Your Resume is Key to your Data Science Job Application

Are you applying to data science jobs but not receiving any phone calls for the position you want? This is among the biggest gripes aspiring data science professionals have. Regardless of the role you’re applying for (data scientist, data engineer, data analyst, etc.) – clearing that first hurdle is a significant obstacle.

If you find yourself in a similar position, recruiters might be passing over your resume.

And this is the single most important aspect of landing a data science interview. A poorly crafted resume or too many irrelevant details will land your resume in the rejection pile. It’s true - a resume might not guarantee you your dream data science role, but it can definitely break your application.

Here’s the good news – crafting the perfect data science resume is a skill you can learn! Once you know how to expertly update your resume, you’ll be able to effectively market your skills when applying for your next data science job.

We have poured through hundreds and hundreds of data science resumes over the years and have noticed certain common patterns that jump out. And it’s critical to pay heed to these patterns as you design your own data science resume. We aim to bring that out in this short course through the medium of tips and tricks.

Key Questions To Answer:

There are certain questions beginners ask when building their data science resume:

  • What is the significance of building an effective resume in the data science field?
  • As a fresher, how can I make my resume stand out for data science roles?
  • Which aspects of a resume should I pay attention to while applying for a data science role?
  • Is it enough to only an impressive and impactful resume when applying for data science jobs?
  • What kind of data science projects should you add in your resume?
  • Are data science resumes role-specific?
  • You have professional experience but not in data science. How should you approach building a data science resume?
  • Does your resume need to have a degree in data science or related fields?
  • Are there any specific tools or frameworks you should highlight in your data science resume?

Who is the “Tips and Tricks to Build an Awesome Data Science Resume” course for?

This course is for anyone who:

  • Is looking for guidance on how to build an impactful data science resume
  • Wants to apply for data science roles but isn’t sure how to craft a resume for that
  • Wants to understand how data science resumes are built
  • Is curious about gaining some industry-relevant tips and tricks for building resumes!


What do you need to get started with the “Tips and Tricks to Build an Awesome Data Science Resume” course?

Here’s what you’ll need:

  • A working laptop/desktop with 4 GB RAM
  • A working Internet connection
  • And of course, an enthusiasm for wanting to land a data science role!

Common Questions Beginners Ask About Data Science Resumes

We saw these questions earlier. Let’s quickly go through them one by one.

What is the significance of building an effective resume in the Data Science field?

A great resume is your first stepping stone to enter the ata science industry. Think of yourself as a brand. Your resume is the best way of advertising your brand in front of companies, industry leaders, and the recruiting managers.


As a fresher, how can I make my resume stand out for Data Science roles?

The key here is to add as many projects as possible. If your data science projects are an academic part of your coursework, they need to be present in your resume. Moreover, the projects you list and the skills you have mentioned need to be consistent with each other.


Which aspects of a resume should I pay attention to while applying for a data science role?

Just like technical roles, your resume should have the following sections at the least:

  • Skills
  • Education
  • Relevant Project/Work Experience.


In the Data Science field, is it enough to only have an impressive resume?

While your resume might be impactful on paper, it needs to be backed up with an active Github account where most of your projects can be seen, and an always updated digital profile.


What kind of Data Science projects should I add in my resume?

Any Data Science project where you made predictions on a given dataset, used preprocessing techniques, and achieved a good evaluation metric score can be mentioned. These projects may include any

  • academic/research/personal projects you undertook
  • Projects from your previous employment
  • Projects from hackathons or competitions


Are Data Science resumes role-specific?

In short, yes. Different industries apply machine learning, and Data Science differently. Nowadays there are even niche roles for specific domains like Computer vision or Natural Language Processing. So while you don’t need a different resume for each position, you can change the order of projects and skills depending on the role and job description.


I do have professional experience, though not in data science. How should I approach building my resume?

It is always good to mention all the work experience you have had previously. If you have unrelated professional experience and your experience in Data Science is from a  course/certification you undertook, then you can mention all the projects you implemented in the course. 


Does my resume need to have a degree in data science or a related field?

Not at all - it is a myth that you need a professional degree to land a Data Science role. There are multiple online courses and certifications - both paid and free that you can enrol and learn from. The key to building a good Data Science resume is your project portfolio


What tools and frameworks should I add in my resume for a DS role?

You would need to list either Python or R in the languages section. Depending on the languages, you can also include the libraries you have worked with, and the platforms you have used - like Apache Spark, Rstudio, Anaconda, etc.

Course curriculum

  • 1
    Building an Effective Data Science Resume
    • Introduction
    • Structure of your Resume
    • Adding Information to the Resume
    • Get Feedback from Industry Experts
  • 2
    Enhacing your Data Science Profile
    • Participate in Data Science Competitions
    • Building your Digital Presence
    • Create a GitHub Profile
    • Write Blogs
    • Create and Optimize your LinkedIn Profile
  • 3
    Next Steps...
    • What next?
    • More resources


  • Analytics Vidhya

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Common questions related to the course

  • Who should take the course?

    This course is for anyone who is applying for data science roles. So whether you are a fresher or a transitioner in data science, this course will provide you with a few key tips and tricks to hash out your resume.

  • Do I need to have any technical expertise to take this course?

    Absolutely not. This course focuses purely on the resume part of your data science journey. If you’re looking to learn data science and machine learning algorithms, we recommend taking the ‘Applied Machine Learning’ course. If you’re looking for other aspects of your data science career, you should check out the ‘Ace Data Science Interviews’ course.

  • What is the fee for the course?

    This course is free of cost!

  • How long would I have access to the course?

    Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enrol in the course again. Your past progress will be lost.

  • How much effort do I need to put in for this course?

    You can complete the course in a few hours.

  • I’ve completed this course and have a good grasp. What should I learn next?

    The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Ace Data Science Interviews” course.

  • Can I download the videos in this course?

    We regularly update the “Tips and Tricks to Your Build Data Science Resume” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.

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