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<p style="text-align: start;"><span style="color: #ee2323;" data-ke-size="size26"><b>주요 강의 내용:</b></span></p><p style="text-align: start;"><span data-ke-size="size26"><span style="color: #0000ff;">구조방정식 다집단 분석 </span><span style="color: #0000ff;">(MPLUS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span></span><br><span style="color: #0000ff;" data-ke-size="size26">잠재성장모형</span><br><span data-ke-size="size26"><span style="color: #0000ff;">다층모형</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">잠재계층분석 등</span></span></p><p style="text-align: start;"> </p><p style="text-align: start;"> </p><p style="text-align: start;"><span data-ke-size="size26">● 강 사 : 신인수 교수 (동국대 교육대학원 인공지능 융합교육 전공, 동국대 서비스과학대학원 교육데이터사이언스 전공)</span></p><p style="text-align: start;"><span data-ke-size="size26">(Florida State University Ph. D, measurement and statistics, 메타분석 전공) 연세대학교 교육학과</span></p><p style="text-align: start;"><span data-ke-size="size26">Florida Department of Education Psychometrician. 행정고시 39회(교육행정직)</span></p><p style="text-align: start;"> </p><p style="text-align: start;"> </p><p style="text-align: start;"><span style="color: #ee2323;" data-ke-size="size23"><b>최근에</b></span><br><span style="color: #ee2323;" data-ke-size="size23"><b>데이터 사이언스나</b></span><br><span style="color: #ee2323;" data-ke-size="size23"><b>머신러닝 알고리즘을 활용한 논문들이 많이 작성되고 있습니다.</b></span><br><span style="color: #333333;" data-ke-size="size18">​</span><br><span style="color: #333333;" data-ke-size="size18">​</span><br><span style="color: #ee2323; background-color: #ffffff;" data-ke-size="size16"><span data-ke-size="size23"><b>또한 기본적인 데이터 분석 이슈 중에서 많은 대학원생들 가설검정의 원리 등을 간과하고 있습니다.</b></span></span><br><span style="color: #333333;" data-ke-size="size18">​</span><br><span style="color: #333333;" data-ke-size="size18">​</span><br><span style="color: #ee2323;" data-ke-size="size23"><b>머신러닝과 딥러닝의 차이,</b></span><br><span style="color: #ee2323;" data-ke-size="size23"><b>딥러닝 기본개념</b></span><br><span style="color: #ee2323;" data-ke-size="size23"><b>딥러닝 적용예도 소개하고 실습할 예정입니다.</b></span><br><span style="color: #333333;" data-ke-size="size18">​</span><br><span style="color: #333333;" data-ke-size="size18">​<br></span><span style="color: #006dd7;" data-ke-size="size16"><b><span data-ke-size="size23">기본적인 데이터분석, 머신러닝 알고리즘을 활용한 논문을 이해하고 싶거나</span></b></span><br><span style="color: #006dd7;" data-ke-size="size16"><b><span data-ke-size="size23">작성하고 싶은 대학원생, 유학생, 유학준비생</span></b></span><br><span style="color: #006dd7;" data-ke-size="size16"><b><span data-ke-size="size23">연구자, 교수님들을 위한 특강 입니다.</span></b></span></p><p style="text-align: start;"> </p><p style="text-align: start;"> </p><p style="text-align: start;"><span data-ke-size="size18">참가신청 및 등록방법</span></p><p style="text-align: start;">● 다음을 클릭하시거나 인터넷 주소창에 다음 주소를 복사해 신청해주시기 바랍니다.</p><p><a href="https://naver.me/583MuT6H" target="_top" class="ke-link">https://naver.me/583MuT6H</a></p><p style="text-align: start;"> </p><p> </p><p> </p><p><span data-ke-size="size26">“데이터 분석 및 머신러닝 알고리즘 적용 논문 작성법” 특강 </span></p><p> </p><p> </p><p> </p><div class="figure-img" data-ke-type="image" data-ke-style="alignCenter" data-ke-mobilestyle="widthOrigin"><img src="https://t1.daumcdn.net/cafeattach/1WxXM/3b7abae7184363bb04838e4789bb8bc183b416c8" class="txc-image" data-img-src="https://t1.daumcdn.net/cafeattach/1WxXM/3b7abae7184363bb04838e4789bb8bc183b416c8" data-origin-width="5198" data-origin-height="7088"></div><p> </p><p> </p><p><span data-ke-size="size26">신인수 교수 </span><span style="color: #0000ff;" data-ke-size="size26">“</span><span