6 Must-Haves for Your Data Science Resume

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With all of the recent talk and tech articles about how hot data scientists are in the job market, many data pros in a job now might be getting a bit restless.

But technologists that have been out of the job market for any length of time can easily forget the basics of a successful resume in their area of specialty. To help ensure that the data professional hits the right notes, senior recruiter Patrick Circelli of Mondo, an IT recruiting firm, offers the following advice.

“Data Science is all about mathematics, so having that type of degree -- mathematics, information science, computer science, etc. -- is especially key for these roles. Hiring managers really love that,” Circelli stresses.

“Within their career, they should speak about the types of data mining they have done, as well as their understanding of how data operates,” Circelli continues. “This could be keywords like "pattern recognition", "predictive analysis", or "visualization".

“For someone with a higher level role, there may be larger, enterprise scale, data warehouses and "big data" environments that they've operated within, and their former computer programming skills in SAS and R may not come into play as often as they once did when they started in analytics,” Circelli says.

Other areas of interest on an prospective candidate's resume would be an extensive grasp of Microsoft Excel (i.e. utilizing their features such as v-lookups or pivot tables), as well as experience in writing SQL queries and stored procedures in various SQL databases, Circelli notes

To recap the must-haves for the data pro resume, Circelli cites the following items:

1. Degree in Mathematics, Computer Science, Information Science, etc.

2. Extensive skill in Excel and SQL queries/stored procedures (v-lookups, pivot tables)

3. Programming in SAS or R, or even Python, Java, and C++

4. Data operations ("pattern recognition", "predictive analysis", or "visualization") - may come with tools like Tableau

5. Database environments - NoSQL databases (HBase, CouchDB, MongoDB)

6. For higher level skills - data warehouses

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