2017 Data Analysis for the Chemical Engineering

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Undergraduate major in Chemical Science and Engineering
Instructor(s)
Okawara Shinichi  Yoshikawa Shiro 
Course component(s)
Lecture
Mode of instruction
 
Day/Period(Room No.)
Tue1-2(南4号館情報ネットワーク演習 第1演習室,第2演習室)  
Group
-
Course number
CAP.E241
Credits
1
Academic year
2017
Offered quarter
1Q
Syllabus updated
2017/4/25
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

[Summary of the course] The course teaches fundamentals of data analysis useful in chemistry-related research fields such as chemical engineering, applied chemistry, polymer engineering with practices. The course also teaches operation of data analysis on computer based on understanding of mathematical background of data analysis with practices on computer.
[Aim of the course] The aim of the course is for students to understand expression methods of data; definitions of statistics; regression analysis; Chi-squared test; discrete probability distribution; continuous probability distribution; probability density distribution; Fourier series. The course also aims that students can operate data analysis on computer by getting ability to write computer program.

Student learning outcomes

By completing this course, students will be able to:
(1) Explain statistics of data
(2) Conduct regression analysis and explain the relation between two variables
(3) Conduct reliability test
(4) Explain discrete probability distribution, continuous probability distribution and probability density distribution
(5) Conduct Fourier series expansion of periodic function and explain characteristics of the function by interpreting Fourier coefficients
(6)Write and use program for data analysis on computer

Keywords

data analysis, statistics, probability, reliability test, Fourier series

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

For each topic, its fundamentals are taught, and subsequently, programming practices are conducted on computer to enhance understanding and programming skills.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Fundamentals of programming (input/output, variable, data type, operator) To program input/output, variable, data type, operator.
Class 2 Fundamentals of programming (conditional branch, comparison operator, loop) To program conditional branch, comparison operator, loop.
Class 3 Solution of equation, linear equation system, numerical integration To write programs for solving equation/linear equation system and for co nducting numerical integration.
Class 4 Fundamentals of finite difference method and approximate solution of differential equation To explain finite difference method and approximate solution of differential equation.
Class 5 Analysis of transport phenomena by finite difference method To analyze transport phenomena by finite difference method.
Class 6 Methods of statistics To explain methods of statistics.
Class 7 Discrete probability distribution and continuous probability distribution To explain discrete probability distribution and continuous probability distribution.
Class 8 Fundamentals of random variation data analysis, Fourier transform and spectral analysis To explain Fourier series.

Textbook(s)

Text book specified by the instructor.

Reference books, course materials, etc.

Unspecified.

Assessment criteria and methods

Learning achievement is evaluated by:
Practices in class: 20%
Final exam: 80%

Related courses

  • CAP.C423 : Computational Fluid Dynamics
  • CAP.C201 : Transport Phenomena I (Momentum)
  • CAP.C202 : Transport Phenomena II (Heat)
  • CAP.C203 : Transport Phenomena III (Mass)
  • CAP.C441 : Transport Phenomena and Operation
  • CAP.C206 : Chemical Reaction Engineering I (Homogeneous System)
  • CAP.C306 : Chemical Reaction Engineering II (Heterogeneous System)

Prerequisites (i.e., required knowledge, skills, courses, etc.)

No prerequisites.

Page Top