In the era of data-driven decision-making, fluency in a programming language that combines statistical power with rapid prototyping is indispensable. This course is designed to teach essential programming skill necessary to progress confidently to more advanced analytics and machine-learning courses. Students will learn to use a relevant R programs and packages required for applied data manipulation.
Topics covered in this course are: i) R and RStudio programs, ii) Scripts, Environment, Projects, Console and help in R, iii) Assignments, Vectors, basic I/O, iv) Atomic Classes, Data Frames, Lists, v) Numeric, Logical, Categorical Operations, vi) Filtering, Conditional Selection, vii) for, while, Vectorization vs. Iteration, viii) Functions, Arguments, Returns, ix) Time Series Objects, Time Series Manipulations.
Course is an applied course, hence, students will be able to use R for elementary tasks. The content of the course is designed to be dynamic and changing to follow new developments.
The content of the course includes (but not limited):
| Week | Theme | Key Competencies |
|---|---|---|
| 1 | Introduction: Programming for Data Science | Role of code in reproducible analytics, scripting vs. point-and-click |
| 2 | Installing R, RStudio, Rstudio 101 | Environment setup on Windows, CRAN |
| 3 | Learning Resources | CRAN, RStudio Cloud, cheat sheets, community forums |
| 4 | Basic R Syntax/Commands | Assignment, vectors, calculator, combining |
| 5 | Style Guide, Coercion | coercion rules |
| 6 | Core Data Types, Subsetting | Atomic classes, data frames, lists |
| 7 | Core Data Types, Subsetting | Atomic classes, data frames, lists |
| 8 | Getting Data In and Out | Basic Import/Export, csv, xlsx |
| 9 | Advanced Logical Operations | Boolean algebra, filtering, conditional selection |
| 10 | Control Flow, Functions | Branching logic, nested conditions, vectorization, iteration, function, arguments, scope, return values |
| 11 | Control Flow, Functions | Branching logic, nested conditions, vectorization, iteration, function, rguments, scope, return values |
| 12 | Time-Series Objects in R | ts, xts, zoo structures; date-time classes |
| 13 | R Examples/Exercises | R Example for all parts |
| 14 | Data Wrangling Paradigms: Teaser (MIS 208) | tidyverse |
Main References:
Suggested References:
Software:
R, RStudio (download links and setup instructions will be provided), and relevant R packages.
Attendance: Regular attendance is expected and will be rewarded.
Late Submissions: Assignments submitted late will incur a penalty unless prior approval is granted.
Academic Integrity:
Academic integrity is fundamental to the academic mission of the university. Acts of academic dishonesty, including but not limited to plagiarism, cheating, fabrication, or unauthorized collaboration, undermine the learning process and violate university policies.
Specific guidelines include:
Plagiarism: Using someone else’s work, ideas, or words without proper attribution is strictly prohibited. This includes copying and pasting from any source, paraphrasing without citation, or submitting another person’s work as your own.
Cheating: Unauthorized use of materials, devices, or information during exams or assignments, including sharing or receiving answers, is not allowed.
Fabrication: Falsifying or inventing data, citations, or research is a breach of academic integrity.
Collaboration: While collaboration on group assignments may be permitted, sharing answers or work on individual tasks is not acceptable unless explicitly authorized.
Consequences: Violations of academic integrity will be addressed following the university’s academic policies, potentially leading to penalties such as assignment failure, course failure, or further disciplinary actions.