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Computational biologist vs resource biologist

The differences between computational biologists and resource biologists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 6-8 years to become both a computational biologist and a resource biologist. Additionally, a computational biologist has an average salary of $61,449, which is higher than the $55,582 average annual salary of a resource biologist.

The top three skills for a computational biologist include python, machine learning and next-generation sequencing. The most important skills for a resource biologist are GIS, water quality, and GPS.

Computational biologist vs resource biologist overview

Computational BiologistResource Biologist
Yearly salary$61,449$55,582
Hourly rate$29.54$26.72
Growth rate17%1%
Number of jobs12,8398,041
Job satisfaction--
Most common degreeBachelor's Degree, 53%Bachelor's Degree, 90%
Average age4040
Years of experience88

Computational biologist vs resource biologist salary

Computational biologists and resource biologists have different pay scales, as shown below.

Computational BiologistResource Biologist
Average salary$61,449$55,582
Salary rangeBetween $38,000 And $99,000Between $35,000 And $87,000
Highest paying CitySan Francisco, CA-
Highest paying stateAlaska-
Best paying companyGoogle-
Best paying industryHealth Care-

Differences between computational biologist and resource biologist education

There are a few differences between a computational biologist and a resource biologist in terms of educational background:

Computational BiologistResource Biologist
Most common degreeBachelor's Degree, 53%Bachelor's Degree, 90%
Most common majorBiologyBiology
Most common collegeHarvard UniversityUniversity of California, Berkeley

Computational biologist vs resource biologist demographics

Here are the differences between computational biologists' and resource biologists' demographics:

Computational BiologistResource Biologist
Average age4040
Gender ratioMale, 80.2% Female, 19.8%Male, 58.8% Female, 41.2%
Race ratioBlack or African American, 2.7% Unknown, 5.1% Hispanic or Latino, 7.4% Asian, 17.0% White, 67.2% American Indian and Alaska Native, 0.6%Black or African American, 2.3% Unknown, 4.9% Hispanic or Latino, 6.3% Asian, 11.1% White, 74.7% American Indian and Alaska Native, 0.6%
LGBT Percentage10%10%

Differences between computational biologist and resource biologist duties and responsibilities

Computational biologist example responsibilities.

  • Develop UNIX shell script, PERL and AWK program and apply TECPLOT software macro files to automate data processing and report.
  • Manage detailed GLP laboratory manual daily.
  • Design and perform model-based gene expression analysis with whole genome tiling-microarray data sets using SAS.
  • Generate QC report and plots for new RNASeq samples using BAM files, PICARD, SAM tools etc.
  • Foster collaborations both within USDA and with external scientists.
  • Breed the mice, perform genotyping, injection and operation.
  • Show more

Resource biologist example responsibilities.

  • Manage detailed GLP laboratory manual daily.
  • Analyze MBSS ecological data against other GIS data sets, e.g., calculate land use/ land cover for all MBSS sites.
  • Gather GIS data, photographs and compile daily reports of field observations make while working with and advising cleanup crews.
  • Project require review of existing environmental data, identification of permitting requirements, wetland identification/delineation and sensitive and endanger species issues.
  • Implement multiple assays to assess plasma biomarkers to support decision-making studies of hypertension benchmark compounds and RAAS pathway new targets.

Computational biologist vs resource biologist skills

Common computational biologist skills
  • Python, 24%
  • Machine Learning, 13%
  • Next-Generation Sequencing, 9%
  • C++, 6%
  • Visualization, 5%
  • Biological Data, 5%
Common resource biologist skills
  • GIS, 34%
  • Water Quality, 23%
  • GPS, 13%
  • Species Surveys, 13%
  • Biological Data, 11%
  • Boats, 7%

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