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  1. Programs
  3. Data for Machine Learning

Data for Machine Learning

IBM SkillsBuild

Course

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

This credential earner demonstrates knowledge of how to prepare and analyze data for machine learning. The individual has introductory knowledge of the process of utilizing data to make informed decisions in machine learning modeling. The learner is familiar with the skills required for success in a machine learning related role.

Format

Online

Eligibility Calculator

Which aid programs apply to this program?

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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

No scholarships listed.

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Locations

Where this program is offered

  • Alabama

    Alabama

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Related Programs

Programs related to this one

No related programs.

Skills & Competencies

Skills developed through this program

  • AI Ethics
  • Data Analytics
  • Data Cleansing
  • Data Transformation
  • Exploratory Data Analysis
  • Machine Learning
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
Match confidence: high51-4041.00Machiniststitle_inference$56,150 median$78,760 top+0.03%0
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: emerging (Level 1)(based on Course)

  • Structured datasets and query tools — retrieve and inspect using standard SQL commands under direct supervisor guidance in a business analytics environment.
  • Program malfunctions and error logs — identify and document following established troubleshooting checklists on assigned data pipelines.
  • Business intelligence dashboards — interpret pre-built visualizations and summarize findings in written reports for team review.
  • Statistical software and development environments — execute provided scripts and record outputs under close mentorship during onboarding projects.
  • Data quality issues and anomalies — recognize and escalate using defined protocols within a structured data governance workflow.
  • Database management systems — navigate and perform basic queries following documented procedures on production or staging environments.
  • Computer program installation and configuration — assist senior staff in coordinating and testing setup steps according to written runbooks.
  • Technical findings and data summaries — communicate clearly in team meetings using active listening and structured speaking techniques.
  • Mathematical and statistical concepts — apply foundational methods such as descriptive statistics to support routine analytical tasks assigned by senior data scientists.
  • Cloud-based management tools — operate under direction to monitor resource usage and flag irregularities for supervisor review.

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

Completion Rate
Not reported
Placement Rate
Not reported