Syllabus for BIOSTAT615 - University of Michigan School of Public

Transcription

Syllabus for BIOSTAT615 - University of Michigan School of Public
Biostatistics 615 – Statistical Computing
Fall 2014, September 2 – December 10
Instructor: Hui Jiang
Email: [email protected]
Office: M4523 SPH II
Time: Tuesday & Thursday 8:30AM - 10:00AM
Room: M1112 SPH II
Couse Web Page: CTools
Objective
Biostatistics 615 aims to provide students with a practical understanding of computational issues
related to the implementation of statistical methods, from basics of programming language to inner
workings of sophisticated statistical methods. C++ and R languages will be used throughout the course.
Prerequisites
Linear algebra (matrix theory) and basic statistics including probability distribution, linear model and
hypothesis testing. Biostatistics 601 or equivalent is required prior to or in parallel to taking Biostatistics
615. Previous experience in programming is not required, but those who do not have previous
programming experience should expect to spend additional time studying and learning to be familiar
with a programming language during the coursework.
Books
1. (Optional) Cormen, Leiserson, Rivest, and Stein, "Introduction to Algorithms", Third Edition, The
MIT Press, 2009
2. (Optional) Stephen Prata, “C++ Primer Plus”, Sixth Edition, Addison-Wesley, 2011
3. (Optional) Press, Teukolsky, Vetterling, and Flannery, "Numerical Recipes", 3rd Edition,
Cambridge University Press, 2007
Grading
Homework assignments will be given out at approximately every two weeks. You are encouraged to
discuss homework problems with fellow students; however, you must implement and write up the
assignment on your own. Plagiarism will not be tolerated.
 Homework: 60%
 Group (team of 2) Final Project: 40%
Standards of Academic Act
The following is an extract from the School of Public Health's Student Code of Conduct.
Student academic misconduct includes behavior involving plagiarism, cheating, fabrication, falsification
of records or official documents, intentional misuse of equipment or materials, and aiding and abetting
the perpetration of such acts. The preparation of reports, papers, and examinations, assigned on an
individual basis, must represent each student’s own effort. Reference sources should be indicated
clearly. The use of assistance from other students or aids of any kind during a written examination,
except when the use of books or notes has been approved by an instructor, is a violation of the
standard of academic conduct.
In the context of this course, any work you hand-in should be your own and any material that is a
transcript (or interpreted transcript) of work by others must be clearly labeled as such.
Topics
 Part I : Basics of C++, R, Data Structure and Algorithms
o Introduction to C++
o Computational Time Complexity
o Key Data Structures
o Sorting Algorithms
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
Divide and Conquer Algorithms
Dynamic Programming
Introduction to R
Interfacing C++ and R languages
Part II : Numerical and Statistical Methods (a selected subset of the following topics will
be covered)
o Matrix Algebra and Least Square Methods
o Root Finding and Optimization
o Hidden Markov Models
o Expectation-Maximization (EM) Algorithm
o Random Number Generation
o Numerical Integration and Importance Sampling
o Markov-Chain Monte-Carlo (MCMC) Methods
Core Competencies
The Council for Education in Public Health recommends every course document in the Public Health
competencies covered in its subject syllabus. This is the list of competencies covered by Biostatistics
615
 Biostatistics
o Describe the roles biostatistics serves in the discipline of public health.
o Describe basic concepts of probability, random variation, and commonly used statistical
probability distributions.
o Describe preferred methodological alternatives to commonly used statistical methods
when assumptions are not met.
o Distinguish among the different measurement scales and the implications for selection of
statistical methods to be used based on these distinctions.
o Apply descriptive techniques commonly used to summarize public health data.
o Apply common statistical methods for inference.
o Apply descriptive and inferential methodologies according to the type of study design for
answering a particular research question.
o Apply basic informatics techniques with vital statistics and public health records in the
description of public health characteristics and in public health research and evaluation.
o Interpret results of statistical analyses found in public health studies.
o Develop written and oral presentations based on statistical analyses for both public
health professionals and educated lay audiences.
 Epidemiology
o Apply the basic terminology and definitions of epidemiology.
o Calculate basic epidemiology measures.
o Communicate epidemiologic information to lay and professional audiences.
o Draw appropriate inferences from epidemiologic data.
o Evaluate the strengths and limitations of epidemiologic reports.
 Health Behavior and Health Education (Social and Behavioral Sciences)
o Apply evidence-based approaches in the development and evaluation of social and
behavioral science interventions.
 Cross-Cutting Competencies
o Demonstrate effective written and oral skills for communicating with different audiences
in the context of professional public health activities.
o Articulate an achievable mission, set of core values, and vision.
o Demonstrate team building, negotiation, and conflict-management skills.
o Apply evidence-based principles and the scientific knowledge base to critical evaluation
and decision making in public health.
o Appreciate the importance of working collaboratively with diverse communities and
constituencies (e.g., researchers, practitioners, agencies, and organizations).

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