- Lecturer: Choong-Wan Woo, Ph.D. Assistant professor (GBME).
- Office: N-center, 86335
- Web: Cocoan lab
- E-mail: please send the message through iCampus or send me an email to choongwan.woo at gmail dot com
- Class: (online) Mon 1:30-2:45, (offline) Wed 12:00-1:15 (flipped class)
Data are everywhere. Data science already became a key element in many research and industrial areas. The primary aim of this course is to learn basic concepts and skills for data analysis, including concepts of random variables, sampling distributions, hypothesis testing, linear modeling, data visualization, etc., preparing you for your life after graduation in this data-are-everywhere age. However, this is not the only aim of this class. Statistics goes beyond just data analysis, I believe. Statistics is a way of thinking and reasoning. It helps with scientific reasoning and critical thinking. Thus, I hope this class could provide you with some useful tips for scientific thinking and reasoning.
This class is designed in a flipped classroom format, which is a new way of teaching and learning. Different from the traditional learning environment (passively listening to the lecture in the class and doing homework at home), in the flipped classroom, you will listen to the lecture at home and do homework and practice in the classroom. I personally experienced this format of learning during my Ph.D. (for the Machine Learning class) and deeply enjoyed it. I found the flipped classroom helped students stay engaged and provided a good environment for hands-on experience.
For each week, we will use one class for watching pre-recorded video lectures (from the last few years' work) and the other class for the activities. It is crucial to watch the video lectures to participate in the class activity. Thus I might give you a quiz for each class.
Main textbook:
"Stats: Data and Models" by De Veaux, Velleman, and Bock
Supplements (not required):
"Statistical Thinking for the 21st Century" by Russ Poldrack Link
"Seeing theory" Link
"The Seven Pillars of Statistical Wisdom" by Stephen M. Stigler Amazon Link
"History of Statistics Reading Group" (CMU) Link
Teaching how to use statistics packages or programming is not the main focus of this class. This class is more about statistical methods and theories. However, software packages and computer programming are actually essential in learning statistics. Therefore, I will provide some lectures about software packages for statistical analysis (e.g., JAMOVI). In addition, I might use Matlab, Python, or R sometimes. You can download Matlab through SKKU. Python and R are open-source programming languages.
TBD
Absolute evaluation will be used for this course.
- Attendance (40%)
- Participation (including quiz) (35%)
- Final exam (25%)
PR: pre-recording | OF: offline | A: activity | V: video
| Week | Video lectures | Class | Chapters or papers |
|---|---|---|---|
| Week 1 | |||
| 3/4 | (PR) V01: Data | Ch.1-2 | |
| 3/6 | (OF) Course overview | ||
| Week 2 | |||
| 3/11 | (PR) V02: Data visualization | Ch.2-3 | |
| (PR) V03: Comparing distribution | Ch.4 | ||
| 3/13 | (OF) A01: Cognitive Errors and Statistical thinking | Tversky & Kahneman, 1974 | |
| Week 3 | |||
| 3/18 | (PR) V04: Normal model | Ch.5 | |
| (PR) V05: Scatterplots and correlation | Ch.6 | ||
| 3/20 | (PR) V06: Linear regression | Ch.7 | |
| Week 4 | |||
| 3/25 | (PR) V07: More about regression, re-expressing data | Ch.8-9 | |
| 3/27 | (OF) A02: Boxplot | ||
| Week 5 | |||
| 4/1 | (PR) V08: Sampling | Ch.8-9 | |
| (PR) V09: Design Experiment | Ch.11-12 | ||
| 4/3 | (OF) A03: Normal Probability Plot | ||
| Week 6 | |||
| 4/8 | (PR) V10: Probability and Bayes theorem | Ch.13-14 | |
| (PR) V11: Random variables | Ch.15 | ||
| 4/10 | 국회의원 선거 | ||
| Week 7 | |||
| 4/15 | (PR) V12: Probability models | Ch.16 | |
| 4/17 | (OF) A04: Regression to the mean | ||
| Week 8 | |||
| 4/22-4/24 | Midterm | ||
| Week 9 | |||
| 4/29 | (PR) V13: Softwares and programming languages | ||
| 5/1 | (PR) V14: Sampling distribution, central limit theorem | Ch.17 | |
| Week 10 | |||
| 5/6 | (PR) V15: Confidence interval for proportions | Ch.18 | |
| (PR) V16: Hypothesis testing, P-values | Ch.19 | ||
| 5/8 | (OF) A05: Bayes theorem and random variables | ||
| Week 11 | |||
| 5/13 | (PR) V17: Inferences about means | Ch.20 | |
| (PR) V18: More about tests and intervals | Ch. 21 | ||
| 5/15 | 석가탄신일 | ||
| Week 12 | |||
| 5/20 | (PR) V19:Comparing groups | Ch. 22 | |
| (PR) V20: Paired t-test | Ch. 23 | ||
| 5/22 | (OF) A06: Probability models | ||
| Week 13 | |||
| 5/27 | (PR) V21: Comparing counts | Ch. 24 | |
| (PR) V22: Inferences about regression | Ch. 25 | ||
| 5/29 | (OF) A07: Central limit theorem | ||
| Week 14 | |||
| 6/3 | (PR) V23: Analysis of Variance (ANOVA) | Ch. 26 | |
| (PR) V24: Multifactor ANOVA | Ch. 27 | ||
| 6/5 | (OF) A8: t-test | ||
| Week 15 | |||
| 6/10 | (PR) V25: Multiple regression | Ch.28 | |
| (PR) V26: Multiple regression wisdom | Ch. 29 | ||
| 6/12 | (OF) A9: chi-square and regression | ||
| Week 16 | |||
| 6/19 | Final |
Note.