Data science is the field that is responsible for extracting insights from a huge pool of data. In this data-driven world, you can imagine how important this field is becoming with each passing day. There will be 11.5 million more employment in the field of data scientists by 2026. There are entire teams of recruiters working for tech powerhouses like Google, Facebook, Amazon, and Apple, looking for brilliant, professional, and skilled data scientists. So, if you've acquired the right skills and are about to begin your career in this field, you've made the right choice for your career.
Well, nothing can be more difficult now that you've gained data science skills. But, framing the perfect and impressive resume can still feel daunting, as this resume will be your first indirect communication with the recruiter. And thus, it is important to design a well-formatted resume. Worry not; this blog will be your guide to preparing the perfect data scientist resume. Let's get going toward the blog.
A Step-By-Step Guide Towards Framing A Data Science Resume
The first and basic step is to pre-decide the format of your resume. Deciding the format varies from individual to individual. Based on your qualification and work experience, select the format that is right for you. For a glance, the important elements of any data science resume are skills, education details, contact information, and work qualification. Let's start with the steps.
Steps To Make A Data Science Resume
Step1: Frame Your Contact Details
The first section of your resume of data analyst is the contact information. It'll be easier for recruiters to search for your contact details if you mention them at the utmost top. The important elements of this section would be named and contact info like phone no, email address, headline, etc.
- Keep your address short. Mention city and state only.
- Mention active phone number and professional mail address.
- Focus on creating a professional headline that is placed below your name.
Step2: Data Science Projects
As you might already know, data science is a highly practical domain. Practical experience is as much important as theoretical knowledge. So the important element of the data science resume that is needed to be highlighted is projects and work done. This section will showcase how much capable you're in the field.
If you have done multiple projects, showcase the most relevant one. You can easily include data science projects, machine learning projects, or any coding articles that you've written. You can add the following points:
- Idea/Aim of the project.
- Tools used in the whole project
- Challenges faced during the work.
Step3: Work Experience
If you're an experienced data scientist, then the work experience holds more value instead of the projects section. Make sure to present relevant experience to the field. And make sure the sequence starts from the latest experience. Don't forget to mention the following points:
- Name of company and your job title.
- You work duration. From start to end date.
- Summary of your role and achievements.
If you're fresher, try gaining a little experience in the following ways:
- Perform some freelancing work
- Do mock projects.
- Be active on GitHub and contribute your portfolio.
Step4: Education Qualification
The sequence of mentioned your education qualification is the same as work experience. The latest qualification comes on top of the list. If you don't have a degree related to the field, proceed with mentioning the high school and college details. Include the following details in the section:
- Name of institute
- Degree major
- Time duration or completion year
- Honors and awards if received.
Step5: Mention Your Skills
It's time to mention your skills after mentioning your schooling and employment history. For easy reading, list skills in a bulleted style and, if possible. Add your teachable abilities, starting with the ones you believe are most relevant to the job you're looking for and your data science experience. You should include both technical and soft skills in your list. Go to the position description for which you're applying and compare your talents to the requirements for the position or a related role to increase your chances of getting hired.
Step6: Certificate Section
Above your experience area, list any certifications you own that are essential to the position you're looking for. This will make these crucial abilities stand out in a way that the hiring manager will quickly recognize. You will probably become certified in a variety of programming languages for your position, including Python, SQL, MySQL, and Git. Obtaining a certification that is particular to the subject of data science, such as SAS Certified Data Scientist or Microsoft Certified: Azure Data Scientist Associate, is another option. Mention the below-listed elements in this section:
- Name of certification and name of the organization
- The date you completed the certification
- Skills you learned in the journey
If you don't have adequate skills or certifications, consider taking an online data science course to gain the skill. So, these were the important sections of the data science resume that you must include. With this, let's move on to the guidelines to frame resume for data analyst.
Guidelines To Frame Data Science Resume
Keep The Resume Concise
The biggest challenge that you can face is to keep the resume short and include the maximum information about you. For this, what you can do is mention every relevant detail on a paper. Now work on keeping it concise by selecting the relevant projects, skills, experience, etc. Try to keep the resume within one page. Recruiters often reject long resumes if they receive any.
Basic Template
Remember, the most basic and first step we told you about in this guide was selecting the template. You must select clean looking, elegant format. This will make reading details easier. The fancier the template, the harder it is to make sense of. Also, do you know that recruiters spend an average of six seconds on a resume? If the resume is hard to read, they'll reject yours in no time.
Add Personalization
Customization is the key here. Recruiters receive tons of resumes that are often similar. Many candidates even copy the content of resumes that are available online. The secret is that recruiters can easily identify those resumes. You can personalize and customize your resume content to make a difference in the eyes of a recruiter.
Proofread Your Resume
Before clicking the submit button, make sure you've proofread your resume a couple of times for spelling mistakes and grammar mistakes. There are various online tools to identify these mistakes. You can use these tools to ensure the writing is perfect.
Change Resume According To The Job You're Applying
Don't copy and paste the same resume to every job that you're applying for. Make sure to change certain aspects according to the different job descriptions. For example, you can change skills, and career objectives, according to the job description of a particular job you're applying for. This process will take a little time of yours but will yield positive results.
Avoid long paragraphs
Remember the six seconds rules of recruiter. In a short period, no recruiter will read long paragraphs of the resume. So, to gain HR's attention, make sure to use bullet points, or numbering, so that HR can read your resume at one glance.
Elements To Avoid In Data Science Resume
Data science is a technical field, and one should avoid putting elements that are not related either to the job or to the field at all. Check the below-mentioned elements that you should avoid in your resume for a data analyst:
Random Summary Or Career Objective
The first thing data scientists shouldn't include in their CV is a generalized, unrelated summary or objective. A more job-specific statement or objective will provide you an advantage and stop recruiters from looking away from your profile.
Irrelevant Projects
Data scientists should make a CV that sticks out in the recruiters' minds to stand out among the thousands of resumes they see every day. Including projects and experiences that are distinct from others and relevant to the position you're looking for can help you achieve this. Thus, include the projects which will demonstrate your expertise, knowledge, and problem-solving skills. Make sure to exclude these couple of points mentioned above. Let's proceed to wrap up this article.
Conclusion
The demand for data scientists has unquestionably grown more than ever. Better technology necessitates the hiring of trained data specialists who can mine this amount of data for useful insights. The crucial query is HOW? One of the most important needs for your next job hunt as a data scientist is a data science internship resume if you're under qualified or a data scientist resume to get the job. Getting a job as a data scientist is undoubtedly beneficial for grabbing prospects for the future and beyond.
Even though it could seem nearly hard to develop a powerful CV, everyone should make the resume differentiate themselves from other applicants to land a job. You now realize that it's not that difficult. You will create the ideal CV if you keep in mind all you learned from today's blog.
