A resume summarizes your entire work and education history and helps you secure a job. This is why it is a very important part of your professional life and you can not compromise it for anything else!
These are the 7 quintessential factors one must include to devise the Perfect 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 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.
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