Data scientist vs spectral scientist

The differences between data scientists and spectral scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both a data scientist and a spectral scientist. Additionally, a spectral scientist has an average salary of $106,296, which is higher than the $106,104 average annual salary of a data scientist.

The top three skills for a data scientist include python, data science and visualization. The most important skills for a spectral scientist are DOD, hyperspectral imaging, and erdas.

Data scientist vs spectral scientist overview

	Data Scientist	Spectral Scientist
Yearly salary	$106,104	$106,296
Hourly rate	$51.01	$51.10
Growth rate	16%	16%
Number of jobs	106,973	41,842
Job satisfaction	-	-
Most common degree	Bachelor's Degree, 51%	Master's Degree, 40%
Average age	41	41
Years of experience	4	4

Data scientist vs spectral scientist salary

Data scientists and spectral scientists have different pay scales, as shown below.

	Data Scientist	Spectral Scientist
Average salary	$106,104	$106,296
Salary range	Between $75,000 And $148,000	Between $68,000 And $164,000
Highest paying City	Richmond, CA	-
Highest paying state	California	-
Best paying company	The Citadel	-
Best paying industry	Start-up	-

Differences between data scientist and spectral scientist education

There are a few differences between a data scientist and a spectral scientist in terms of educational background:

	Data Scientist	Spectral Scientist
Most common degree	Bachelor's Degree, 51%	Master's Degree, 40%
Most common major	Computer Science	Physics
Most common college	Columbia University in the City of New York	Northwestern University

Data scientist vs spectral scientist demographics

Here are the differences between data scientists' and spectral scientists' demographics:

	Data Scientist	Spectral Scientist
Average age	41	41
Gender ratio	Male, 79.6% Female, 20.4%	Male, 100.0% Female, 0.0%
Race ratio	Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%	Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage	9%	9%

Differences between data scientist and spectral scientist duties and responsibilities

Data scientist example responsibilities.

Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
Perform cross-validation-test on linear regression model of data using scikit-learn.
Develop python base statistical visualization to provide insights of fuzzy social media data.

Spectral scientist example responsibilities.

Develop and implement tools in both IDL/ENVI and Erdas to automate preprocessing and enhancement of imagery data for exploitation and interpretation.
Require to acquire geospatial information and extract essential elements from a wide array of multi-intelligence data to include Sigint and Masint.
Assay results are used to screen compounds to ensure that high quality candidates are selected for subsequent pharmacodynamic and pharmacokinetic experiments.

Data scientist vs spectral scientist skills

Common data scientist skills

Python, 13%
Data Science, 10%
Visualization, 5%
Java, 4%
Hadoop, 4%
Tableau, 3%

Common spectral scientist skills

DOD, 74%
Hyperspectral Imaging, 9%
Erdas, 9%
MASINT, 8%

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Updated January 8, 2025

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