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  1. Programs
  3. SQL for Data Science

SQL for Data Science

Great Learning Academy

Certificate

Become a contributor for free to openly demonstrate student outcomes, industry alignment & eligibility criteria.

SQL clauses, joins, GROUP BY, subqueries, and Python integration. Free certificate. One of the most in-demand technical skills for analysts.

Cost

FREEShow moreShow less

Format

Online

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Program Pathways

Credentials this program stacks toward

No program pathways.

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Program Details

Detailed information about this program

No detailed information available.

Requirements

What you need to earn this credential

No requirements listed.

Financial Aid

Eligible funding programs

No funding information available.

Scholarships

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Locations

Where this program is offered

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Related Programs

Programs related to this one

No related programs.

Skills & Competencies

Skills developed through this program

  • SQL clauses, joins, GROUP BY, subqueries
  • Python integration
Career Pathways

Occupations this program prepares you for

Auto-populated·from O*NET + BLS
Occupations matched to this program, with median wage, top wage, growth, and openings
SOCOccupationMethodWageGrowthOpenings
Match confidence: high15-2051.00Data Scientiststitle_inference$112,590 median$194,410 top+33.51%8,250
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: developing (Level 2)(based on Certificate)

  • Recurring data pipeline malfunctions — diagnose and resolve with reduced oversight by applying systematic debugging techniques in production environments.
  • Business problems involving integrated data sources — analyze independently using business intelligence software to develop actionable solution recommendations.
  • Computer programs and automated workflows — test, maintain, and monitor on a scheduled basis, adapting procedures when standard approaches prove insufficient.
  • Staff and end-user data-related inquiries — address by providing clear, accurate assistance on database tools and analytical software in a service-oriented setting.
  • Moderately complex datasets from multiple systems — join, transform, and model using SQL and scripted environments to support departmental decision-making.
  • Project timelines and analytical deliverables — manage using project management software, coordinating tasks with cross-functional stakeholders independently.
  • Analytical findings and methodology — document in written technical reports that meet organizational standards for clarity and reproducibility.
  • Statistical and machine learning models — build and validate in familiar problem contexts, adjusting hyperparameters based on performance metrics.
  • Data storage and cloud infrastructure configurations — maintain and troubleshoot using storage networking and cloud-based management software with limited supervision.
  • Emerging tools and analytical techniques — evaluate through active learning and apply selectively to improve existing workflows within established team practices.

Some details on this page are auto-populated from public workforce data sources: O*NET (opens in new tab), BLS (opens in new tab), College Scorecard (opens in new tab), DOL Training Provider Results (opens in new tab), NSX (opens in new tab). Provided in partnership with LER.me Career Intelligence.

Student Outcomes

Performance metrics for this program

Auto-populated·from Scorecard + DOL
Completion Rate
100%
Placement Rate
Not reported