Outline
- Unit 1: Contextualising Data Preprocessing
- Unit 2: Fundamentals of Data Preprocessing
- Unit 3: Dealing with Missing Values
- Unit 4: Dealing with Noisy Data
- Unit 5: Data Reduction
- Unit 6: Discretization
- Unit 7: Feature Selection
- Unit 8: Instance Selection
Note: As you move through the units, you will refer to the Data Preprocessing With R and Python section of the course for code samples that support and extend the concepts introduced in each unit.
Learning outcomes
Upon successful completion of this course, you should be able to
- articulate the fundamentals of data preprocessing: what it is, why it is important in data science, and what steps are involved.
- explain and resolve issues with missing and noisy data.
- identify, optimize, and extract key data.
- implement data preprocessing techniques in Python or R.
- research and communicate on issues in data science.
Evaluation
To receive credit for DATA 400, you must achieve a course composite grade of at least D (50 percent) and a grade of at least D (50 percent) on the final exam. All activities, except for the data science wiki article, must be completed to pass the course.
The weighting of the composite grade is as follows:
| Activity | Weight |
| Quiz | 5% |
| Assignment 1 | 25% |
| Assignment 2 | 25% |
| Data Science Wiki Article | 5% |
| Final Exam | 40% |
| Total | 100% |
The final examination for this course must be requested in advance and written under the supervision of an AU-approved exam invigilator. Invigilators include either ProctorU or an approved in-person invigilation centre that can accommodate online exams. Students are responsible for payment of any invigilation fees. Information on exam request deadlines, invigilators, and other exam-related questions, can be found at the Exams and grades section of the Calendar.
Materials
This course either does not have a course package or the textbooks are open-source material and available to students at no cost. This course has a Course Administration and Technology Fee, but students are not charged the Course Materials Fee.
Challenge for credit
Overview
The challenge for credit process allows you to demonstrate that you have acquired a command of the general subject matter, knowledge, intellectual and/or other skills that would normally be found in a university-level course.
Full information about challenge for credit can be found in the Undergraduate Calendar.
Evaluation
To receive credit for the DATA 400 challenge registration, you must obtain a composite grade of at least D (50 percent), including a grade of at least 50 percent on each assignment and the final exam.
The weighting of the composite grade is as follows:
| Activity | Weight |
| Assignment 1 | 25% |
| Assignment 2 | 25% |
| Final Exam | 50% |
| Total | 100% |
Challenge for credit course registration form