style="color: #0000ff;" data-ke-size="size26">데이터 분석 및 머신러닝 알고리즘 적용 논문 작성법</span><span style="color: #0000ff;" data-ke-size="size26">”</span> <span data-ke-size="size26">특강 개최</span></p><p> </p><p>● 강 사 : 신인수 교수</p><p><span data-ke-size="size14">(Florida State University Ph. D, measurement and statistics, </span><span data-ke-size="size14">메타분석 전공</span><span data-ke-size="size14">) </span><span data-ke-size="size14">연세대학교 교육학과</span></p><p><span data-ke-size="size14">Florida Department of Education Psychometrician. </span><span data-ke-size="size14">행정고시 </span><span data-ke-size="size14">39</span><span data-ke-size="size14">회</span><span data-ke-size="size14">(</span><span data-ke-size="size14">교육행정직</span><span data-ke-size="size14">)</span></p><p> </p><p><span data-ke-size="size18">워크숍 프로그램</span></p><p> </p><p> </p><p><span style="color: #ff0000;">▶ </span><span style="color: #ff0000;" data-ke-size="size18">특강 일정</span><span style="color: #ff0000;" data-ke-size="size18">1: </span><span style="color: #ff0000;" data-ke-size="size18">데이터 분석</span></p><div class="table-wrap"><table data-ke-type="table" data-ke-align="alignLeft" style="width: 100%;" border="1"><tbody><tr><td>요일</td><td>강의명</td><td>강의개요</td></tr><tr><td><span style="color: #ff0000;">1.8.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">가설검정 원리의 이해와 적용</span></td><td><span style="color: #0000ff;">가설검정의 개념 및 원리</span><span style="color: #0000ff;">. (SPSS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span><br><span style="color: #0000ff;">P-value</span><span style="color: #0000ff;">의 의미와 한계</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">효과크기 개념 및 종류</span><br><span style="color: #0000ff;">Type I error, </span><span style="color: #0000ff;">표준오차 </span><span style="color: #0000ff;">vs. </span><span style="color: #0000ff;">표준편차</span><br><span style="color: #0000ff;">다양한 분석 기법과 가설검정</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">해석</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">결과보고</span></td></tr><tr><td><span style="color: #ff0000;">1.15.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">중다회귀분석 매개효과</span><span style="color: #0000ff;" data-ke-size="size18">/</span><span style="color: #0000ff;" data-ke-size="size18">조절효과</span></td><td><span style="color: #0000ff;">중다회귀분석 </span><span style="color: #0000ff;">dummy coding, k-1 </span><span style="color: #0000ff;">코딩</span><br><span style="color: #0000ff;">중다회귀분석 매개효과</span><br><span style="color: #0000ff;">공분산분석</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">조절효과</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">상호작용 </span><span style="color: #0000ff;">(SPSS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">1.22.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">ANOVA, </span><span style="color: #0000ff;" data-ke-size="size18">실험연구 상호작용</span></td><td><span style="color: #0000ff;">분산분석 기본 개념</span><span style="color: #0000ff;">, TWO WAY ANOVA</span><br><span style="color: #0000ff;">반복측정</span><span style="color: #0000ff;">(REPEATED MEASURE)</span><br><span style="color: #0000ff;">공분산 분석 및 효과크기</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">결과보고</span><span style="color: #0000ff;">. (SPSS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">1.29.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">로지스틱 회귀분석</span></td><td><span style="color: #0000ff;">로지스틱 회귀분석 의의 및 필요성</span><br><span style="color: #0000ff;">로지스틱 회귀분석 적용예 및 실습 </span><span style="color: #0000ff;">(SPSS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">2.5.