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5주 완성! 25시간 데이터 분석 그룹 과외

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학습내용 파이썬 | 데이터분석 | 모델링

수업방식 실시간 라이브 (Live)

수강분반 월수반(8pm) | 화목반(8pm) | 토일반(10am)

학습기간 주2회 | 5주 | 25시간

수강가격 80만원 → 39.8만원 [슈퍼얼리버드 할인가]

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※ 꼭! 부탁드려요 ※

강의 수강 전 사전 설문을 통해 자신에게 맞는 반으로 구성해드립니다.

사전 설문에 잊지 않고 참여해주세요!

(결제 후 화면에 나타나는 절차를 따라주시면 됩니다)

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지금 놓치면

5주를 기다려야 해요!

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월수반

개강일 : 2021.08.30 (월)

종강일 : 2021.10.06 (수)

강의시간 : 오후 8시 ~ 10시 30분 (2시간 30분)

화목반

개강일 : 2021.08.31 (화)

종강일 : 2021.10.05 (화)

강의시간 : 오후 8시 ~ 10시 30분 (2시간 30분)

토일반

개강일 : 2021.08.31 (토)

종강일 : 2021.10.05 (일)

강의시간 : 오전 10시 ~ 12시 30분 (2시간 30분)

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강의소개

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소수정예

25시간 데이터 분석 그룹 과외

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데이터다이빙은,

쌩초보 입문자도 가장 쉽고 빠르게

데이터 분석에 다이빙 할 수 있도록 만들어진

소수정예 온라인 라이브 과외입니다.

인강이나 대형 강의와는 달리,

밀착 관리와 언제든 질문할 수 있는 환경으로

단 5주 만에, 누구나 데이터 왕초보 탈출시켜드립니다!

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Your content goes here. Edit or remove this text inline or in the module Content settings. You can also style every aspect of this content in the module Design settings and even apply custom CSS to this text in the module Advanced settings.

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차별점

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데이터다이빙, 왜 특별할까?

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소수정예 그룹과외

소규모 인원으로 밀도 있는 수업을 받아보세요!

개별 인원에 대한 피드백을 통한

프리미엄 교육 서비스만을 제공합니다.

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초단기 실용주의 커리큘럼

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완강률 83%

등록해두고 완강 못 한 강의가 한 가득!

데이터다이빙의 라이브 수업으로

중도 포기는 이제 그만!

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최고의 강사진

서울대 석박사 이상 분들만 모셨습니다.

현업 실무 전문가의 밀착 지도는 다릅니다.

오프라인의 장점은 살리고, 위험은 줄이고!

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커리큘럼

5주 완주! 실용주의 커리큘럼

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머신러닝을 위한 확률 통계
파이썬과 판다스 익숙해지기
파이썬으로 자유자재로 데이터 다뤄보기

데이터 처리를 위한 프로그래밍 언어인 파이썬과 판다스 기초, 변수와 연산, google colab환경,

리스트와 반복문, Pandas Series, Pandas Dataframe, 외부 데이터를 PD로 불러오기.

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데이터 시각화하기
머신러닝 입문하기

scikit-learn 패키지를 활용한 머신러닝 실전 적용
머신러닝을 위한 확률/통계, 머신러닝이란?, Seaborn library를 이용한 데이터 시각화,
Feature array와 target array, 데이터 타입과 인코딩, Train/Test 데이터 스플릿, 과적합, 교차검증

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데이터 전처리하기
모형 적합하기
가설 확인하기

Exploratory Data Analysis, Dataset 확인, 모형(가설) 세우기,
Missing data 처리, feature engineering, 기계학습 모델로 성능 검증하기

[/et_pb_toggle][et_pb_toggle title=”회귀(Regression) ” open_toggle_text_color=”#0031e2″ open_toggle_background_color=”#f9f9f9″ closed_toggle_background_color=”#F9F9F9″ icon_color=”#888888″ _builder_version=”4.9.10″ _module_preset=”default” title_text_color=”#232323″ title_font=”Noto Sans KR||||||||” title_font_size=”16px” title_letter_spacing=”-0.8px” body_font_size=”12px” body_letter_spacing=”-0.5px” body_quote_border_weight=”1px” body_quote_border_color=”#4e6ee3″ body_quote_text_color=”#4e6ee3″ body_quote_font_size=”14.5px” body_quote_letter_spacing=”-0.5px” custom_padding=”30px|30px|30px|30px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”0px” global_colors_info=”{}”]

회귀 모델 적합하기
회귀 모델 실습

회귀 정의, 분류와 회귀, 관련용어, 단순 선형 회귀(Linear regression),
다중 선형 회귀(Multiple linear regression), App 매출액 예측(실습)

