Instructor: Dr. Liping Liu, College of Business Administration Building 360, 330-972-5947, liping@uakron.edu
Text Books:
- Tilman M. Davies, The Book of R: A First Course in Programming and Statistics, No Starch Press, 2016. ISBN 978-1593276515
- Liping Liu, Lecture Notes on R Programming, available on ecourse.org
Time and Location: Mondays: 6:00-8:30 PM; August 26-December 10, 2024. Regular Classroom: CBA 176 (Computer Lab).
Office Hours: 1:30 – 3:30 PM Mondays and Wednesdays
Course Description: This is introduction to statistical programming in R. It covers the issues on how to load data from various sources such as text files, databases, and other statistical packages, how to create objects such as vectors, matrices, lists, and data frames, how to use programming controls for non-routine computation, how to write functions to maximize reusability of code, how to describe and visualize data, how to compute and visualize probability distributions, how to conduct parametric and non-parametric tests, how to conduct linear and non-linear regressions, how to conduct simulation and bootstrapping, and how to create dynamic 3D visualizations. Optional topics may include machine learning techniques such as decision tree learning and Bayesian learning.
Philosophy: This course prepares students for a career for Data Analytics. It teaches how to use R for analytical programming and builds a foundation for advanced business analytics. It does not require any programming or database courses as prerequisites, but as learning any new language such as French or Spanish, practice is mandatory toward mastery.
An undergraduate course on statistics and probability is assumed. Please review your statistics text, or read a book such as "Statistics for Dummies", or follow any youtube.com tutorial on Statistics and Probability such as https://www.youtube.com/watch?v=sbbYntt5CJk before the first midterm exam.
Course Objectives: Upon satisfactory completion of this course, a student should be able to
- Understand the basic elements of R including primitive data objects and common data structures: vectors, matrices, list, and data frames
- Understand the basic programming principles on how to create data objects, use loops and decision controls, and create custom functions
- Gain hands-on skills on using R interactive environment, managing extension packages, and integrating data sources from Oracle, XML, the web, and statistical software tools.
- Build hands-on experience on how to examine, summarize, explore, visualize, and transform data using R
- Apply R for various statistical computing such as data visualization, hypothesizing testing, regression, Bayesian conditioning, and statistical simulation
Weekly Schedule:
- Day 1: Primitive data types, object variables, simple numerical computation, and data conversion
- Day 2: Labor Day (no class)
- Day 3: Package, Environment, and File Management
- Day 4: Vectors and Matrices
- Day 5: Lists and Data frames
- Day 6: Load Data from External Data Sources: Data Files and Databases
- Day 7: Midterm Exam
- Day 8: Programming Controls
- Day 9: Programming Functions
- Day 10: Visualize Data
- Day 11: Visualize Probability Distributions
- Day 12: Statistic Tests in R
- Day 13: Random Sampling, Simulation, and Bootstrapping in R
- Day 14: 3D Visualization in R
- Day 15: Final Exam (6:00-8:00 PM on 12/9/2024)
Exams: This course will have two major exams as scheduled above. Each exam includes both hands-on and written problems.
Assignments: Homework is assigned once a week for 12 weeks; each consists of conceptual questions and hands-on projects classified into three grading categories: correctness, closeness, and completeness. The correctness problems will be graded by ecourse.org, and closeness questions are graded and/or commented by instructors. Students will earn points automatically for each completeness question if it is finished (it has to be deemed complete). Assignments are due at the beginning of each class meeting. No late homework will be graded. Please show your work in a neat and orderly fashion. If it is a written assignment, write or type your work on one side and in every other line. Use standard size paper (8 1/2'' by 11''). Do not use spiral notebook paper.
Attendance: Attendance is MUST and will be 10% of your final grade. Attendance will be managed by ecourse.org system. The formula for computing your attendance grade is non-linear. It will take 2 points off for the first absence and 7 points off for the second absence. If you missed the equivalent of three-week classes, you fail the course automatically. Under special situations, you can take some classes online with the following guidelines:
- You must obtain permission from the instructor at least one day ahead of each online session
- Follow the lectures or recordings to perform all in-class hands-on exercises and take notes. Within one day from the class submit your notes and the finished exercises to ecourse.org as Proof of Attendance.
- All weekly assignments are due at the same time as in-person classes. All exams must be onsite.
Quizzes: I will use quizzes regularly to check your completion or preparation of assignments
Makeup: Each student with appropriate excuses may have at most one chance to makeup homework or quiz. Note that it is your privilege but not right to have this special favor.
Grades: Your final grades will be calculated by the following formulas:
40% (HW or Projects) + 50% (Tests) + 10% (Attendance)
A = 93-100%; A– = 90-92%; B+ = 87-89%; B = 83-86%; B– = 80-82%; C+ = 77-79%; C = 73-76%; C– =70-72%; D = 60-69%; F = 59% and less
Misconduct: Academic misconduct by a student shall include, but not limited to: disruption of classes, giving and receiving unauthorized aid on exams or in the preparation of assignments, unauthorized removal of materials from the library, or knowingly misrepresenting the source of any academic work. Academic misconduct by an instructor shall include, but not limited to: grading student work by criteria other than academic performance or repeated and willful neglect in the discharge of duly assigned academic duties.
On Collaboration: All for-credit assignments, except for those designated as group projects, must be done independently, and collaboration in providing or asking for answers to those assignments constitutes cheating.
On AI Tools: In this class, I allow students to use AI tools to help their learning. However, submitting AI generated work for credits is a violation of academic code. If a submitted work is suspected to be AI generated, the student will be asked to reproduce the submitted work in front of the instructor.
School Rule Cited: For graduate students that have been caught cheating: First offense = either a zero on the exam or assignment, or an F in the course; Second offense = Either an F in the course or expulsion (depending upon the punishment of the first offense)
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