Subject description

The subject focuses on Python programming basics with practical applications in data processing, analysis and visualisation. Students learn basic programming concepts, common data structures, simple visualisation techniques, how to write custom programs using Jupyter notebooks, and how to perform data processing and exploratory data analysis using common Python packages and libraries with practical case studies.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Apply the fundamentals of the Python programming language. (D.1)

2. Apply common Python libraries for data processing and visualisation. (D.1)

3. Design custom Python programs to pre-process and visualise real-world datasets. (C.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in   new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)

Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced

discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity.   (D.1)

Teaching and learning strategies

This subject will be delivered in collaborative face-to-face sessions as workshops focusing on hands-on tutorial approaches designed to help students learn about and immediately practice techniques for Python programming in data processing. Students will need to prepare using materials available on Canvas to use their class time effectively.

The workshops will cover the theoretical aspects of the weekly topics and primarily emphasise hands-on labs in data processing in Python, including writing Python code for data manipulation, processing, and visualisation. The

workshops are conducted collaboratively, allowing students to interact and discuss while writing code to solve

problems, thereby maintaining peer-to-peer collaboration within the cohort. Students will receive valuable feedback

from both their peers and the tutor during these workshops. These workshops will provide the tools necessary to

complete assignments. Regular quizzes throughout the semester will enable students to assess their progress to

further seek advice and feedback to continue to achieve.

Content (topics)

1.  Introduction to programming in Python

2.  Decisions and iterations in Python

3.  Data structures in Python

4.  Functions and libraries

5.  Reading data from multiple sources

6.  Exception handling

7.  Data manipulation

8.  Data pre-processing

9.  Introduction to data visualisation

Assessment

Students can use either a Jupyter notebook on their personal computer or Google Colab to complete assessment 2 and 3.

Assessment task 1: Knowledge quizzes

Intent: To design, implement, and execute data processing tasks using Python-related packages.

Objective(s): This assessment task addresses the following subject learning objectives (SLOs): 1 and 2

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

D.1

Type: Report

Groupwork: Individual

Weight: 35%

Task: This assessment evaluates students'capacity to acquire the skills necessary for designing,

implementing, and executing programs that perform data importing and processing tasks using Python-related packages on practical case studies.

Students must design and implement a Python program using Jupyter notebooks for data

processing. They must find a dataset, develop a working program to load data into appropriate formats (i.e., data frames), display statistics, and identify the data quality issues.

Each student needs to create:

1.  A Jupyter Notebook,

2.  A Word document report that describes the main outcomes of the assessment, and 3.  A 3-5 mins video demonstration to describe results and challenges.

Length: There is no line limit on Python programs. A maximum of 1000 words for the report

Due: Week 10 | Sunday, 05 May 2024 at 11:59 PM

Further Deliverables:

Jupyter Python notebook (15%)

Word document report (10%)

A video demonstration (10%)

Intent: To design, implement, and execute data pre-processing and visualisation tasks using Python-related

packages.

Objective(s): This assessment task addresses the following subject learning objectives (SLOs): 2 and 3

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

C.1 and D.1

Type: Report

Groupwork: Individual

Weight: 45%

Task: This assessment task builds on Assessment Task 2. The student will continue working on the same

raw dataset used in Assessment Task 2.

The student needs to design strategies for data processing and data visualisation to address the

target problem using Python and relevant packages. In this task, students must resolve data quality issues using Python and relevant Python packages. Also, they must answer specific business

questions using data summaries and visualisations. It is expected that they will use Jupyter notebooks for implementation.

The student will be evaluated to acquire the skills necessary for designing, implementing, and

executing programs that perform data manipulations, pre-processing, and visualisation tasks using Python-related packages on real-world problems.

Each student needs to create:

1.  A Jupyter Notebook,

2.  A report to explain the outcomes of the analysis, highlight interesting results, and discuss any challenges encountered, and

3.  A 5-7-minute video demonstration to describe the results and challenges.

Length: There is no line limit on the Python program. A report with a maximum of 1500 words.

Due: Week 12 | Sunday, 19 May 2024 at 11:59 PM

Further Deliverables:

information:

Jupyter Python notebook (25%)

Word document report (10%)

A video demonstration (10%)