Course

Credit Type:
Course
ACE ID:
MLS-0115
Version:
1
Organization:
Coursera Inc.
Location:
Online
Length:
13 weeks (100 hours)
Minimum Passing Score:
80
ACE Credit Recommendation Period:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Lower-Division Baccalaureate 3 Applications of Generative AI
Description

Objective:

The course objective is to empower learners to leverage AI across the entire data lifecycle, streamlining processes and uncovering deeper insights crucial for career success. This program is designed for both seasoned data professionals and aspiring analysts. Learners will engage in real-world projects for practical skill applications, learn to implement AI responsibly and ethically, and showcase their new skills with a robust portfolio. They will develop expertise in integrating AI tools into data analysis workflows; optimizing data processing and preparation with AI assistance; enhancing exploratory data analysis and uncovering hidden insights; applying advanced analytical methods, including predictive modeling; creating dynamic visualizations and interactive dashboards; and automating workflows through AI-powered code generation.

Learning Outcomes:

  • Integrate generative AI tools and platforms into existing data analysis processes to enhance efficiency in data exploration and insight generation
  • Develop and apply prompt engineering techniques to effectively interact with generative AI systems for various data analysis tasks
  • Use generative AI to explore, analyze, and interpret datasets, including identifying patterns, relationships, and generating insights
  • Evaluate ethical considerations and responsible use of generative AI in data analysis, including bias, reliability, and appropriate applications
  • Define generative AI and explain its role and applications within data analysis workflows, including its impact on modern data-driven decision-making

General Topics:

  • Introduction to Generative AI for Data Analysis
  • Data Processing and Optimization with Generative AI
  • Advanced Data Analysis with Generative AI
  • Data Visualization and Reporting with Generative AI
  • Coding and Automation for Data Analysis with Generative AI
  • Scenario and Root Cause Analysis with Generative AI
Instruction & Assessment

Instructional Strategies:

  • Audio Visual Materials
  • Computer Based Training
  • Discussion
  • Laboratory
  • Lectures
  • Practical Exercises

Methods of Assessment:

  • Quizzes
Supplemental Materials
Equivalencies

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