Syllabus

Course prefix and number, section number, and title

BIOSTATISTICAL COMPUTING - 22068 - BIOS 524 - 001

Semester term and credit hours

Fall 2022, 3.000 Credit hours, 3.000 Lecture hours

Class meeting days/times/location, Instructor name, contact information, and office hours

University course description

BIOS 524. Biostatistical Computing. 3 Hours.

Semester course; 3 lecture hours. 3 credits. Techniques for biostatistical computing are presented by way of contemporary statistical packages. Students learn how to create and manage computer data files. Methods for data entry, preparation of data for analysis and summaritive procedures are covered. Students learn the basics of random number generation and its applications, numerical methods for statistical algorithms, and concepts of numerical accuracy and stability. Advanced topics include interactive matrix and macro languages. Emphasis is placed on computational methods and data management rather than on statistical methods and procedures.

Course prerequisites

• No formal course prerequisites, but basic knowledge of the following will help
  • Basic linear algebra: vectors, matrices, determinants
  • Simple calculus: derivatives, integrals, gradients
  • Some probability theory: probability, random variables, distributions
  • Basic statistics knowledge: descriptive statistics, estimators.
  • (Linear) modeling

Student learning outcomes

1. Learn advanced R programming and reproducible research practices
   
2. Understand principles and tools for R data analysis and visualization
   
3. Implement reproducible analysis reports and presentations

Required texts and/or course materials

• Publicly available course materials will be used. Every reasonable effort has been made to protect the copyright requirements of the materials by referring to the original sources.

• Required hardware - a laptop, Mac or Linux OSs are recommended.

Course schedule

• R/RStudio
• Vector/matrix operations
• Probability distributions and random number generation
• Statistical functions
• RMarkdown, GitHub
• Functions, Packages
• Data management, tidyverse
• R graphics, data visualization
• Interactive web apps with Shiny

The schedule is subject to change.

Final exam date and time

• A take-home final project
  
• Final project should be submitted as a fully reproducible GitHub repository
• The due date is to be announced.

Class attendance

Classes will be held in person and will not be recorded. If you miss a class, you are responsible for submitting homework assignments by regular deadlines.

Grading scale

Standard A-F grading system will be applied:

• A: 90-100
• B: 80-89
• C: 70-79
• D: 60-69
• F: 0-59

Grade categories and weights

• Each homework and the final project will be graded on the scale 0-10, 10 points being the best
• Total homework grade possible - 100 points
  • Missed deadline - minus 3 points for each missed day
  • Missed deadline and not submitted within three days - 0 points
  • Three-day extension may be granted under exceptional circumstances and should be discussed with the instructor before the deadline.

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