Data Scientist
Competency-based
ONET: 15-2051.00
2
Years
16
Skills
65h
Related instructions
Classroom instruction topics
- Introduction to Geographic Information Systems (GIS)
- Programming Fundamentals I
- Cartography and Geography in Geographical Information Systems (GIS) and Global Positioning Systems
- Learning Framework
- Programming Fundamentals II
- Introduction to Machine Learning
- ** College Algebra (May waive out or may have to take Developmental Algebra)
- Computer Vision for Artificial Intelligence (A.I.)
- Introduction to Raster-Based Geographic Information Systems (GIS)
- Web-Served Geographic Information Systems (GIS)
- Artificial Intelligence (A.I.) in Cybersecurity
- Federal Government
- Artificial Intelligence (A.I.) in Applications & Case Histories
- Natural Language Processing NLP
- Database Design and Theory
- Robotic Operating Systems and Platforms in Artificial Intelligence (A.I.)
- Deep Learning in Artificial Intelligence
- Data Science in Artificial Intelligence
- Introduction to Philosophy
- Humanities / Fine Arts Elective (Student will select)
On-the-job training
- Advise others on analytical techniques
- Recommend data-driven solutions to key stakeholders.
- Analyze business or financial data
- Apply feature selection algorithms to models predicting outcomes of interest, such as sales, attrition, and healthcare use.
- Analyze data to identify or resolve operational problems
- Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
- Analyze data to identify trends or relationships among variables
- Identify relationships and trends or any factors that could affect the results of research.
- Analyze data to inform operational decisions or activities
- Analyze, manipulate, or process large sets of data using statistical software.
- Apply mathematical principles or statistical approaches to solve problems in scientific or applied fields
- Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
- Determine appropriate methods for data analysis
- Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
- Develop procedures to evaluate organizational activities
- Design surveys, opinion polls, or other instruments to collect data
- Develop scientific or mathematical models
- Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
- Prepare analytical reports
- Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software
- Prepare data for analysis
- Clean and manipulate raw data using statistical software.
- Prepare graphics or other visual representations of information
- Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software
- Present research results to others
- Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users
- Select resources needed to accomplish tasks
- Identify business problems or management objectives that can be addressed through data analysis
- Update technical knowledge
- Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
- Write computer programming code
- Write new functions or applications in programming languages to conduct analyses
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