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">MPLUS </span><span style="color: #0000ff;" data-ke-size="size18">데이터분석 기초</span></td><td><span style="color: #0000ff;">MPLUS </span><span style="color: #0000ff;">기본 명령문 이해 </span><span style="color: #0000ff;">(MPLUS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span><br><span style="color: #0000ff;">MPLUS </span><span style="color: #0000ff;">활용 기술통계</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">회귀분석</span><br><span style="color: #0000ff;">MPLUS </span><span style="color: #0000ff;">활용 경로분석</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">요인분석</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">구조방정식</span></td></tr><tr><td><span style="color: #ff0000;">2.19.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">MPLUS </span><span style="color: #0000ff;" data-ke-size="size18">데이터분석 적용</span></td><td><span style="color: #0000ff;">구조방정식 다집단 분석 </span><span style="color: #0000ff;">(MPLUS, R, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span><br><span style="color: #0000ff;">잠재성장모형</span><br><span style="color: #0000ff;">다층모형</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">잠재계층분석 등</span></td></tr><tr><td><span style="color: #ff0000;">2.26.(</span><span style="color: #ff0000;">월</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">관련변인 메타분석</span></td><td><span style="color: #0000ff;">관련변인 메타분석 </span><span style="color: #0000ff;">VS. </span><span style="color: #0000ff;">처치효과 메타분석</span><br><span style="color: #0000ff;">관련변인 메타분석 적용예 </span><span style="color: #0000ff;">(CMA, R, REVMAN, JAMOVI </span><span style="color: #0000ff;">실습</span><span style="color: #0000ff;">)</span><br><span style="color: #0000ff;">관련변인 메타분석 보고기준</span></td></tr></tbody></table></div><p>▶시간: <u><span style="color: #ff0000;">오후 </span></u><u><span style="color: #ff0000;">2</span></u><u><span style="color: #ff0000;">시 </span></u><u><span style="color: #ff0000;">~ </span></u><u><span style="color: #ff0000;">오후</span></u><u><span style="color: #ff0000;">5</span></u><u><span style="color: #ff0000;">시 </span></u><u><span style="color: #ff0000;">(</span></u><u><span style="color: #ff0000;">각 </span></u><u><span style="color: #ff0000;">3</span></u><u><span style="color: #ff0000;">시간</span></u><u><span style="color: #ff0000;">) (</span></u><u><span style="color: #ff0000;">실시간 온라인 강의 </span></u><u><span style="color: #ff0000;">+ 3</span></u><u><span style="color: #ff0000;">월 </span></u><u><span style="color: #ff0000;">17</span></u><u><span style="color: #ff0000;">일까지 녹화 링크 제공 예정</span></u><u><span style="color: #ff0000;">)</span></u></p><p> </p><p><span style="color: #ff0000;">▶ </span><span style="color: #ff0000;" data-ke-size="size18">특강 일정</span><span style="color: #ff0000;" data-ke-size="size18">2: </span><span style="color: #ff0000;" data-ke-size="size18">머신러닝 알고리즘 적용</span></p><p> </p><p> </p><div class="table-wrap"><table data-ke-type="table" data-ke-align="alignLeft" style="width: 100%;" border="1"><tbody><tr><td>요일</td><td>강의명</td><td>강의개요</td></tr><tr><td><span style="color: #ff0000;">1.2.(</span><span style="color: #ff0000;">화</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">벌점회귀모형</span></td><td><span style="color: #0000ff;">벌점회귀모형의 의의 및 필요성</span><br><span style="color: #0000ff;">Lasso Regression, Ridge Regression, Elastic Net Regression</span><br><span style="color: #0000ff;">적용예 및 보고방법 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">1.9.