1 Subject Information
Data Science Practice is designed to provide MDSI students with the technical data science skills that they will require to succeed in ‘Data Scientist’ roles across many industries.
Note that the official UTS Subject Outline (available on UTS Canvas) is the only official version of this information. Useful sections of the Subject Outline are reproduced below for reference.
In the case where the information below conflicts with the information in the official UTS Subject Outline, you should refer to the Subject Outline.1.1 Subject Learning Objectives
Upon successful completion of this subject students should be able to:
- Participate in the development of data science projects using popular programming languages including R, Python and SQL.
- Articulate the strengths, weaknesses and use-cases of common code control workflows, and demonstrate ability to work collaboratively using these tools
- Interact with and query data from modern data warehousing and data lake technologies.
- Work confidently in a Linux or Unix environment, including the use of Bash via a Command Line Interface, and the use of containers to create a reproducible data science environment
- Articulate some of the common risks when deploying data science projects, and identify controls that can be used to minimize those risks
- Create and present high-quality data analysis artefacts using literate programming tools (i.e. notebooks)
1.2 Teaching and Learning Strategies
This subject is conducted in 4 face-to-face sessions with weekly activities & readings assigned between classes. The classes are a mix of lecture components and collaborative “lab” sessions, working on data science projects as a team. Each session runs for eight hours on non-consecutive Saturdays.
The lab components involve two types of activities:
- ‘code together’ sessions in which the instructor and students build understanding through collaboratively coding solutions to problems or implementing theoretical concepts.
- Practical coding tasks for students to complete themselves or in small groups.
Assignments are a mix of practical coding exercises, report writing (for a business audience) as well as solution design and implementation tasks. Through these students get deep exposure to historical and current industry trends and challenges, while developing tangible skills to implement these technologies and in a work context.
Due to the rapidly advancing nature of this field it is important for students to develop skills in quickly absorbing, dissecting and understanding new technologies and their value to business problems. This assignments in the subject are designed to help students develop these critical new skills
1.3 Assessment Overview
| Task | Assessment | Type | Group/Individual | Weight |
|---|---|---|---|---|
| 1 | Collaborative Development using Centralised Code Repositories | Laboratory/ Practical | Group work, group assessed | 25% |
| 2A | Programming Cheat Sheets | Report | Individual | 20% |
| 2B | MDSI Slack Analysis | Laboratory/ Practical | Individual | 30% |
| 3 | Reproducibility and Risk Audit | Project and Report | Individual | 25% |
1.4 Schedule
| UTS Week |
Week Starts Monday | Learning Tasks |
|---|---|---|
| 1 | 2019-07-22 | UTS Orientation Week Reading: Subject Information and Introduction |
| 2 | 2019-07-29 | Reading: Data science is different now by Vicki Boykis |
| 3 | 2019-08-05 | Lecture: (Saturday 2019-08-10) Git and Remote Repositories Assessment: Assessment 1 issued |
| 4 | 2019-08-12 | Online: Learning SQL with DataCamp |
| 5 | 2019-08-19 | Online: Learning SQL with DataCamp |
| 6 | 2019-08-26 | Online: Learning R with DataCamp Lecture: (Saturday 2019-08-31) Structured Query Language (SQL), R and Python Assessment: Assessment 1 due Assessment 2A issued |
| 7 | 2019-09-02 | Online: Learning R with DataCamp |
| Break | 2019-09-09 | Online: Learning Python with DataCamp |
| 8 | 2019-09-16 | Online: Learning Python with DataCamp Assessment: Assessment 2A due Assessment 2B issued |
| 9 | 2019-09-23 | Lecture: (Saturday 2019-09-28) Programming Concepts Reading: Machine Learning: The High-Interest Credit Card of Technical Debt by Google AI |
| 10 | 2019-09-30 | Reading: R Docker Tutorial by rOpenSci Labs |
| 11 | 2019-10-07 | Lecture: (Saturday 2019-10-12) Unix Systems, Containers and Application Programming Interfaces (APIs) Assessment: Assessment 2B due Assessment 3 issued |
| 12 | 2019-10-14 | |
| StuVac | 2019-10-21 | Assessment: Assessment 3 due |
| Assessment Period |
2019-10-28 | |
| Assessment Period |
2019-11-04 |
1.5 License
The UTS Master of Data Science and Innovation MDSI 94692: Data Science Practice course materials here are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). If re-using/re-mixing please provide attribution and link to this webpage, and ensure that the re-used/re-mixed content is made available via a similar license.