[ 빅데이터 분석기사 ] 실기 준비하는 방법 - 파이썬 책

안녕하세요.

오늘은 빅데이터 분석기사 실기 책에 대해서 살펴보겠습니다.

 

우선 실기 시험에는 파이썬과 R 2가지 도구 중에 1개를 선택할 수 있습니다.

저는 개인적으로 파이썬을 추천합니다.

하지만, 코딩에 자신이 없을 경우에는 R도 좋습니다.

 

 

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1. 파이썬 책 

 

1) 파이썬 한권으로 끝내기 : 데이터분석전문가(ADP) + 빅데이터분석기사 실기대비

출간일 : 2022년 6월 3일

출판사 : 시대고시기획

가격 : 24,300원

 

제1장 시험소개 및 환경구성

제1절 데이터분석 자격시험 소개
제2절 분석환경 설정하기

제2장 데이터 핸들링

제1절 판다스 데이터 구조
제2절 DataFrame 기본
제3절 row/column 선택·추가·삭제
제4절 조건에 맞는 데이터 탐색 및 수정
제5절 데이터 정렬
제6절 데이터 결합
제7절 데이터 요약
제8절 데이터 재구조화
제9절 데이터프레임에 함수 적용하기
제10절 문자열 데이터 변환하기
제11절 날짜 데이터 핸들링

제3장 EDA와 시각화

제1절 EDA의 의미
제2절 막대그래프와 히스토그램
제3절 상자 그림(Box Plot)
제4절 산점도(Scatter Plot)
제5절 선 그래프
제6절 상관관계 시각화
제7절 Pandas Profiling

제4장 데이터 전처리

제1절 데이터 전처리의 의미
제2절 이상치 확인 및 정제
제3절 범주형 변수 처리
제4절 데이터 분할
제5절 데이터 스케일링
제6절 차원 축소
제7절 데이터 불균형 문제 처리

제5장 머신러닝 프로세스

제1절 머신러닝의 의미
제2절 머신러닝 분석 프로세스 설명
제3절 성능평가 기법
제4절 머신러닝 분석 과정 빠르게 맛보기 - 회귀분석
제5절 머신러닝 분석 과정 빠르게 맛보기 - 분류분석

제6장 머신러닝 - 지도학습

제1절 단순 선형 회귀(Simple Linear Regression Model)
제2절 다항 회귀(Polynomial Regression)
제3절 다중 회귀(Multiple Regression)
제4절 로지스틱 회귀(Logistic Regression)
제5절 서포트 벡터 머신(Support Vector Machine)
제6절 K-최근접 이웃(KNN)
제7절 의사결정나무(Decision Tree)
제8절 앙상블(Ensemble)
제9절 나이브베이즈(Naive Bayes) 분류

제7장 통계분석

제1절 통계분석 프로세스
제2절 t-test
제3절 분산분석(ANOVA)
제4절 교차분석(카이제곱 검정)
제5절 선형 회귀분석
제6절 군집분석
제7절 연관분석
제8절 시계열분석

최신 기출동형 모의고사

제1회 기출동형 모의고사
제2회 기출동형 모의고사
제3회 기출동형 모의고사

 

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2) 빅데이터분석기사 실기 한권완성 필답형+작업형

출간일 : 2022년 5월 13일

출판사 : 예문사

가격 : 27,000원

PART 01 필답형

CHAPTER 01 빅데이터 분석 기획
챕터 마무리 문제
CHAPTER 02 데이터 전처리 작업
챕터 마무리 문제
CHAPTER 03 데이터 모형 구축 작업
챕터 마무리 문제
CHAPTER 04 데이터 모형 평가 작업
챕터 마무리 문제

PART 02 작업형

CHAPTER 01 파이썬 기본 문법
CHAPTER 02 파이썬 빅데이터 분석 패키지
CHAPTER 03 빅데이터 분석 실무
CHAPTER 04 A to Z 빅데이터 분석 실습
CHAPTER 05 마무리 문제
CHAPTER 06 마무리 문제 정답 및 해설

PART 03 2021년 기출복원문제

2021년 제3회 기출복원문제
2021년 제2회 기출복원문제
2021년 제3회 기출복원문제 정답 및 해설
2021년 제2회 기출복원문제 정답 및 해설

 

 

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3) 이기적 빅데이터분석기사 실기 기본서

출간일 : 2022년 5월 6일

출판사 : 영진닷컴

가격 : 28,800원

목차
이 책의 구성
실기 시험 안내
QR 목록
실기 시험 응시 가이드
저자 소개/추천사

『1권』

PART 01 빅데이터 분석 개요(필답형 대비)
Chapter 01 데이터 수집과 전처리
Section 01 데이터 수집과 탐색
Section 02 데이터 전처리
Chapter 02 분석 모형 구축과 평가
Section 01 분석 모형 구축
Section 02 분석 모형 평가

