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Data Science

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Data Analysis

Understanding the World Through Statistics, Interpreting with AI

AI, the Key to Opening the World of Statistics "AI, by enabling easy mastery of complex statistical principles and analysis techniques, uncovers hidden meanings in data and provides insights for better decision-making. AI is now beyond a complex statistical calculator; it is a companion supporting statistical thinking."

17 learners are taking this course

  • papadave
ai통계분석
chatgpt활용
ai코딩
인공지능모듈개발
데이터분석
ChatGPT
Statistics
AI
Python

What you will learn!

  • Easily analyze statistics and data with chatGPT and apply to work.

  • Understanding operating principles, case studies, and practical exercises for various analytical models, including statistical theory, machine learning, and deep learning.

  • With ChatGPT, statistical and data analysis model development and system implementation

  • With ChatGPT, finding data, creating data, creating research papers, creating lecture materials

  • Real Data-Based Problem Solving and Decision Making

  • Excel, Ornage3, My GPT, etc., utilizing various analysis tools and practical data analysis

Decision making through various data analysis

"Seeing the World Through Statistics, Interpreting It Through AI"


🎯 What will I learn from the lecture?

  • Understand the need for statistics and AI statistics in decision-making and learn how to utilize AI Assistant.


  • Develop practical skills in exploratory data analysis (EDA) and hypothesis testing to understand the characteristics and structure of data.


  • Learn how to use various analysis tools such as Excel, Orange3, ChatGPT, and NodeXL.


  • Learn various statistical techniques and data analysis models.

  • Learn how to find ChatGPT data, create data, and build an analysis model system.

  • Learn how to write experimental research papers with ChatGPT.


📌 Areas of Lecture Utilization

  • Data-driven rapid decision-making and prediction

  • Developing marketing strategies and analyzing customers and markets

  • Implementation of various analysis model systems

  • Data preprocessing and error verification

  • Analysis of prior research and writing of papers

  • Writing lecture materials for statistics and data analysis


After taking the lecture, you will be able to create results like this.

AI makes statistical analysis easy.

Make data analysis fun and confident.

You can create source code for data analysis and statistics.


It provides systematic support and helps save time in writing research papers and lecture materials.

  • What will I understand and be able to do well after attending the lecture?


  • Be as specific as possible about how students can change.

Learning content

  • Statistics meets AI to support rapid decision-making.

  • AI makes big data analysis planning, data collection and preprocessing, data analysis, analysis model development, and system construction easier and more understandable.

  • We quickly handle the problem definition, prior research analysis, questionnaire creation, hypothesis setting and verification, statistical analysis and result interpretation, and reference organization required for paper writing.

  • We will teach you how to use Oranage3 and Excel tools for statistical analysis.

Data scatter plot

Implementing AI-powered exploratory data analysis (EDA)

  • You can check the characteristics of the data in the datasets at a glance.

Data preprocessing by AI

  • As long as you have a dataset, it can help you with various data preprocessing tasks.


Data quartile chart

Data analysis source code

Writing analysis model code

  • It creates source code suitable for various development environments such as VS Code and Colab.

Writing papers and lecture materials

  • Assists in writing course materials for data analysis and statistical analysis.

  • Helps you write research-based papers.

Thesis research hypothesis

Things to note before taking the course

  • This course is based on university lecture materials, so it helps you acquire systematic theory and case studies.

Practice environment

  • Google Colab, Python development environment (VS Code, WSL (Ubuntu 22.04.5 LTS))

  • Tools used: Excel analysis tool, Orange3, Python code

  • PC specifications: For general learning

Learning Materials

  • PDF, cloud link, text, source code, etc.

  • Excel, Orange3, ChaGPT4.o


Player Knowledge and Precautions

  • Simple Excel tasks

  • This lecture is based on university lecture materials.

Recommended for
these people

Who is this course right for?

  • Data-driven decision maker

  • Data and Statistics-driven Marketing Strategist

  • Data Analysis-Based Research Paper Preparer

  • Those challenged by statistics and data analysis in work.

  • Interested in AI-based statistical analysis

Need to know before starting?

  • Simple Excel operations

  • Simple chatGPT Usage

Hello
This is

78

Learners

14

Reviews

4.3

Rating

3

Courses

경력

  • [구] LG유플러스 네트워크 기술

  • [구] 홈플러스 정보서비스 기획

  • [구] 다산네트워크 융합연구소

     

  • [현]NIPA/NIA/IITP 블록체인/인공지능 기술 심사위원

  • [현]명지대학교 기록정보과학대학원 AI정보과학과 초빙교수

  • [현]호서대학교 벤처대학원 융합공학과 초빙교수

  • [현]고려사이버대학 융합정보대학원 외래교수

  • [현]스마트코아솔루션 창업/대표이사

연구

  • Mainstream 방식을 이용한 비연결형 감시 프로토콜-한국통신학회

  • 딥러닝 알고리즘 기반의 초미세먼지(PM2.5) 예측 성능 비교분석-융합정보논문지

  • 빅데이터 기반한 미세플라스틱 지적네트워크 분석-융합정보논문지

  • 텍스트 마이닝 의한 한·중·일 미세플라스틱 연구 동향 비교 분석-융합정보논문지

  • 토픽모델링과 네트워크 분석에 기반한 AI 음성기술 연구 동향 분석 -융힙정보논문지

관심분야

  • 데이터 분석 및 통계 예측

  • 음성인식과 자연어 처리

  • 블록체인/ NFT

Curriculum

All

26 lectures ∙ (15hr 1min)

Course Materials:

Lecture resources
Published: 
Last updated: 

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$77.00

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