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:

  1. Participate in the development of data science projects using popular programming languages including R, Python and SQL.
  2. Articulate the strengths, weaknesses and use-cases of common code control workflows, and demonstrate ability to work collaboratively using these tools
  3. Interact with and query data from modern data warehousing and data lake technologies.
  4. 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
  5. Articulate some of the common risks when deploying data science projects, and identify controls that can be used to minimize those risks
  6. 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

Week Starts Monday Learning Tasks
1 2019-07-22 UTS Orientation Week
Subject Information and
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 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 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 2A due
Assessment 2B issued
9 2019-09-23 Lecture: (Saturday 2019-09-28)
Programming Concepts
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 2B due
Assessment 3 issued
12 2019-10-14
StuVac 2019-10-21 Assessment:
Assessment 3 due

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.