What to include in a Data Science Resume?

When it comes to landing the data science job of your dreams, you only get one
opportunity to make a first impression. You need to make it count!
How can you do that? It’s simple: A concise and targeted data science resume. When written effectively, this document sets you apart from other candidates and conveys
exactly what you can offer and why.
These are the 7 quintessential factors one must include to devise the Perfect Data Science resume.

1. Contact information
Be sure to include your full name, credentials, address, phone number, and email address. If you have a professional presence on LinkedIn, Twitter, GitHub, or another social media site, consider providing links to those as well. Make it as easy as possible for hiring managers to get in touch with you.

2. Professional summary
Your professional summary should be no more than three or four sentences stating the
following:

• Years of experience
• Primary areas of data science expertise (e.g., data mining, data warehousing, or data visualization)
• Type of position you seek (e.g., data analyst, data scientist, or data engineer)
• Industry in which you hope to apply your data science knowledge (e.g.,

healthcare, retail, government, or finance)
The professional summary should tell your story—where you’ve been and where you’re headed next. Think of it as your elevator speech—that is, the way in which you’d describe yourself and your abilities in 30 seconds or less.
This is your chance to grab a hiring manager’s attention and encourage him or her to keep reading, so make it worthwhile.

3. Core competencies
This section should include a bullet-point list of your data science strengths (e.g., statistical analysis, data interpretation, and communication) and explain generals tasks you’ve completed to achieve each competency.

4. Education
List each degree and institution as well as the date of graduation. Consider listing any courses that are particularly relevant to the job for which you’re applying. Tie your education experience as much as possible to the job you seek.

5. Technical expertise and certifications This section should include a bullet-point list of specific data science skills you’ve honed as well as tools with which you’ve worked.

If you earned a data science master’s degree, list that you have hands-on experience with data science tools such as SQL Server and Tableau and languages such as R and Python For example: A certification in Data Science from Board Infinity proves to employers that you have the wide range of knowledge and skills needed to do any data science job.

Don’t list tools for which you only have a theoretical knowledge.  Hiring managers want to know that you have experience working with these tools and will likely ask you for examples during an in-person interview.

6. Work experience
Don’t worry about keeping your resume to one page—that’s an outdated notion. If you’re a mid-level or above professional, you’ll likely need two pages to list all work experience. And never skip significant job experiences, even if they’re unrelated to the job to which you are applying. A gap of time between jobs is a red flag to hiring managers. For unrelated job experiences, don’t include as much detail on your resume as you would for other jobs.

Also focus on key accomplishments in each previous position by listing a select group of three to five projects and briefly describing your role in each project’s success. Use action words (e.g., created, managed, coordinated, or led) to describe your participation.

7.Make Your Data Science Resume Keyword-Friendly for Hiring Managers A strong data science resume also incorporates keywords for which hiring managers are searching. This will vary from job to job, and a good rule of thumb is to incorporate as many of the same terms and phrases used in the job posting directly into your resume.