Data miner vs office specialist

The differences between data miners and office specialists can be seen in a few details. Each job has different responsibilities and duties. While it typically takes 2-4 years to become a data miner, becoming an office specialist takes usually requires 1-2 years. Additionally, a data miner has an average salary of $80,042, which is higher than the $33,477 average annual salary of an office specialist.

The top three skills for a data miner include python, R and ETL. The most important skills for an office specialist are customer service, data entry, and patients.

Data miner vs office specialist overview

	Data Miner	Office Specialist
Yearly salary	$80,042	$33,477
Hourly rate	$38.48	$16.09
Growth rate	19%	-5%
Number of jobs	68,035	98,760
Job satisfaction	-	-
Most common degree	Bachelor's Degree, 65%	Bachelor's Degree, 44%
Average age	44	47
Years of experience	4	2

Data miner vs office specialist salary

Data miners and office specialists have different pay scales, as shown below.

	Data Miner	Office Specialist
Average salary	$80,042	$33,477
Salary range	Between $54,000 And $118,000	Between $25,000 And $43,000
Highest paying City	San Mateo, CA	Washington, DC
Highest paying state	California	Massachusetts
Best paying company	Oracle	Brookhaven National Laboratory
Best paying industry	Real Estate	Finance

Differences between data miner and office specialist education

There are a few differences between a data miner and an office specialist in terms of educational background:

	Data Miner	Office Specialist
Most common degree	Bachelor's Degree, 65%	Bachelor's Degree, 44%
Most common major	Business	Business
Most common college	Stanford University	Western Carolina University

Data miner vs office specialist demographics

Here are the differences between data miners' and office specialists' demographics:

	Data Miner	Office Specialist
Average age	44	47
Gender ratio	Male, 60.1% Female, 39.9%	Male, 18.3% Female, 81.7%
Race ratio	Black or African American, 6.2% Unknown, 4.7% Hispanic or Latino, 8.8% Asian, 12.0% White, 67.7% American Indian and Alaska Native, 0.5%	Black or African American, 8.1% Unknown, 4.9% Hispanic or Latino, 19.8% Asian, 8.4% White, 57.7% American Indian and Alaska Native, 1.2%
LGBT Percentage	11%	6%

Differences between data miner and office specialist duties and responsibilities

Data miner example responsibilities.

Provide clinical staff with customize population health analytic solutions for tracking specific clinical quality measure and managing high risk patients.
Develop Matlab code for exploring the dichotomy in differential performance of Satscan using exact locations and census tracts.
Train a logistic regression classifier to differentiate the positive and negative articles.
Develop single and multiple regression models to identify relationships between stock volatility and financial ratios
Develop a hierarchical text classification model in python to classify websites into multilevel/ multiple taxonomy.
Develop discriminative features to boost the classification accuracy, using the domain specific language written in Java.

Office specialist example responsibilities.

Provide support for enrolling patients into health coverage programs and manage sensitive patient information to ensure accuracy and confidentiality.
Manage and maintain patients' medical records by utilizing computer applications management database processing system to ensure county compliance.
Manage operational FedEx relationship through order fulfillment, shipment tracking, and communication with parties involve in shipment transaction.
Provide assistance to office staff including preparing and proofreading memos and grants, transcribing meeting minutes for staff and answering phones.
Perform multiple bookkeeping duties including departmental payroll, print financial reports and other financial material as needed.
Communicate HIPAA compliance to staff, field and outside agencies.

Data miner vs office specialist skills

Common data miner skills

Python, 31%
R, 15%
ETL, 8%
Data Quality, 7%
HTML, 7%
SAS, 7%

Common office specialist skills

Customer Service, 18%
Data Entry, 10%
Patients, 9%
Office Equipment, 6%
Phone Calls, 6%
Word Processing, 4%