This short course is ideal for professionals interested in a data science and/or entry-level programming career. It is also suitable for those seeking programming and data science skills to enhance their specific job capabilities.

Please note that participants should hold a bachelor's degree (in any discipline) and possess digital literacy skills. In addition, knowledge of specific computer programs for communication and collaboration, e.g. Zoom, is required. Familiarity with basic statistical concepts is also desirable, though not a requirement.

Python is the preferred language for data analysis across fields like math, business, science, and engineering. This makes learning Python extremely beneficial for anyone seeking a new career in the fast-developing data science industry.

By the end of this course, you'll have a deeper understanding of data analytics with Python.

You’ll be able to:

• Apply fundamental programming techniques, including sequence, selection, iteration and abstraction, to produce simple Python programs
• Use fundamental data structures related to data science, including arrays, lists, dictionaries, series’ and data frames to store and represent data
• Understand how different types of data can be represented and extracted from a variety of forms, including structured, semi-structured and unstructured data
• Apply simple pre-processing techniques to clean data before conducting a detailed investigation
• Apply simple machine learning algorithms for solving regression, clustering, and classification problems
• Evaluate the suitability and performance of various machine learning algorithms for solving representative data science problems
• Create simple visualisations to convey the results of a data science investigation
• Critically evaluate different approaches for solving a data science problem
• Participate actively in teams oriented towards a common goal, including through the use of collaborative software tools.

You will be taught by a teaching team led by Dr Geela Chee from the School of Computing and Information Systems at the University of Melbourne. Dr Chee holds a PhD in Computer Science from the University of Canterbury (New Zealand) and has taught various programming subjects at multiple universities, including introductory programming courses in Java, C and Python. Dr Chee has also taught courses in data science, database systems, and user design principles.

Data Analytics with Python runs for 12 weeks, with an approximate weekly time commitment of between 12 and 16 hours.

This includes:

• 3 hours of live online classes
• 1 - 2 hours of pre-recorded classes
• 8 to 11 hours for self-study and assignments.

Assessments

Assessment 1: Assignment 1

Form: Programming assignment
Weighting: 40%
Due date: Week 7

Assessment 2: Assignment 2

Form: Programming assignment with written report
Weighting: 40%
Due date: Week 11

Assessment 3: Weekly quizzes.

Form: Online quizzes
Weighting: 20%
Due date: Throughout course

This short course course offers a pathway to university degrees in IT at the University of Melbourne. Participants who complete the course with an overall mark of 70% or above and meet all other course entry requirements may be considered eligible to apply for a Master of Information Technology (200 points).

The Digital Jobs for Manufacturing Program also includes the following short courses:

• Additive Manufacturing for Innovative Design and Manufacturing
• Industrial Engineering: Digital Design to Manufacturing