EMSE 6574: Programming for Analytics
Course Information:
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Level:
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Description:
This course provides students with a foundation in programming for data analytics using the R computing language. Emphasis will be on learning how to read, write, test, and debug code to produce comprehensible and reproducible analyses. Throughout the course, students will primarily work on individual programming assignments to help practice coding skills. Students will be assessed through quizzes, exams, and a final project. Teaching will involve interactive lectures and recitations with a lot time spent live coding. At the end of the semester, students will demonstrate mastery of the course’s topics by working on a 3-week term project. This course will prepare students for higher level courses in data analytics.
Prerequisites:
There are no prerequisites, and no prior programming experience is necessary to succeed in this class.
Learning Outcomes:
Having successfully completed this course, students will be able to:
This course provides students with a foundation in programming for data analytics using the R computing language. Emphasis will be on learning how to read, write, test, and debug code to produce comprehensible and reproducible analyses. Throughout the course, students will primarily work on individual programming assignments to help practice coding skills. Students will be assessed through quizzes, exams, and a final project. Teaching will involve interactive lectures and recitations with a lot time spent live coding. At the end of the semester, students will demonstrate mastery of the course’s topics by working on a 3-week term project. This course will prepare students for higher level courses in data analytics.
Prerequisites:
There are no prerequisites, and no prior programming experience is necessary to succeed in this class.
Learning Outcomes:
Having successfully completed this course, students will be able to:
- Read, write, test, and debug code to produce comprehensible and reproducible analyses.
- Use computational problem solving to solve problems.
- Create visualizations of data.
- Explain the efficiency of algorithms by predicting the Big-O run time of code.
- Design and write an R package and deploy it on github.
- Generate fully reproducible reports in RMarkdown that contain contain code, equations, visualizations, and narrative text.