(</span><span style="color: #ff0000;">화</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">Stacking and Boosting </span><span style="color: #0000ff;" data-ke-size="size18">기법</span></td><td><span style="color: #0000ff;">Stacking and Boosting </span><span style="color: #0000ff;">기법의 의의 및 필요성</span><br><span style="color: #0000ff;">XGBoost, Gradient Boosting, ADABoost </span><span style="color: #0000ff;">등</span><br><span style="color: #0000ff;">적용예 및 보고방법 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">1.19.(</span><span style="color: #ff0000;">금</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">Hierarchical Clustering, </span><span style="color: #0000ff;" data-ke-size="size18">비지도학습 상관분석</span><span style="color: #0000ff;" data-ke-size="size18">, </span><span style="color: #0000ff;" data-ke-size="size18">이미지분석</span></td><td><span style="color: #0000ff;">군집분석의 의의 및 필요성</span><br><span style="color: #0000ff;">비지도 학습</span><br><span style="color: #0000ff;">이미지분석 의의 및 적용예 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">1.26.(</span><span style="color: #ff0000;">금</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">텍스트 마이닝</span><span style="color: #0000ff;" data-ke-size="size18">, </span><span style="color: #0000ff;" data-ke-size="size18">감성분석</span><span style="color: #0000ff;" data-ke-size="size18">, </span><span style="color: #0000ff;" data-ke-size="size18">토픽모델링</span></td><td><span style="color: #0000ff;">텍스트 분석의 이해 및 필요성</span><br><span style="color: #0000ff;">감성분석</span><span style="color: #0000ff;">, </span><span style="color: #0000ff;">토픽모델링</span><br><span style="color: #0000ff;">이미지분석 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">2.2.(</span><span style="color: #ff0000;">금</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">머신러닝 알고리즘</span><br><span style="color: #0000ff;" data-ke-size="size18">비교 및 평가</span></td><td><span style="color: #0000ff;">머신러닝 알고리즘 성능평가의 의의 및 필요성</span><br><span style="color: #0000ff;">성능평가 의의 및 적용예 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">2.16.(</span><span style="color: #ff0000;">금</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">머신러닝 알고리즘</span><br><span style="color: #0000ff;" data-ke-size="size18">체계적 문헌고찰</span></td><td><span style="color: #0000ff;">인공지능 성능평가 체계적 문헌고찰 의의 및 필요성</span><br><span style="color: #0000ff;">성능평가 </span><span style="color: #0000ff;">SR </span><span style="color: #0000ff;">의의 및 적용예 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr><tr><td><span style="color: #ff0000;">2.23.(</span><span style="color: #ff0000;">금</span><span style="color: #ff0000;">)</span></td><td><span style="color: #0000ff;" data-ke-size="size18">머신러닝 알고리즘</span><br><span style="color: #0000ff;" data-ke-size="size18">메타분석</span></td><td><span style="color: #0000ff;">인공지능 성능평가 메타분석 의의 및 필요성</span><br><span style="color: #0000ff;">성능평가 </span><span style="color: #0000ff;">MA </span><span style="color: #0000ff;">의의 및 적용예 </span><span style="color: #0000ff;">(</span><span style="color: #0000ff;">오렌지</span><span style="color: #0000ff;">3, R, </span><span style="color: #0000ff;">파이썬 코랩 실습</span><span style="color: #0000ff;">)</span></td></tr></tbody></table></div><p>▶시간: <u><span style="color: #ff0000;">오후 </span></u><u><span style="color: #ff0000;">2</span></u><u><span style="color: #ff0000;">시 </span></u><u><span style="color: #ff0000;">~ </span></u><u><span style="color: #ff0000;">오후</span></u><u><span style="color: #ff0000;">5</span></u><u><span style="color: #ff0000;">시 </span></u><u><span style="color: #ff0000;">(</span></u><u><span style="color: #ff0000;">각 </span></u><u><span style="color: #ff0000;">3</span></u><u><span style="color: #ff0000;">시간</span></u><u><span style="color: #ff0000;">) (</span></u><u><span style="color: #ff0000;">실시간 온라인 강의 </span></u><u><span style="color: #ff0000;">+ 3</span></u><u><span style="color: #ff0000;">월 </span></u><u><span style="color: #ff0000;">17</span></u><u><span style="color: #ff0000;">일까지 녹화 링크 제공 예정</span></u><u><span style="color: #ff0000;">)</span></u></p><p> </p><p> </p><p><span data-ke-size="size18">참가신청 및 등록방법</span></p><p>● 다음을 클릭하시거나 인터넷 주소창에 다음 주소를 복사해 신청해주시기 바랍니다.