Statistics and Data Analytics for Technology
สถิติและการวิเคราะห์ข้อมูลสำหรับงานเทคโนโลยี
Course description
Descriptive statistics, probability, data distributions, estimation, hypothesis testing, correlation, regression, data preparation, visualization, and software tools for technology data analytics.
Thai description
สถิติเชิงพรรณนา ความน่าจะเป็น การแจกแจงข้อมูล การประมาณค่า การทดสอบสมมติฐาน ความสัมพันธ์ การถดถอย การเตรียมข้อมูล การแสดงผลข้อมูล และการใช้ซอฟต์แวร์ในการวิเคราะห์ข้อมูลทางเทคโนโลยี
Course learning outcomes (CLO)
| CLO | Outcome | PLO |
|---|---|---|
| CLO1 | Summarise and present data with descriptive statistics | PLO1PLO4 |
| CLO2 | Use statistical inference to evaluate test results | PLO1PLO4 |
| CLO3 | Analyse flight-log data with Python | PLO4 |
Learning modules
1Data and descriptive statistics
Weeks 1–3 · 30 h1 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Descriptive statistics and probability distributionsIn development
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
2Probability and distributions
Weeks 4–6 · 30 h1 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Descriptive statistics and probability distributionsIn development
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
3Confidence intervals and hypothesis tests
Weeks 7–9 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Statistical inferenceIn development
- SciPyTH
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
4Data analysis with pandas
Weeks 10–12 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Data handling and visualisation with PythonIn development
- pyulogTH
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
5Experiments and interpreting test results
Weeks 13–15 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
Assessment (draft)
| Labs and worksheets | 35% |
| Module quizzes | 10% |
| Midterm examination | 20% |
| Mini-project or practical exam | 35% |
Knowledge domain
Key references
- Montgomery, D. C., & Runger, G. C. (2018). Applied statistics and probability for engineers (7th ed.). Wiley.
- McKinney, W. (2022). Python for data analysis (3rd ed.). O'Reilly.