Overview
Data Science 300: Introduction to Data Science provides a comprehensive foundation in the principles and practices of data science for students with a background in Python programming and statistics. The course introduces the relevance of data in solving real-world problems across various domains. You will learn data preparation, pre-processing, and exploration, all key steps in any data science project. You will explore how data analysis and analytics techniques are used to support research and decision-making. Through hands-on activities and coding projects, you will gain experience in solving data-driven problems and evaluating model performance, preparing you for further study or a career in data science and analytics.
Learning outcomes
Upon successful completion of this course, you should be able to
- assess and articulate the relevance of data for particular business, healthcare, educational, and other societal challenges.
- collect, store, retrieve, and preprocess data.
- undertake different kinds of data analysis and data analytics.
- exhibit familiarity with data science methods by learning and experiencing essential algorithms and approaches.
- identify data-driven analytics problems, design solutions, and develop code to solve them.
Evaluation
To receive credit for DATA 300, you must achieve a course composite grade of at least D (50 percent) and a grade of at least 50 percent on both the project and the final exam. You must also submit all the other course activities.
The weighting of the composite grade is as follows:
| Activity | Weight |
| Quiz 0 | 5% |
| Quiz 1 | 10% |
| Quiz 2 | 10% |
| Project | 35% |
| 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
Digital course materials
Links to the following course materials will be made available in the course:
Shah, C. (2020). A hands-on introduction to data science. Cambridge.
Programming languages
For graded material, all code must be written in Python. However, for hands-on practice or examples, you can use any language you wish. There are examples in the eText that employ R, and learning R is helpful for a data scientist, but it is not formally taught as part of this course.
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 300 challenge registration, you must obtain a composite grade of at least D (50 percent), including a grade of at least 50 percent on both the project and final exam.
The weighting of the composite grade is as follows:
| Activity | Weight |
| Project | 35% |
| Final exam | 65% |
| Total | 100% |
Challenge for credit course registration form