Résumé Preparation Tips

1. Look and Feel


  • End each sentence should with a period(.)
  • Margins should be 0.75 to 1 inches.
  • Check for spelling mistakes in English words. For technical keywords, protect the Word document as read only before distributing to hide the red and green squiggly lines.
  • Resume should be between 2 to 4 pages based on the experience.
  • Avoid images


  • For headings use serif fonts 12pt like Times new roman, Garamond, Georgia, Goudy Old Style
  • For body use sans serif fonts 10pt like Calibri, Arial, Tahoma, Century Gothic, Lucida Sans
  • Don’t try to use fancy fonts. Readers may not have them in their system

2. Content


  • Start with a verb – avoid 1st person pronouns (I, we) and 3rd person pronouns (he, she)
  • Avoid articles that crowd sentences (a, an, the) e.g., use ‘trained staff‘ instead of ‘trained the staff‘.
  • Helping verbs (have, had, may, might): Helping verbs weaken claims and credibility — implying that your time has passed and portraying you as a job-hunting weakling. Say ‘managed‘ instead of ‘have managed‘.
  • Being” verbs (am, is, are, was, were): Being verbs suggest a state of existence rather than a state of motion. Try “monitored requisitions” instead of “requisitions were monitored“. The active voice gives a stronger, more confident delivery.
  • Shifts in tense: Use present tense for a job you’re still in and past tense for jobs you’ve left. But, among the jobs you’ve left, don’t switch back and forth between tenses. Another big mistake: dating a job as though you’re still employed (2008-Present) and then describing it in the past tense.
  • Complex sentences: Unless you keep your sentences lean and clean, readers won’t take time to decipher them.
    • Process this mind-stumper:
      • Reduced hospital costs by 67% by creating a patient-independence program, where they make their own beds, and as noted by hospital finance department, costs of nails and wood totaled $300 less per patient than work hours of maintenance staff.
    • Eliminate complex sentences by dividing ideas into sentences of their own and getting rid of extraneous details:
      • Reduced hospital costs by 67%. Originated patient independence program that decreased per-patient expense by $300 each.
  • Overwriting: Use your own voice; don’t say expeditious when you want to say swift.


  • Accomplishment-oriented resume packs much stronger punch than a responsibility-oriented resume. Recommended keywords: persuaded, influenced, contributed to, participated in, or helped out with.
  • Quantifiable results: Don’t just say ‘reduced cost‘ or ‘improved efficiency‘. Quantify them with technical terms like: seconds of latency, number of bugs or prod incidents, or even an algorithmic improvement in big-O time.
  • Targeted resumes: Tailor your resume according to the role and company you are applying to. e.g., if you’re applying for a technical lead position after years of being a software engineer, you’ll want to mention the time that you led the design of a new feature.
  • Avoid long sentences: Make every bullet point around 1 or 2 lines.
  • No outdated skills under technical skills.
  • Summary/Value Statement: State the role you are applying for and what values you bring to job; Explain why you should be hired.
  • Core Strength / Technical skill summary:
    • comes before chronological project experience.
    • No outdated skills
    • Avoid crowding with too many technical skills.
    • Avoid obvious ones like MS Office, Windows, etc.
  • Experience:
    • Accomplishments, not responsibilities
    • Highlight company names more than the title, project title and the duration.
    • Ensure no gaps in experience timeline.
    • A résumé does not need to be a complete employment history. Do not go more than 10 or 15 yrs back.
    • Reduce the older job descriptions to 2 or 3 lines
  • Always prepare your resume in both Microsoft Word & PDF versions. Some automated systems prefer Word.

3. References


7V’s of Big Data – Briefly

  1. Velocity – speed at which data is created currently is almost unimaginable. e.g., 2.5 million Google queries per minute
  2. Volume – enormous amount of data generated. e.g., airplanes generate 2.5 billion TB of data each year from the sensors installed in the engines.
  3. Variety – Data today comes in many different formats: structured data, semi-structured data, unstructured data and even complex structured data. e.g., Facebook, Twitter, etc.
  4. Veracity – Trust-worthy data. Analysis performed would be useless unless the data is accurate.
  5. Variability – Meaning of the same data could be different based on the context. e.g., Same tweet words can have different meanings and sentiments.
  6. Visualization – Visual representation of analysed data in a comprehensible way.
  7. Value – Data in itself is not valuable at all. The value is in the analyses done on that data and how the data is turned into information and eventually turning it into knowledge.

More detailed explanation: http://www.bigdata-startups.com/3vs-sufficient-describe-big-data/