</p><p><a href="https://naver.me/583MuT6H" target="_top" class="ke-link">https://naver.me/583MuT6H</a></p><p>● 참가비(교재비 포함):</p><p><span style="color: #ff0000;">- 3</span><span style="color: #ff0000;">시간 강의 </span><span style="color: #ff0000;">14</span><span style="color: #ff0000;">과목</span><span style="color: #ff0000;">: </span><span style="color: #ff0000;">온라인 </span><span style="color: #ff0000;">1</span><span style="color: #ff0000;">일</span><span style="color: #ff0000;">:10</span><span style="color: #ff0000;">만원</span><span style="color: #ff0000;">, 2</span><span style="color: #ff0000;">일</span><span style="color: #ff0000;">:18</span><span style="color: #ff0000;">만원</span><span style="color: #ff0000;">, 4</span><span style="color: #ff0000;">일</span><span style="color: #ff0000;">:32</span><span style="color: #ff0000;">만원</span><span style="color: #ff0000;">, 7</span><span style="color: #ff0000;">일</span><span style="color: #ff0000;">:49</span><span style="color: #ff0000;">만원</span><span style="color: #ff0000;">, 14</span><span style="color: #ff0000;">일</span><span style="color: #ff0000;">:84</span><span style="color: #ff0000;">만원</span></p><p><span style="color: #ff0000;">▪ </span><span style="color: #ff0000;">계좌번호</span><span style="color: #ff0000;">: </span><span style="color: #ff0000;">국민은행 </span><span style="color: #ff0000;">501001-04-391822 </span><span style="color: #ff0000;">▪ </span><span style="color: #ff0000;">예금주</span><span style="color: #ff0000;">: </span><span style="color: #ff0000;">교육통계연구소</span></p><p> </p><p>● <span style="color: #c75252;">연구비 처리 및 계산서 발급 가능</span></p><p>- 워크샵 스텝 연락처: 메일 주소 (<a style="color: #0070d1;" href="mailto:001447@naver.com" target="_top" class="ke-link"><u><span style="color: #800080;">001447@naver.com</span></u></a>)</p><p> </p><p><span data-ke-size="size18">환불기준</span></p><div class="table-wrap"><table data-ke-type="table" data-ke-align="alignLeft" style="width: 100%;" border="1"><tbody><tr><td><span data-ke-size="size18">수강취소 시기</span></td><td><span data-ke-size="size18">환불액</span></td></tr><tr><td>특강 4일 전</td><td>100% 환불</td></tr><tr><td>특강 3일 이내</td><td>80% 환불</td></tr><tr><td>특강 당일</td><td>환불 불가</td></tr></tbody></table></div><p> </p><p><span data-ke-size="size18">접수 세부사항</span><span data-ke-size="size18">, </span><span data-ke-size="size18">기타 자료 및 질문 카페 참조</span></p><p>SCI SSCI 논문 작성법 카페: <u><a href="http://cafe.daum.net/researcharticle" target="_top" class="ke-link">http://cafe.daum.net/researcharticle</a></u></p><p>질적연구 쳬계적문헌고찰 카페: <a href="http://cafe.daum.net/QUALITATIVEMETHOD" target="_top" class="ke-link">http://cafe.daum.net/QUALITATIVEMETHOD</a></p><p>페이스북: <u><a href="https://www.facebook.com/researchsynthesis" target="_top" class="ke-link">https://www.facebook.com/researchsynthesis</a></u></p><p><u><span style="color: #0000ff;">네이버 블로그</span></u><u><span style="color: #0000ff;">: </span></u><u><a href="https://blog.naver.com/9065031" target="_top" class="ke-link">https://blog.naver.com/9065031</a></u></p><p> </p><p style="text-align: center;"><span data-ke-size="size20">교육통계연구소</span></p><p> </p><p> </p>
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2024년 2월 19일(월) 오후2시 신인수 교수 "MPLUS 데이터분석 적용" 논문작성법 특강
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