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A Learning Plan pulls together training content for a particular role or solution, and organizes those assets from foundational to advanced. Use Learning Plans as a starting point to discover training that matters to you. This Learning Plan is…

This credential earner understands basic statistical concepts such as measures of central tendency, dispersion, and linear regressions. The individual uses spreadsheets and database engines to handle, clean, and analyze data, and generates visual reports using data visualization tools such as Microsoft Power BI.

This credential earner has applied technical competencies of data analytics concepts for machine learning such as data processing, supervised learning, and unsupervised learning. The individual has the technical knowledge and practical skills on data collection and preprocessing; training and testing data, and uncovering data patterns and structures to predict outcomes and make decisions.

This credential earner has applied knowledge of sustainability concepts such as environmental, social, and governance (ESG) reporting, conservation, and resource management and uses data to gain insights about sustainability issues. The individual has skills in Python programming for data analysis and visualization, and proposal writing. The earner is aware of the job outlook, grant opportunities in sustainability fields, and the skills required for success in roles in the domain.

This credential earner has the advanced competencies to analyze data based on its source and type. The individual has the technical knowledge and practical skills to analyze the characteristics of data sets and sources and to refine data sets to align with organizational requirements. The earner has practiced essential workplace skills and explored data analytics career pathways.

This credential earner has the technical knowledge and practical skills to construct effective prompts to classify and summarize data using IBM Granite. The earner can adjust the generative AI model’s parameters to refine the output of prompts.

This credential earner has the advanced competencies to complete the five steps of the data analysis process. The individual has the technical knowledge and practical skills to prepare for analysis, gather data, clean data, analyze data, and interpret the results. The earner has practiced essential workplace skills and explored data analytics career pathways.

In this course, you will learn essential data engineering concepts such as what does a data engineer do, how to perform data discovery, and which AWS data services are available for running, automating, and securing your data solutions and…

This comprehensive learning plan is designed to provide a solid foundation in data engineering principles, tools, and best practices to prepare you to perform the data engineering role in the AWS Cloud. Through a series of courses, you will…

This credential earner demonstrates knowledge of data analytics concepts, methodologies and applications of data science, and the tools and programming languages used in the data ecosystem. The individual has a conceptual understanding of how to clean, refine, and visualize data using IBM Watson Studio. The earner is aware of the job outlook in data and is familiar with the skills required for success in various roles in the domain.

This credential earner demonstrates knowledge of data analytics concepts, methodologies and applications of data science, and the tools and programming languages used in the data ecosystem. The credential verifies the ability to apply data analysis skills in a data visualization tool, including connecting to data, building and creating interactive dashboards to share insights. The earner is aware of the job outlook and skills required for success in roles in data analytics.

Learn best practices for importing, exporting, and managing your data to ensure its integrity and quality.