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It Makes Perfect Sense – No-Code AI Data Analysis That Really Works in Practice (Development Work Edition)

Even if you don't know coding, you can read data - everything about no-code AI analysis that makes perfect sense! If you can use Excel, PPT, and Google Sheets, AI will now handle data analysis for you.

1 learners are taking this course

  • skmns
AI 활용법
데이터분석
Excel
PowerPoint
Google Sheets
Generative AI

What you will learn!

  • You can analyze and visualize data using generative AI

  • You can apply data preprocessing, EDA, and machine learning modeling to real-world work using no-code methods.

  • You can integrate AI-based data analysis capabilities into your work through real-world case studies by job function.

  • Understand the characteristics of various generative AI models and be able to select and utilize them appropriately for different situations

Enhancing Data-Driven Work Capabilities Using AI Tools

  • Easy and fast data practice innovation even if you don't know coding


    Instead of memorizing complex formulas or code, we help you perform core tasks such as data analysis, visualization, text processing, and machine learning model building easily and quickly using AI and natural language tools.


  • Step-by-step practical + case-based learning


    It helps anyone develop concrete survival strategies for the AI era through job-specific change scenarios and practical application strategies that reflect industrial structural changes, automation levels, and AI adoption trends.


💡In the AI era, data utilization capabilities are no longer a choice but a survival strategy!

In the rapidly evolving AI technology environment, the importance of data-driven decision-making capabilities is growing across all job functions. Now, beyond simple tool usage, the ability to analyze, visualize, and interpret data through natural language-based AI tools and connect it to actual business has become a core competitive advantage.

This is a course designed to develop practical data literacy skills essential for all job functions. It is designed to enable professionals in various fields such as marketing, HR, planning, and operations to perform data-driven tasks using AI tools without complex coding. In particular, through hands-on content that can be immediately applied in the workplace, such as customer analysis, report automation, text analysis, and time series forecasting, you can simultaneously strengthen both operational efficiency and strategic thinking.


You'll learn this kind of content

Direct lecture by Lee Gi-bok, expert author of 'ChatGPT No-Code Data Analysis'

I provide guidance on no-code based data analysis and generative AI utilization capabilities based on books I have personally authored. I offer highly reliable lectures based on my expertise and practical experience as an author.

Explains how to clearly design prompts using the CORE framework by dividing them into context, output, role, and examples.

First, clarify the background and purpose of the question, specifically specify the format, length, and style of the deliverable, and clearly define the role to be assigned to the model. Provide reference examples such as past success cases to enhance consistent quality and reproducibility.

This is a practice exercise for organizing judge score data in Excel to calculate totals, averages, rankings, and pass/fail status.

Use ranking functions and IF conditional statements to determine pass/fail based on top criteria (e.g., top 10 candidates), and display totals, averages, and score distributions together. Align formatting across the entire sheet and verify data labels to connect evaluation indicator (index) meanings with explanations at the bottom of the screen.

Compare the cumulative returns of monthly, semi-annual, and annual investment strategies to visualize which period strategy performed better

Draw cumulative value curves for four strategies on one screen to intuitively compare performance differences. Configure visualization libraries for Korean font application, generate profit curves by strategy using the same data, and proceed with result interpretation.

Compare the top 10 sales by payment method (debit card, credit card, cash) for each store and check the similarity and variability of purchasing patterns through charts.

Compare the sales volume by payment method across stores simultaneously using three bar charts to identify at a glance which stores are strong in which payment methods.

We examine the entire EDA process including descriptive statistics, type adjustment, missing value handling, outlier detection, and correlation analysis to identify key points.

Organize variable distributions and missing/outlier values, and perform scaling and type conversions as needed to create data ready for analysis. The more features you have, the more emphasis you should place on EDA to visualize correlations and patterns, which will help concretize your modeling direction and hypotheses.

Medical Data Challenge Definition: Confirms the necessity of MRI preprocessing, ensuring reliability with small datasets, and implementing no-code diagnostic assistance models.

We organize strategies for preprocessing MRI images so AI can understand them and securing performance in data-scarce environments. We present step-by-step requirements and precautions for diagnostic assistance AI models that can be built without development knowledge.

Recommended for
these people

Who is this course right for?

  • Working professionals who want to systematically learn from basic data analysis concepts to the latest no-code techniques

  • Office workers who want to improve work efficiency by utilizing AI for repetitive tasks and data processing

  • Business professionals who want to perform data analysis and visualization without programming languages like SQL or Python

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Curriculum

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12 lectures ∙ (4hr 55min)

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