PART 02 파이썬 분석
Chapter 01 파이썬 프로그래밍
Section 01 파이썬 프로그래밍 기초
Section 02 파이썬으로 데이터 다루기
Chapter 02 파이썬 제1유형 : 데이터 전처리
Section 01 데이터 탐색
Section 02 데이터 전처리 개요
Chapter 03 파이썬 제2유형 : 데이터 분석
Section 01 빅데이터 분석 과정
Section 02 지도학습-분류
Section 03 지도학습-회귀(예측)
Section 04 비지도학습
Chapter 04 파이썬 모의고사
Section 01 모의고사 작업형 1회
Section 02 모의고사 작업형 2회

『2권』

PART 03 R 분석
Chapter 01 R 프로그래밍
Section 01 R 프로그래밍 기초
Section 02 R로 데이터 다루기
Chapter 02 R 제1유형 : 데이터 전처리
Section 01 데이터 탐색
Section 02 데이터 전처리 개요
Chapter 03 R 제2유형 : 데이터 분석
Section 01 빅데이터 분석 과정
Section 02 지도학습-분류
Section 03 지도학습-회귀(예측)
Section 04 비지도학습
Chapter 04 R 모의고사
Section 01 모의고사 작업형 1회
Section 02 모의고사 작업형 2회

 

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4) 공개적 빅데이터분석기사 실기

출간일 : 2022년 2월 4일

출판사 : 와이즈인컴퍼니

가격 : 30,600원

PART 01 단답형 대비

CHAPTER 01 단답형 예상문제 · 5
CHAPTER 02 단답형 기출문제 · 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 · 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 · 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 · 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 · 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 · 373
01 예제문제 및 기출문제 ····················································374
02 예제문제 및 기출문제 풀이 ···········································384

 

 

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5. 빅데이터 분석 기사 실기 필답형+작업형

출간일 : 2021년 11월 19일

출판사 : 프리렉

가격 : 25,200원

저자 서문
들어가기 전에
출제 유형과 분석

PART 1 그림으로 단답형 박살 내기

_1. 기획가 되기: 빅데이터 분석 기획
__1.1 빅데이터의 이해
__1.2 데이터 분석 계획
__1.3 데이터 수집 및 저장 계획

_2. 탐험가 되기: 빅데이터 탐색
__2.1 데이터 전처리
__2.2 데이터 탐색
__2.3 통계기법 이해

_3. 모델러 되기: 빅데이터 모델링
__3.1 분석 모형 설계
__3.2 분석 기법 적용

_4. 검토자 되기: 빅데이터 결과 해석
__4.1 분석 모형 평가 및 개선
__4.2 분석 결과 해석 및 활용

PART 2 파이썬에 발 담그기

_1. 너무 쉬운 파이썬
__1.1 대화 나누기: 대화식 인터프리터
__1.2 파이썬과 약속하기
__1.3 내 안의 모든 것을 저장하기: 데이터 타입, 변수
__1.4 이거해라 저거해라

_2. 파이썬의 잠재력 활용하기
__2.1 내 생각을 그대로 보여주기: if/elif/else, in, lambda
__2.2 라이브러리library 활용하기
__2.3 유용한 내장 함수 활용하기

PART 3 파이썬으로 데이터 분석 준비하기

_1. 실습 데이터와 실행 환경 구성하기
__1.1 실습 데이터 구성하기
__1.2 파이썬 실습 환경 구성하기
__1.3 실습 데이터 이해하기

_2. 데이터 분석 절차 체득하기
__2.1 데이터 준비하기: 데이터 로드load
__2.2 데이터를 관찰하고 가공하기: 전처리preprocessing
__2.3 학습 데이터로 공부하기: 모델 생성과 모델 검증
__2.4 최종 결과 공유하기

PART 4 파이썬으로 초보 분석가 되기(제1유형 박살 내기)

_1. 단순한 데이터 분석
__1.1 Top 10 구하기
__1.2 결측치 확인하기
__1.3 이상값 확인하기
__1.4 사분위수 구하기
__1.5 순위 구하기

_2. 복잡한 데이터 분석
__2.1 그룹별 집계/요약하기
__2.2 오름차순/내림차순 정렬하기
__2.3 최소최대 변환하기MinMaxScaler
__2.4 빈도값 구하기
__2.5 표준 변환하기StandardScaler
__2.6 유니크한 값 구하기

PART 5 파이썬으로 초보 분석가 탈출하기 (제2유형 박살 내기)

_1. 데이터 분석 연습하기
__1.1 데이터 탐색하기
__1.2 전처리하기
__1.3 학습하고 평가하기
__1.4 결과 제출하기

_2. 분류 모델 수행하기
__2.1 데이터 탐색하기
__2.2 전처리하기
__2.3 학습하고 평가하기
__2.4 결과 제출하기

_3. 예측 모델 수행하기
__3.1 데이터 탐색하기
__3.2 전처리하기
__3.3 학습하고 평가하기
__3.4 결과 제출하기