity go together, so just enjoy what you do, and let your work speak for itself.
Good luck in your data science journey, and happy learning!
