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2022 공개적 빅데이터분석기사 실기
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2022 공개적 빅데이터분석기사 실기

  • 김원표
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  • 와이즈인컴퍼니
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  • 2022-02-04 출간
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  • 404페이지
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  • 준비중
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  • ISBN 9791189507589
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본 도서는 한국데이터산업진흥원에서 실시하는 빅데이터분석기사 국가기술자격 실기시험 대비 도서입니다.

 

본 교재는 Python을 활용하여 쉽고 빠른 자격증 취득으로 이어질 수 있도록 도움을 줄 것입니다.

 

최신 개정판에는 단답형 대비 예상문제 및 작업형 예제문제 및 기출문제 풀이를 담아 크게 단답형 및 작업형 대비로 나누었습니다.

단답형 예상문제 및 기출문제, 작업형 대비는 Python과 데이터 다루기, 데이터탐색과 데이터정제, 머신러닝 프로세스, 머신러닝 핵심 알고리즘 총 4부로 구성하였습니다.

 

데이터캠퍼스의 빅데이터 교육은 많은 사람들이 데이터 분석을 쉽게 익히고 활용할 수 있는 기회를 마련하고자 시작되었습니다.

또한 통계 분석ㆍ빅데이터 실무 20년의 전문가 ‘김원표’ 교수의 제대로 된 강의로 기초부터 활용까지 탄탄한 내용을 자랑합니다.

70여개의 대학 연구 기관과 1,000개의 기업, 20,000명의 수강생들을 데이터 분석 전문가로 양성해 낸 데이터캠퍼스의 교육으로

4차 산업 혁명의 시작과 끝인 빅데이터 전문가로 성장해보세요.

합격의 가장 빠른 지름길은 데이터캠퍼스가 정답입니다.

목차


PART 01 단답형 대비

CHAPTER 01 단답형 예상문제 ㆍ 5
CHAPTER 02 단답형 기출문제 ㆍ 9
01 제2회 단답형 기출문제 ····················································10
02 제3회 단답형 기출문제 ····················································12
03 문제 해설 및 정답 ····························································14

PART 02 작업형 대비

CHAPTER 01 파이썬과 데이터 다루기 ㆍ 19
01 빅데이터 분석기사 실기 시험 특징 ·································20
02 파이썬 설치하기 ································································25
03 파이썬 기초 ·······································································33
04 numpy 함수 ·····································································49
05 pandas와 기본 데이터 처리 ···········································56

CHAPTER 02 데이터 탐색과 데이터정제 ㆍ 73
01 단변량 데이터 탐색 ··························································74
02 이변량 데이터 검색 ··························································82
03 이상치 처리 ·······································································86
04 변수 변환 ··········································································94
05 결측치 처리 ·······································································98
06 데이터정제 실전과제 ······················································117

CHAPTER 03 머신러닝 프로세스 A to Z ㆍ 129
01 머신러닝 프로세스 ··························································130
02 머신러닝 맛보기 1. 분류문제 ········································138
03 머신러닝 맛보기 2. 회귀문제 ········································154
04 머신러닝 프로세스 1. 범주변수의 변환 ························166
05 머신러닝 프로세스 2. 데이터셋 분할과 모델검증 ········170
06 머신러닝 프로세스 3. 데이터 정규화 ··························176
07 머신러닝 프로세스 4. 모델훈련과 세부튜닝 ·················186
08 머신러닝 프로세스 5. 모델평가 ·····································194
09 머신러닝 프로세스 6. 다중분류 ·····································198

CHAPTER 04 머신러닝 핵심 알고리즘 ㆍ 203
01 로지스틱 회귀모델 ··························································204
02 K-최근접이웃법(KNN) ·················································213
03 나이브 베이즈 ·································································225
04 인공신경망 ······································································236
05 서포트 벡터머신 ·····························································246
06 의사결정나무 ···································································259
07 랜덤 포레스트 ·································································270
08 투표기반 앙상블 ·····························································282
09 앙상블 배깅(Bagging) ···················································291
10 앙상블 부스팅(Boosting) ···············································297
11 앙상블 스태킹(Stacking) ···············································307
12 선형회귀모델 ···································································314
13 릿지 회귀모델 ·································································322
14 라쏘(Lasso) 회귀모델 ····················································327
15 엘라스틱넷 ······································································332
16 군집분석 ··········································································337
17 DBSCAN ········································································356
18 연관규칙분석 ···································································363

CHAPTER 05 작업형 예제문제 및 기출문제 풀이 ㆍ 373
01 예제문제 및 기출문제 ····················································374
02 예제문제 및 기출문제 풀이 ···········································384
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