[/et_pb_toggle][et_pb_toggle title=”분류(Classification) & 앙상블 기법(Ensemble Learning) ” open_toggle_text_color=”#0031e2″ open_toggle_background_color=”#f9f9f9″ closed_toggle_background_color=”#F9F9F9″ icon_color=”#888888″ _builder_version=”4.9.10″ _module_preset=”default” title_text_color=”#232323″ title_font=”Noto Sans KR||||||||” title_font_size=”16px” title_letter_spacing=”-0.8px” body_font_size=”12px” body_letter_spacing=”-0.5px” body_quote_border_weight=”1px” body_quote_border_color=”#4e6ee3″ body_quote_text_color=”#4e6ee3″ body_quote_font_size=”14.5px” body_quote_letter_spacing=”-0.5px” custom_padding=”30px|30px|30px|30px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”0px” global_colors_info=”{}”]

의사결정나무 & 앙상블 기법 이해하기
의사결정나무 & 앙상블 실습하기

의사결정나무 생성 및 붓꽃 데이터, 의사결정나무 모델 학습, 의사결정나무 Graphviz,
Graphviz, 의사결정나무 가지치기, 앙상블 기법, 랜덤포레스트, XGboost, 신용카드사기 검출(실습)

[/et_pb_toggle][et_pb_toggle title=”실습 & 딥러닝” open_toggle_text_color=”#0031e2″ open_toggle_background_color=”#f9f9f9″ closed_toggle_background_color=”#F9F9F9″ icon_color=”#888888″ _builder_version=”4.9.10″ _module_preset=”default” title_text_color=”#232323″ title_font=”Noto Sans KR||||||||” title_font_size=”16px” title_letter_spacing=”-0.8px” body_font_size=”12px” body_letter_spacing=”-0.5px” body_quote_border_weight=”1px” body_quote_border_color=”#4e6ee3″ body_quote_text_color=”#4e6ee3″ body_quote_font_size=”14.5px” body_quote_letter_spacing=”-0.5px” custom_padding=”30px|30px|30px|30px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”0px” global_colors_info=”{}”]

퍼셉트론, 역전파 이해하기
딥러닝으로 회귀와 분류 구현

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실제 데이터를 활용한 실습 구성

App 스토어 데이터를 활용하여,
특정 App의 매출액 예측하기

[/et_pb_blurb][et_pb_blurb title=”분류” image=”https://datadiving.net/wp-content/uploads/2021/08/ezgif.com-gif-maker-4-1.gif” _builder_version=”4.9.10″ _module_preset=”default” header_font_size=”20px” header_letter_spacing=”-1px” header_line_height=”1.3em” body_letter_spacing=”-1px” min_height=”480.7px” custom_margin=”||-15px||false|false” custom_padding=”||0px||false|false” border_radii_image=”on|20px|20px|20px|20px” box_shadow_style_image=”preset1″ box_shadow_vertical_image=”0px” box_shadow_blur_image=”30px” box_shadow_spread_image=”-15px” global_colors_info=”{}”]

신용카드 거래 데이터를 활용하여,
비정상적인 카드거래 판별하기

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정규화를 통해 모델을 개선하고,
데이터를 시각화하여 표현하기

[/et_pb_blurb][et_pb_blurb title=”딥러닝” image=”https://datadiving.net/wp-content/uploads/2021/08/ezgif.com-gif-maker3.gif” _builder_version=”4.9.10″ _module_preset=”default” header_font_size=”20px” header_letter_spacing=”-1px” header_line_height=”1.3em” body_letter_spacing=”-1px” min_height=”480.7px” custom_margin=”||-15px||false|false” custom_padding=”||0px||false|false” border_radii_image=”on|20px|20px|20px|20px” box_shadow_style_image=”preset1″ box_shadow_vertical_image=”0px” box_shadow_blur_image=”30px” box_shadow_spread_image=”-15px” global_colors_info=”{}”]

의상 이미지 데이터를 활용하여,
10가지 종류의 옷 자동으로 구별하기

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추천대상

이런 분들께 추천해요!

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파이썬의 ‘ㅍ’자도 모르는 노베이스 비전공자

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머신러닝과 모델링에 대해 더 알고 싶은 현업 개발자

[/et_pb_blurb][et_pb_blurb use_icon=”on” _builder_version=”4.10.1″ _module_preset=”default” body_text_color=”#232323″ body_letter_spacing=”-1px” body_line_height=”0.6em” custom_margin=”|220px||220px|false|true” custom_padding=”27px|0px|27px|0px|true|true” border_radii=”on|16px|16px|16px|16px” border_width_all=”1px” border_color_all=”#cecece” global_colors_info=”{}”]

