How to become a data analyst? Our machine learning computers have scanned through the job descriptions of hundreds of recent job posts to identify the following:
- tasks data analysts are expected to do
- sought after data analyst skills and experience
Within your cover letter, resume, or CV, highlight these skills, experience, and qualification keywords to attract the attention of potential recruiters, hiring managers, and CV scanning BOTS.
Having a good understanding and highlighting these skills in your resume will help you get noticed. Most hiring managers today rely on recruiters or BOTS to scan through resumes before even meeting a potential candidate. These recruiters may not have extensive knowledge of the role and may pick out candidates based on key topics that they can find. Having these skills listed and emphasized may help you stand out and get selected.
What are data analysts expected to do and know? #
Companies are seeking data analysts with an understanding of the following topics.
Click on the sections to copy the keywords and helpful descriptions to paste and modify on your cover letter or resume.
Main Data Analyst Experience and Skills #
Use these descriptive experience keywords to make your CV stand out
reporting management insights systems analytics project operations strategy databases statistics procedures Computer Science visualizations IT modeling business processes data science presentations automation communicating large data sets data management optimization statistical analysis risk
Successful Data Analyst traits to emphasize in your resume/CV #
Besides experience and qualifications, recruiters also look out for key behavioral traits. Below is the list of key traits that recruiters for data analysts are seeking. Upon reviewing the list, here are some areas that you should try to emphasize. Click the section and copy, paste, and edit your cover letter or CV.
Upon reviewing the list, these are the top listed sought after skills and traits:
communicate multi-task collaborate interpret lead absorb detailed information apply statistical analysis influence leverage multiple tools meet deliverable timelines optimize queries perform statistical analysis present data prioritize workload synthesize ability in using databases data sources ability analyse large datasets ability facilitate workshops absorb business requirements aggregate large data sets analyze situations anticipate new data problems apply new technologies apply technical knowledge articulate issues assimilate new methodologies balance business priorities breakdown complex ideas ability breakdown processes build consensus