데이터 분석을 위한 통계학 : 이론에서 실무까지

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Part 1: 통계학 기초와 데이터의 이해

Chapter 1: 통계학이란 무엇인가?
1.1 통계학의 역할 ······································································ 12
1.2 데이터와 변수 ······································································ 15
1.3 모집단과 표본 ······································································ 18

Chapter 2: 기술통계 - 데이터 요약과 시각화
2.1 중심경향 측도 ······································································ 24
2.2 산포도 측도 ········································································· 29
2.3 데이터 분포 ········································································· 34
2.4 시각화 ·················································································· 39

Chapter 3: 확률과 확률분포
3.1 확률의 기초 ········································································· 44
3.2 이산확률분포 ········································································ 48
3.3 연속확률분포 ········································································ 52

Chapter 4: 추론통계 - 추정과 검정
4.1 점추정과 구간추정 ······························································· 56
4.2 가설검정 기초 ······································································ 60

Part 2: 회귀분석과 시계열 분석

Chapter 5: 단순 회귀분석
5.1 회귀분석의 개념 ··································································· 66
5.2 회귀분석 추론 ······································································ 71
5.3 OLS의 기하학적 해석 ························································· 74
5.4 회귀분석의 기본 가정 ·························································· 76
5.5 횡단면 회귀의 확장: Fama–MacBeth(Eugene Fama 교수 수업 경험) · 77

Chapter 6: 다중 회귀분석
6.1 다중회귀 모형 ······································································ 81
6.2 다중공선성(Multicollinearity) ·············································· 88
6.3 변수 선택과 정보 기준 ·························································· 88
6.4 더미 변수와 교호작용 ·························································· 89
6.5 로지스틱 회귀와 분위 회귀 ················································· 91

Chapter 7: 시계열 분석
7.1 정상성과 단위근 검정 ·························································· 95
7.2 시계열 데이터의 구조와 자기 상관 ····································· 99
7.3 수익률 예측과 할인율(John H. Cochrane 교수 수업 경험) · 102

Chapter 8: 다변량 시계열 분석
8.1 VAR 모형(Vector autoregression) ····························· 106
8.2 공적분과 장기균형 ····························································· 108
8.3 공적분과 VECM ································································ 110
8.4 충격 반응 분석(Impulse Response Function) ·············· 111

Part 3: 머신러닝과 텍스트 마이닝

Chapter 9: 머신러닝 기초
9.1 머신러닝 개요 ···································································· 116
9.2 일반화, 과적합, 모형 평가 ················································ 117
9.3 정규화(Regularization) ····················································· 119

Chapter 10: 의사결정 나무와 앙상블
10.1 의사결정 나무(Decision Tree) ······································· 122
10.2 랜덤 포레스트(Random Forest) ···································· 124
10.3 그래디언트 부스팅 ··························································· 127

Chapter 11: 비지도 학습
11.1 군집 분석(Clustering) ····················································· 130
11.2 차원축소 ·········································································· 134

Chapter 12: 텍스트 마이닝
12.1 텍스트 전처리 ·································································· 138
12.2 텍스트 표현 ····································································· 140
12.3 키워드 추출: KeyBERT ·················································· 142
12.4 텍스트 지수 구축: NKPII 사례 ······································· 143

Part 4: 경영정보시스템과 종합 사례

Chapter 13: 경영정보시스템 개론
13.1 정보시스템의 개념과 발전 ··············································· 147
13.2 정보시스템의 계층 구조: TPS·MIS·DSS·EIS ················· 148
13.3 주요 정보시스템 유형과 데이터 기반 의사결정 ··············· 149
13.4 클라우드 인프라와 MIS의 교육적 의의 ·························· 151

Chapter 14: 데이터베이스와 빅데이터
14.1 데이터베이스, SQL, ETL ················································ 153
14.2 빅데이터 인프라와 데이터 거버넌스 ································ 155

Chapter 15: 사례 1, 함정 MRO 의사결정 지원시스템(DSS) 설계와 구현
15.1 문제 정의, 데이터 아키텍처, ETL ·································· 159
15.2 예측 모형 구축과 DSS 기능 설계 ·································· 161

Chapter 16: 사례 2, 안보 충격 모니터링 시스템과 국방 예산 EIS
16.1 시스템 개요, 데이터 아키텍처, 안보 충격 지수 산출 ····· 164
16.2 VECM 분석, 충격 반응 해석, EIS 설계 ························ 166

부록
Appendix A: Python 환경 설정 ············································ 170
Appendix B: R 기초 ······························································ 171
Appendix C: 통계 검정 요약표 ·············································· 173
Appendix D: 데이터 소스 ······················································· 174
Appendix E: 참고문헌 ···························································· 175