3. 강의목표
Data science integrates statistical knowledge, computing skill, data-related technique, and communication skills. This course introduces the foundational concepts of statistics and their applications in the business domain. Students will learn to collect, analyze, interpret, and present data within a business context. By mastering descriptive statistics, probability, and hypothesis testing, students will develop the ability to analyze data based on its type and select the most appropriate statistical methods for each subject matter.
● Understand and apply basic statistical technique
● Analyze and interpret data using descriptive and inferential statistics
● Utilize statistical software to conduct data analysis
● Present statistical insights effectively in the business or industry sector
4. 강의선수/수강필수사항
No Prerequisite
5. 성적평가
Attendance 5% (0.1 off per absence)
Homework (Quiz) 25%
Midterm exam 30%
Final exam 30%
Short term report 10% (at the end of semester)
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 : Tabular and Graphical display
3 Descriptive Statistics (Chap3) -Numerical Measures : Location measures, Variability, Boxplots, Correlation.
4 Introduction to Probability (Chap4)- Random experiment, Basic probability, Conditional probability
5 Discrete & Continuous Prob. Distribution (Chap5, Chap6)- Binomial, Uniform, Normal Prob. Dist.
7 Sampling Distribution (Chap7) - Central limit theorem, Sampling error
6 Interval Estimation (Chap8) - Confidence Interval, Sample size, Margin of error
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 Multiple Regression (Chap15) - Multicollinearity, Variable selection
14 Data Analytics - Data exploration, Prediction model
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
If you are unable to attend the class, you must complete the recorded video by Sunday for the corresponding week for attendance to be recognized.
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