AI-Enabled Data Analytics

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Overview

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AI-Enabled Data Analytics prepares learners to use artificial intelligence to analyze data, uncover insights, and make more informed business decisions. Designed for business professionals, aspiring analysts, managers, and anyone interested in improving their analytical skills, the course explores how AI-powered tools can simplify data analysis, automate repetitive tasks, and support data-driven decision-making. Learners develop practical skills in organizing, analyzing, and interpreting data while using AI to streamline common analytics workflows. Through hands-on activities and real-world business scenarios, they learn how to identify trends, generate meaningful insights, create visualizations, and communicate findings effectively. The course also explores responsible AI use, helping learners understand both the opportunities and limitations of AI-assisted analytics. Rather than focusing on programming or building AI models, the curriculum emphasizes practical workplace application. By the end of the course, learners will be prepared to confidently use AI-enabled data analytics tools to improve productivity, support strategic decision-making, and solve real-world business challenges.

Syllabus

Course introduction: AI in data analytics
- Artificial intelligence is transforming the way data analysts collect, prepare, analyze, and visualize information. In this course, you'll explore how AI can support each stage of the data analytics workflow while learning prompt engineering techniques that help you work more efficiently and effectively. This introduction provides an overview of the learning journey ahead and prepares you to apply AI thoughtfully throughout the data analytics process. Module Objective: Summarize the topics in this course
Prompt engineering for data analytics
- Effective prompt engineering is the foundation of using AI successfully in data analytics. In this module, you'll explore the characteristics of well-crafted prompts and learn how prompt design influences the quality, accuracy, and fairness of AI-generated results. You'll also examine best practices for writing responsible prompts that support reliable data analysis. As you begin working with AI, remember that the quality of the output depends on the quality of the prompt. Clear, specific, and thoughtful prompts lead to more useful and trustworthy results throughout the data analytics workflow. Module Objective: Analyze prompt engineering techniques that improve the quality, fairness, and reliability of AI-generated outputs for data analytics.
Collecting data with AI
- High-quality analysis begins with collecting the right data. In this module, you'll explore how AI can help you discover relevant datasets, connect to existing data sources, and generate prompts that support efficient data collection. You'll also examine data privacy best practices and learn how to use AI responsibly when working with sensitive information. Before collecting data, take a moment to consider whether the information is relevant, reliable, and appropriate for your analysis. A strong dataset provides the foundation for meaningful insights and trustworthy results. Module Objective: Develop AI prompts that locate, access, and collect relevant data while following responsible data practices.
Cleaning data with AI
- Clean, well-prepared data is essential for producing accurate and meaningful analysis. In this module, you'll explore how AI can help identify data quality issues, generate prompts for common data cleaning tasks, and streamline the process of preparing data for analysis. You'll also examine the limitations of AI-assisted data cleaning and learn when human review is necessary to ensure reliable results. Treat AI as a partner in the data cleaning process, not a replacement for critical thinking. Reviewing AI-generated outputs helps ensure your data is accurate, complete, and ready for analysis. Module Objective: Evaluate and refine data using AI prompts to prepare datasets for effective analysis.
Analyzing data with AI
- Data analysis is about transforming information into insights that support better decisions. In this module, you'll explore how AI can generate descriptive analyses, summarize key findings, and help you identify meaningful patterns in your data. You'll also examine the limitations of AI-assisted analysis and learn when to validate results using your own analytical judgment. AI can help uncover trends and accelerate analysis, but it's your understanding of the data that gives those insights meaning. Take time to question unexpected results and consider whether they align with the evidence before drawing conclusions. Module Objective: Develop AI prompts that generate accurate descriptive analyses and meaningful summaries of data.
Visualizing data with AI
- Data visualizations help transform complex information into clear, actionable insights. In this module, you'll explore how AI can assist with creating effective visualizations, summarizing key findings, and communicating results that support informed decision-making. You'll also examine the limitations of AI-generated visualizations and learn how to evaluate whether a visualization accurately represents the underlying data. The most effective visualizations do more than display data. They highlight the insights your audience needs to understand, making it easier to communicate findings with clarity and confidence. Module Objective: Develop AI prompts that generate effective data visualizations and communicate actionable insights.
Course wrap-up: AI in data analytics
- Throughout this course, you've explored how AI can support each stage of the data analytics workflow, from writing effective prompts and collecting data to cleaning, analyzing, and visualizing information. In this final module, you'll review the key concepts from the course before completing the knowledge check. As you prepare for the assessment, think about how each step in the workflow builds on the one before it. Effective AI-assisted data analysis depends on combining strong prompts, high-quality data, thoughtful analysis, and clear communication to produce meaningful insights. Module Objective: Evaluate AI-assisted approaches to collecting, preparing, analyzing, and visualizing data.