Course Information
Course title
 Computational Statistics for Data Analytics 
Semester
 109-2 
Designated for
 COLLEGE OF ENGINEERING  DEPARTMENT OF CIVIL ENGINEERING  
Instructor
 LI-PEN WANG 
Curriculum Number
 CIE5140 
Curriculum Identity Number
 521 U9270 
Class
  
Credits
 3.0 
Full/Half
Yr.
 Half 
Required/
Elective
 Elective 
Time
 Monday 7,8,9(14:20~17:20) 
Remarks
 Restriction: sophomores and beyond
The upper limit of the number of students: 40.
The upper limit of the number of non-majors: 10. 
  
Course introduction video
 
Table of Core Capabilities and Curriculum Planning
 Association has not been established
Course Syllabus
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Course Description

This course is an extension of the Engineering Statistics and Computer Programming courses. Students will work extensively with real-world data (relevant to engineering, physics and environment) during classes. The knowledge learned from the aforementioned two courses will be briefly reviewed and further strengthened through a series of hands-on projects. This course will enable students to develop solid data analytical skills and problem-solving mindsets, which will be useful whether they decide to work in industry or academia in the future. 

Course Objective
 With the development of sensing and computational technologies, the amount of data that modern engineers have to handle on a daily basis has largely increased. The aim of this course is to provide civil engineering students proper training to ensure that they will be equipped with essential skills to explore unknown data, as well as to develop data scientists’ problem-solving and self-learning mindsets. 
Course Requirement
 Computer programming
Engineering statistics 
Student Workload (expected study time outside of class per week)
  
Office Hours
  
Designated reading
  
References
 Larry Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer, USA, 2004.
Allen B. Downey, Think Bayes: Bayesian Statistics Made Simple, O'Reilly, 2012.
Allen B. Downey, Think Stats: Probability and Statistics for Programmers, O'Reilly, 2014.
Allen B. Downey, Think Stats: Exploratory Data Analysis in Python, O'Reilly, 2014.
Annette J. Dobson & Adrian G. Barnett, An Introduction to Generalized Linear Models, 4th Edition, Chapman & Hall/CRC, 2018.
Christian Onof, Lecture Notes for Statistics, Imperial College London, 2017. 
Grading
  
No.
Item
%
Explanations for the conditions
1. 
 Lab/hands-on Projects 
30% 
  
2. 
 Quiz 
15% 
  
3. 
 Mid-term exam 
25% 
  
4. 
 Final-term exam / project 
30% 
  
 
Progress
Week
Date
Topic
Week 1
 02/22  Course Introduction 
Week 2
 03/01  No Class - Make-up holiday 
Week 3
 03/08  Quiz 0 (30 mins.)
Descriptive Statistics 
Week 4
 03/15  Probability, random variables 
Week 5
 03/22  Probability distributions 
Week 6
 03/29  Quiz 1 (50 mins.)
Maximum likelihood estimation 
Week 7
 04/05  No Class - Make-up holiday 
Week 8
 04/12  Probability distribution fitting 
Week 9
 04/19  Midterm Exam 
Week 10
 04/26  Common scientific data file formats, public datasets and access to online data via Python 
Week 11
 05/03  Confidence Intervals 
Week 12
 05/10  Statistical tests 
Week 13
 05/17  Linear regression (LS, MLE) 
Week 14
 05/24  Quiz 2 (50 min)
Spatial Statistics 
Week 15
 05/31  Bayes' theorem and its applications (I) 
Week 16
 06/07  Bayes' theorem and its applications (II) 
Week 17
 06/14  No Class - Holiday 
Week 18
 06/21  Final Exam / project