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Common Sense Explodes – No-Code AI Data Analysis That Really Works in Practice (Non-Development Tasks Edition)

Even if you don't know coding, you can read data—everything about no-code AI analysis that makes work sense explode! From marketing to HR and quality management—we'll make your work sense explode with AI.

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 a no-code approach.

  • You can apply AI-based data analysis capabilities to your work through real-world examples by job function.

  • You can understand the characteristics of various generative AI models and select and utilize them appropriately for different situations.

Strengthening 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, this helps you easily and quickly perform core tasks such as data analysis, visualization, text processing, and machine learning model building by utilizing AI and natural language tools.


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


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


💡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. Through hands-on content that can be immediately applied in the workplace, including customer analysis, report automation, text analysis, and time series forecasting, you can simultaneously enhance 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 guide no-code based data analysis and generative AI utilization capabilities based on books I have personally authored. I provide 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 students), 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 relationship between model-specific performance and price satisfaction using kernel density distribution to identify central tendencies and variance.

We visualize the performance-to-price satisfaction distribution for each of the S·E·X·Y models to identify which models cluster in the high-performance·high-satisfaction/low-satisfaction areas. We compare the center, spread, and asymmetry of the distributions to derive evidence for positioning and price·specification adjustments.

Compare the age group ratios of the customer segment (Champions) using a bar chart to identify the main age demographics.

Visualize the proportion of each age group from 30s to 60s and above using a bar chart to identify which segment has the highest distribution. Target the dominant age group as the core audience and develop expansion strategies and differentiated messaging for adjacent age groups.

Automate the derivation of key insights by using AI to summarize, classify, and extract descriptive feedback collected from Excel.

Utilize the creation, summarization, classification, and extraction features in the right panel to batch process sentence-level summaries, keyword/sentiment tagging, and improvement point extraction. Template repetitive prompts to reproduce consistent results and organize them into sheet columns to accelerate report writing speed.

Compare NVIDIA's 1-year returns after major events using a bar chart to verify the impact of each event.

Distinguish log return bars by color for each event such as RTX, 4-to-1, ARM, market cap, and others to identify upward/downward effects at a glance. Compare the magnitude and direction of returns to determine which events are significant for long-term performance and establish response strategies for similar events.

Recommended for
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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 56min)

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