Get Shortlisted with an Impressive Data Science Portfolio

Published in

VisionNLP

6 min readFeb 10, 2022

Worried about How to build your Resume for Data Science profile??
Most of the students/Data Science enthusiast are quite skilled in their skills, but always gets demotivated as their resumes doesn't get selected!!!

Do you know why your resumes is not getting shortlisted????

In this article, I am going to share important tips that will increase your chances of success in a data science job resume screening process.
Now don’t get anxious about not getting shortlisted, focus on Interview preparation and get your dream job early…

As a Data Scientist you should have following amazing profiles, so that anyone would get impressed.

1. Hackerrank

HackerRank is 100% free to use for developers who is trying out coding questions to prepare for interviews. Most of the companies do use HackerRank for technical tests, this means that familiarising yourself with the HackerRank platform is an important step in the interview process.
1. coding challenges and practice questions
2. Job board
3. certification challenges
4. regular coding contests with a competitive ranked ladder.
HackerRank coding challenges and their practice questions can help you prepare for your data science interview. And additionally you can put your HackerRank profile link in your resume to impress hiring managers.

2. Kaggle

Kaggle is my favourite Data Science portfolio. Kaggle is a great place to learn and master data science skills. As a fresher its one of the best opportunity for you to participate in real-world data science projects/competitions and try to achieve best rank. Better rank in kaggle competition plays major role in your profile, with this amazing success you can easily attract many companies.
Kaggle competitions are not only about the money. They are about doing super cool stuff, collaborating with great people and learning a lot of things.
1. Free Courses and free certificates availabl -For your free Kaggle certificate, completion of all the tasks and exercises is necessary.
2. Participate in Kaggle competitions
3. Create a notebooks and share knowledge with others
4. kaggle provides Free GPU’s to train you models in faster way.

3. GitHub

Having a good GitHub portfolio plays a very important role. Projects based on the Titanic or Credit Card datasets can’t even get you an internship role. So, please spend enough time identifying some of the right projects. A good project will not only help in your learning but also with your job search.
There is so much data out there. Many government agencies and organisations are publishing their datasets. Social media is full of unstructured data with a lot of useful information. It is now easy to come up with a unique project for your portfolio.
1. Build project add details on public GitHub profile that would again help a lot to get shortlisted.

4. LinkedIN

Best LinkedIN profile??? is it really not enough to have above profiles only? You might be wondering where to focus, what are different things I really need to do to get a better job??? Don’t get distracted or demotivated. Let’s understand importance role of amazing LinkedIN platform.
LinkedIN Profile tips:
1. Add appropriate headline, Add about me sections and describe yourself
2. As a Fresher → In Experience Section add following points:-
2.1 Kaggle Data Scientist
2.2 Join unpaid internship and add those experience too
3. Add certifications — there are many free certifications are available, learn everyday with free certifications
4. Skills Section: This is most important part of your profile, add as many as skills you can add in your profile.
5. Describe your projects properly.

Additional LinkedIN tips:
Share daily or weekly post on Data science, Python or Machine learning this will boost your profile for sure. Following things you can share
1. Data Science or python cheat-sheet
2. Interview Questions
3. Small concepts
4. Your Kaggle, GitHub or HackerRank portfolio achievements/work
Here your main goal is engage with Data Science community and impress hiring managers or other Data Scientists. People will start following you and will ask you for collaborations or even they will share Data Science opportunities with you.

Resume

Now Lets talk about Resume tips : )
I have been evaluating around 2000+ resumes since last 2–3 months for various levels and positions in Data Science Domain. The most important thing is your resume should not be under-representing or over-representing.

Basic observations were
:- Freshers having 2+ pages resume
:- Skills and projects were not updated/highlighted properly (even not mentioned)
:- 2 column resume
:- Fancy coloured and heavily designed resume
:- No contact details
So basically you won’t be able to crack even the resume screening round of many tech companies.
But why?
:- Recruiter will have 1000 of resume and will only spend 4–5 second on each resume (multi page resumes are eliminated)
:- If the recruiter uses a tool to filter resume then it may mess up in multi column resumes and ultimately you will be rejected( Tools may go in left to right direction and may mix different fields — not all the tools are smart enough to understand two column resumes).
:- Shortlisting is done based on skills and projects and if they are not highlighted properly again you may fail.

How to make your resume stand out of crowd:

Basic Things:
:- Prepare your resume in pdf format, so that the recruiter sees the resume the same way you see it.
:- Making use of a good resume template
:- Making use of bullet points and and highlighting/bold important points
:- having a consistent format
:- Avoiding typographical errors
:- Make it one page.
:- Make it single column so resume parsing tools won’t reject you.
:- No one is interested in your photo/ hobbies/ 10th and 12th marks / blah blah…..
:- Keep your contact details in top(email and phone)
:- Add all profile links(LinkedIN, Kaggle, GitHub, HackerRank etc.)
Important Things:
:- Keep your skills section highly rich with keywords relevant to job you are applying.
:- For projects/prev work experience try to highlight what things you achieved(performance improvements, awards)
:- Add relevant keywords for the role you are applying for.
Eg: Use machine learning algorithms names / data science / python in multiple places if you’re applying for data science roles.
:- Make use this formula:
Accomplished “X” as measured by “Y” by doing “Z”
Let’s understand this concept with an example,

“Built a credit card fraud detection system”
this is a simple statement, not attractive at all because it doesn’t exactly mention the impact of the use case. We can try to improve it by including details of its impact by using below the statement:

“Built a credit card fraud detection system (X) that helped to improve the customer engagement (Y) on the platform by using Logistic Regression Algorithm and ensemble predictive model(Z)”

Is there anything I have missed or said wrongly please comment down below or add your suggestions.

Let’s talk about why Portfolio is most important thing to do???

Getting a data science job is becoming very competitive, though the number of opportunities is historically high the number of people applying for these jobs is extremely high as well, as everyone is moving towards Data Science.
For example, below is a screenshot of a job posting from LinkedIn, this job posting has a total of 200+ applications within 20 hrs only. So you can understand why your resume plays a critical role in getting shortlisted.