2. Instructor Information
|
Name |
Lee Hyeseon |
Department |
Dept. of Industrial & Management Eng. |
| Email address |
hyelee@postech.ac.kr
|
Homepage |
|
| Office |
|
Office Phone |
054-279-8222 |
| Office Hours |
|
3. Course Objectives
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. Prerequisites & require
No Prerequisite
5. Grading
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. Course Materials
| Title |
Author |
Publisher |
Publication
Year/Edition |
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. Course References
Statistics for Management and Economics. Keller, G. (2022). Cengage Learning
8. Course Plan
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. Course Operation
* 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. How to Teach & Remark
Data analysis practice using analytical software tool
11. Supports for Students with a Disability
- Taking Course: interpreting services (for hearing impairment), Mobility and preferential seating assistances (for developmental disability), Note taking(for all kinds of disabilities) and etc.
- Taking Exam: Extended exam period (for all kinds of disabilities, if needed), Magnified exam papers (for sight disability), and etc.
- Please contact Center for Students with Disabilities (279-2434) for additional assistance