Entrepreneur and author Jordan Goldmeier discusses the enduring relevance of Excel in data science, its use cases, limitations, and future enhancements like generative AI. Topics include Power Query for data cleaning, effective data communication, developing a data mindset, and transitioning tools for improved data analysis.
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Quick takeaways
Excel remains relevant in data science for algorithm creation and communication with stakeholders.
Power Query in Excel simplifies data transformation and analysis, potentially integrating generative AI in the future.
Deep dives
Excel's Role in Data Science
Excel is discussed as a potent tool for data science despite facing criticism from practitioners favoring Python, R, or SQL. The episode features insights from data visualization expert Jordan Goldmeyer, who asserts Excel's continued relevance. The visual and formulaic nature of Excel assists in algorithm creation, data visualization, and communication with non-technical stakeholders.
AI Impact on Excel
The evolving landscape of generative AI and tools like DataLab and Codepilot raises questions about the future convergence of data tools with AI capabilities. The potential integration of generative AI into Excel could streamline data analysis tasks, particularly in visualization and simplification of complex functions.
Power Query and Data Transformation in Excel
Power Query in Excel revolutionizes data transformation by providing a user-friendly approach to cleaning and processing data. The tool simplifies tasks such as data frame creation and analysis, offering an intuitive alternative to scripting features like VBA. Excel's dynamic arrays and let function further enhance data processing capabilities.
Cultivating a Data Mindset
Developing a successful data mindset involves critical thinking and skepticism towards presented data. The podcast highlights the importance of understanding data accuracy and the influence of bias in decision-making. Encouraging communication within teams and focusing on continual improvement in data literacy are essential for cultivating a data-driven approach.
Excel often gets unfair criticism from data practitioners, many of us will remember a time when Excel was looked down upon—why would anyone use Excel when we have powerful tools like Python, R, SQL, or BI tools? However, like it or not, Excel is here to stay, and there’s a meme, bordering on reality, that Excel is carrying a large chunk of the world’s GDP. But when it really comes down to it, can you do data science in Excel?
Jordan Goldmeier is an entrepreneur, a consultant, a best-selling author of four books on data, and a digital nomad. He started his career as a data scientist in the defense industry for Booz Allen Hamilton and The Perduco Group, before moving into consultancy with EY, and then teaching people how to use data at Excel TV, Wake Forest University, and now Anarchy Data. He also has a newsletter called The Money Making Machine, and he's on a mission to create 100 entrepreneurs.
In the episode, Adel and Jordan explore excel in data science, excel’s popularity, use cases for Excel in data science, the impact of GenAI on Excel, Power Query and data transformation, advanced Excel features, Excel for prototyping and generating buy-in, the limitations of Excel and what other tools might emerge in its place, and much more.