ETC1010 Introduction to Data Analysis
Difficulty:
Year Completed: Semester 1, 2022
Prerequisite: N/A
Exemption: N/A (prerequisite for other units)
Mean Setu Score: 66.41%
Clarity of Learning Outcomes: 73.33%
Clarity of Assessments: 64.44%
Feedback: 53.33%
Resources: 63.64%
Engagement: 75.56%
Satisfaction: 68.18%
Subject Content:
Lecture(s) and Tutorial(s):
Textbook(s):
Assessments:
What is R? This introductory unit introduced a new language for data analysis, R studio. This included showing us how to utilise packages in the platform and where to run our code.
With a focus on creating various types of visualisation for the data we used into an analytic format.
Using R code to analyse regression output, decision trees, text and network analysis
1 x 2 hour lecture (hybrid)
1 x 2 hour tutorial
No materials were recommended or required.
Weekly Quizzes: 5%
Assignment 1: 10%
Assignment 2: 15%, both Assignments case study reports utilizing R code
Mid-semester test: 20%
Final Exam: 50%
Comments
This unit was a very helpful unit as it created a foundation knowledge for students who have barely used R Studio before. As many units down the line require some proficiency in R code to present their work.
The lectures held by the Chief Examiner were hybrid. During the lectures, the CE would go through step by step tutorials on how to use certain packages and code chunks and showed us outputs which were analysed interactively.
The tutorials were more of a workshop where students worked collaboratively on a R document based on what they were taught in the previous lecture.
The assignments were very similar to the workshops in format with students given a case study with different data in which they presented a report in a html format. The mid-semester test consisted on textboxes and multiple choice questions on moodle covering the content taught in the first half of the semester.
The 50% exam consisted of coding questions where students could utilise R to generate graphs and analyse their findings over many different scenarios and data sets.
General Overview:
Lectures:
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Assessments/Other Assessments:
Exams