Utilizing Excel, Python, and Copilot as a Citizen Data Scientist

As the tools for integrating AI into everyday productivity workflows become more refined and accessible, a new term for people using these tools has become increasingly common: the citizen data scientist. A citizen data scientist is someone who can now do much of the work of what a bona fide data scientist did just a few years ago, including being equipped with basic skills and conceptual knowledge to effectively approach data-related questions and integrate established tools like Excel with the power of AI. In this course, instructor Chris Hui covers the AI frameworks needed to optimize prompt retrievals from GPT-4 LLMs, as well as hands-on practical deployment workflows combining Excel with Power Query, Python, and Copilot. Are you ready to augment your capabilities as an AI-powered citizen data scientist?

Learning objectives
Differentiate between the roles and workflows of a traditional data scientist and a citizen data scientist, and understand how large language models (LLMs) and AI assistants like Copilot can augment and streamline the citizen data scientist workflow.
Apply agentic workflows and prompting frameworks, such as the COSTAR framework, to effectively leverage AI assistants like Copilot for data analysis tasks, enabling chain-of-thought prompting and reflection.
Utilize Power Query and Python for data ingestion, preparation, and transformation, including numerical and categorical data cleansing, and integrate Power Query and Python data pipelines within Excel workbooks.
Automate numerical analysis and exploratory data analysis (EDA) in Excel by leveraging the Analyze Data Pane and integrating Python scripts, enabling streamlined and efficient data exploration.
Integrate outputs from LLMs into Excel workbooks, leveraging the capabilities of AI assistants to enhance and enhance data analysis workflows in the Excel environment.