3. 강의목표
Data science requires integration of statistical knowledge, computing skill, data-related technique, and communication skills. This course introduces the fundamental concepts of statistics and their application in the business area. Students will learn to collect, analyze, interpret, and present data in a business context. Based on statistical knowledge on descriptive statistics, probability and hypothesis testing, we learn how to analyze data depending on data type and which statistical method is appropriate for each subject matter.
● Understand and apply basic statistical technique
● Analyze and interpret data using descriptive and inferential statistics.
● Use statistical software to perform data analysis.
● Present statistical findings for efficiency in business/industry sector
4. 강의선수/수강필수사항
No Prerequisite
5. 성적평가
Attendance 5% (0.1 off per absence)
Homework (Quiz) 25%
Short term report 10% (at the end of semester)
Midterm exam 30%
Final exam 30%
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
Eseentials of Statistics for Business and Economics, 10/E (Asia Edition)
|
Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson Phd, Dennis J. Sweeney, Thomas A. Williams
|
Cengage Learning
|
2023
|
978-981-5119-35-0
|
7. 참고문헌 및 자료
Statistics for Management and Economics. Keller, G. (2022). Cengage Learning
8. 강의진도계획
1 Data and Statistics (Chap1)
2 Descriptive Statistics (Chap2) (Data Visualization)
3 Descriptive Statistics (Chap3) (Numerical Measures) - Location measures, Variability, Boxplots, Correlation.
4 Introduction to Probability (Chap4)- Random experiment, Basic probability, Conditional probability
5 Discrete Prob. Distribution (Chap5)- Continuous Prob. Distribution (Chap6) Binomial, Uniform, Normal Prob. Dist.
6 Interval Estimation (Chap8) - Confidence Interval, Sample size, Margin of error
7 Data Analytics I -Data visualization & Data exploration with real data
8 Midterm Exam
9 Hypothesis Testing (Chap9 & 10) - Two samples (Mean, Proportion)
10 Chi-Square test (Chap12)- Chi-Square Test for Independence
11 ANOVA (Chap13) - Analysis of Variance
12 Simple Regression (Chap14) - Regression, Assumption
13 Data Analytics II -Prediction model : Regression, Residual plot, Data exploration
14 Multiple Regression (Chap15) - Multicollinearity, Variable selection
15 Multiple Regression (Chap15) - Optimal prediction model
16 Final Exam
9. 수업운영
* Homework : biweekly, Quiz in occasion
* Data analytical tool : ECMiner tool (Menu-driven software, no coding) will be given. ECMiner tool functions similarly to the SAS OnDemand interface,
* Real data in public and private sector. Utilize various types of data.
HW : late submission 10% off within 24 hrs, 20% off after 24hrs
Short term report : Report of 10+_page(ppt) for data analysis (data will be given) at the end of semester
10. 학습법 소개 및 기타사항
Data analysis practice using analytical software tool
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청