데이터 사이언스 분야로 대학원 진학을 준비 중인 대학생

배웠던 내용들을 한번에 큰 그림으로 정리하고 싶은 전공자

[/et_pb_blurb][et_pb_image src=”https://datadiving.net/wp-content/uploads/2021/08/KakaoTalk_20210423_142525692.png” title_text=”KakaoTalk_20210423_142525692″ _builder_version=”4.10.1″ _module_preset=”default” width=”69%” module_alignment=”left” height=”286px” custom_margin=”-276px|||0px|false|false” custom_margin_last_edited=”off|desktop” custom_padding=”13px|14px||74px|false|false” global_colors_info=”{}”][/et_pb_image][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”4.9.10″ _module_preset=”default” custom_padding=”75px|||||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.10″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.9.11″ _module_preset=”default” text_text_color=”#4e6ee3″ text_font_size=”12px” header_font=”Abril Fatface|700|||||||” header_text_color=”#4e6ee3″ header_font_size=”33px” header_3_font=”|300|||||||” header_3_text_color=”#4e6ee3″ header_3_font_size=”23px” header_3_letter_spacing=”-1px” header_3_line_height=”0.7em” custom_margin=”||6px|||” global_colors_info=”{}”]

‘앗! 이건 내 얘기?!’ 싶다면,

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데이터 다이빙을 선택해야 할 때입니다!

[/et_pb_text][et_pb_text quote_border_weight=”1px” quote_border_color=”#e5e5e5″ _builder_version=”4.10.1″ _module_preset=”default” text_text_color=”#232323″ text_font_size=”17px” text_letter_spacing=”-1px” quote_letter_spacing=”-1.5px” quote_line_height=”2.5em” custom_padding=”22px|||330px||” global_colors_info=”{}”]

□ 취준 중에 디지털 역량 부족으로 고생해본 적이 있다

□ 데이터 관련 역량이 필요한 직무로 취업/이직을 희망한 적이 있다.

□ 데이터를 직접 분석해서 새로운 인사이트를 얻어보고 싶다.

□ 자동화를 배워서 퇴근 시간을 1분이라도 앞당기고 싶다.

□ AI, 빅데이터 등 4차산업 트렌드에 뒤처지고 싶지 않다.

[/et_pb_text][/et_pb_column][/et_pb_row][/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.9.11″ _module_preset=”default” background_color=”#000000″ global_colors_info=”{}”][et_pb_row _builder_version=”4.9.11″ _module_preset=”default” custom_padding=”50px||20px||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.10″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.9.10″ _module_preset=”default” text_text_color=”#4e6ee3″ text_font_size=”12px” header_font=”|700|||||||” header_text_color=”#4e6ee3″ header_font_size=”33px” custom_margin=”||6px|||” global_colors_info=”{}”]

강사진

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최고에게 배우세요

[/et_pb_text][/et_pb_column][/et_pb_row][et_pb_row _builder_version=”4.9.11″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.9.11″ _module_preset=”default” global_colors_info=”{}”][et_pb_slider show_image_video_mobile=”on” _builder_version=”4.9.11″ _module_preset=”default” background_color=”#000000″ height=”640px” custom_margin=”-45px||||false|false” custom_padding=”8px||36px||false|false” auto=”on” global_colors_info=”{}”][et_pb_slide heading=”Paul” image=”https://datadiving.net/wp-content/uploads/2021/08/KakaoTalk_20210615_191703307.gif” _builder_version=”4.9.11″ _module_preset=”default” header_font=”Poppins|800|||||||” header_letter_spacing=”-1px” header_line_height=”2.1em” body_font_size=”14px” body_letter_spacing=”-1px” body_line_height=”1.1em” max_width=”85%” body_font_size_tablet=”11px” body_font_size_phone=”” body_font_size_last_edited=”on|tablet” global_colors_info=”{}” sticky_transition=”on”]

학력

現 서울대 전기정보공학 석박통합과정

서울대 전기정보공학부 졸업

경기북과학고등학교 졸업

경력

前 수아랩(SUALAB) 사업 개발팀

Top AI Conference (NIPS, AAAI, ICML, ICLR 등)에서

딥러닝 연구 결과물 발표 및 게재

[/et_pb_slide][et_pb_slide heading=”Won” image=”https://datadiving.net/wp-content/uploads/2021/08/KakaoTalk_20210616_104504494.gif” _builder_version=”4.9.11″ _module_preset=”default” header_font=”Poppins|800|||||||” header_letter_spacing=”-1px” header_line_height=”2.1em” body_font_size=”14px” body_letter_spacing=”-1px” body_line_height=”1.1em” max_width=”85%” body_font_size_tablet=”11px” body_font_size_phone=”” body_font_size_last_edited=”on|tablet” global_colors_info=”{}” sticky_transition=”on”]

학력

서울대 수리과학부 학사 졸업

서울대 통계학과 박사과정 수료

서울대 통계 연구실 박사과정 연구

경력

StanKorea 커뮤니티 활용

패스트캠퍼스 머신러닝 강의 진행

서울대학교 기초학문분야 학문후속세대 선정

서울대학교 R 프로그래밍 관련 강의 진행 및

수리통계학 등 통계학 과목 조교 활동 진행

[/et_pb_slide][et_pb_slide heading=”Alice” image=”https://datadiving.net/wp-content/uploads/2021/08/KakaoTalk_20210615_191304394.gif” _builder_version=”4.9.11″ _module_preset=”default” header_font=”Poppins|800|||||||” header_letter_spacing=”-1px” header_line_height=”2.1em” body_font_size=”14px” body_letter_spacing=”-1px” body_line_height=”1.1em” max_width=”85%” body_font_size_tablet=”10px” body_font_size_phone=”” body_font_size_last_edited=”on|tablet” global_colors_info=”{}” sticky_transition=”on”]

학력

서울대 전기정보공학부 졸업

‘S사’ Research Scholarship

경력

現 ‘S사’ AI 관련 현직자

前 Human-centered Computer Systems Lab

前 서울대 융합과학기술대학원 동적로봇시스템 연구실

[/et_pb_slide][et_pb_slide heading=”Kyu” image=”https://datadiving.net/wp-content/uploads/2021/08/KakaoTalk_20210616_105948195_01.gif” _builder_version=”4.9.11″ _module_preset=”default” header_font=”Poppins|800|||||||” header_letter_spacing=”-1px” header_line_height=”2.1em” body_font_size=”14px” body_letter_spacing=”-1px” body_line_height=”1.1em” max_width=”85%” body_font_size_tablet=”10px” body_font_size_phone=”” body_font_size_last_edited=”on|tablet” global_colors_info=”{}” sticky_transition=”on”]

학력

서울대학교 산업공학과 학사 졸업

서울대학교 산업공학과(금융리스크공학) 석박통합과정

경력

삼성전자 차세대 risk management 모델 구축

포스코인터내셔널 물류 프로세스 모니터링 알고리즘 구축

애니파이브 부품 수명주기 예측 알고리즘 설계

현대캐피탈 Upsell 잠재 고객 분류 알고리즘 구현

현대캐피탈 감사시스템 모델 및 데이터분석 고도화

신한은행 변동성 예측을 위한 인공지능 기법 융합 모형 연구

서울대학교 AI 연구원 인공지능 관련 기술 교육

한국연구재단 금융위험 감시모형 국가연구 프로젝트

[/et_pb_slide][et_pb_slide heading=”Joe” image=”https://datadiving.net/wp-content/uploads/2021/08/KakaoTalk_20210616_105948195.gif” _builder_version=”4.9.11″ _module_preset=”default” header_font=”Poppins|800|||||||” header_letter_spacing=”-1px” header_line_height=”2.1em” body_font_size=”14px” body_letter_spacing=”-1px” body_line_height=”1.1em” max_width=”85%” body_font_size_tablet=”11px” body_font_size_phone=”” body_font_size_last_edited=”on|tablet” global_colors_info=”{}” sticky_transition=”on”]

학력

現 서울대 산업공학 석사 과정

서울대 산업공학과 수석 졸업

경기과학고등학교 졸업

경력

前 OECD Data Analyst

前 E-bay Korea Software Engineer

前 Visual Camp Software Engineer

머신러닝 관련 프로젝트 경험 多

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수료증

이제 경험으로 증명하세요!

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기업교육

출강 기업

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오래 전부터 마음속으로만 생각하고 실천을 하지 못했던 공부를 시작할 수 있는 계기가 되었습니다. 또, 직장 생활과 아이들 때문에 평일 강의, 오프라인 강의를 듣기 힘들었는데 주말 강의, 강의 영상이 제공되니 정말 좋은 기회였습니다.

여러 가지를 배웠지만… 강의 이론은 물론, 제가 공부하던 대학, 대학원 시절과 좀 다른 데이터에 대한 관점이라고 할까… 그때는 클리닝이 잘 된 데이터 셋을 가지고 분석을 하면서도 타인과의 공유보다는 나만 잘 알고 있는 그런 것이 중요했는데 이번 기회를 통해서 내가 더 발전할 수 있는 방향에 대해 생각해 볼 수 있었습니다.

앞으로 의학 관련 데이터 분석을 해 볼 예정인데, 빅데이터라서 이번에 배운 강의 내용을 참고하면서 분석하고, 예전에 쓰던 프로그램들과 비교도 해보고 싶습니다.

앞으로도 이런 온라인 주말 강의를 더 많은 커리큘럼으로 부탁드립니다. 저와 같이 육아를 병행하는 워킹맘들은 더없이 좋은 기회이고 정말 감사했습니다.

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FAQ

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지금이 최저가!

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