Course Summary

Software engineering has advanced rapidly in recent years. The knowledge-, service-, and cloud-based economy in parallel with the continuous improvement in connectivity, storage and data processing     capabilities allow access to a data deluge from sensors, social-media, news, user-generated,                    government and private data sources. Accordingly, in a modern data-oriented landscape, data-driven   applications may need to deal with a collection of data sets - from unstructured, relational to NoSQL - that holds a vast amount of information gathered from various private/open data sources. Therefore,    well-engineered service-oriented functionalities are critical for ingesting, organizing and querying the growing volume of data in modern web-based applications.

This course aims to introduce the student to core concepts and practical skills for engineering the data in service-oriented data-driven applications. Specifically, the course aims to answer these questions:

•    Howtoaccessandingestdatafromvariousexternalsources?

•    Howtoprocessandstorethedataforapplications?

•    Howtocurate(e.g. Extract, Transform, Correct, Aggregate, and Merge/Split)andpublishthe data?

•    Howtoapplyavailableanalyticstothedata?

•    Howtovisualizethedatatocommunicateeffectively

Fundamentally, we will look at these questions through the lens of 'service-oriented' software design and implementation principles. At each topic, we will learn some core concepts, and how to               implement the concepts in software through services.

The course uses the Python Programming Language as the practical basis for its modules. However, the concepts taught are universal and can be applied to any other web development framework.

For 2022T1, the course will be delivered fully Online. The lab activities along with walkthroughs will be made available and online consultation labs will be scheduled to provide additional support.

Assumed Knowledge

Before commencing this course, we will assume that students have:

•    Completed one programming course (expected to be in Python)

•    Basic data modelling and relational database knowledge

These are assumed to have been acquired in the following courses: For Postgrad - COMP9021 and COMP9311. For Undergrad - COMP1531 and COMP2041.

Student Learning Outcomes

When you successfully complete this course, you should be able to:

•    Understand how to work with data and the various technologies involved in accessing, curating, storing, and publishing data

•    Understand how to apply existing analytics and visualisation techniques to data

•    Describe the main requirements to design and implement APIs (specifically REST APIs).

•    Describe the main architecture of a modern Web-based, data-oriented services.

•    Design and develop non-trivial data services solutions. The solutions can be about building      applications that utilise the above technologies, or about making the technologies accessible to potential consumers

•    Understand the basic issues with scalability, security/privacy and using online data processing platforms

Prospectivestudentsshouldnotethatthiscoursedoesnotaimtointroduceyoutothelatestpackages   orproductsavailableinthemarket. Rather, itstrivestoteachstudentsthebasicconceptsandthe         fundamentalprinciplesintheirimplementationtechnologiessothattheyareabletofollowandabsorb technologicaldevelopmentsinthisspace.

Graduate Capability

Acquired in

Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems

lectures and

assignments

Entrepreneurial leaders capable of initiating and embracing innovation and change, as        forums

well as engaging and enabling others to contribute to change

Professionals capable of ethical, self- directed practice and independent lifelong learning    assignments

Global citizens who are culturally adept and capable of respecting diversity and acting in   forums a socially just and responsible way

Teaching Strategies

•    Lectures: introduce concepts, show examples (Online lecture for 2022T1)

•    Lab Activities: introduce technology required for the assignments

•    Consultation Labs every day of the week

•    Online Quizzes: revision of the concepts introduced in Lectures and Lab Work

•    Assignments: solve significant problems

Teaching Rationale

This course is taught the way it is because we believe in structured learning, also learning by doing.    We provide timely feedback for learning via small, step-by-step individual assignments that gradually build up knowledge and practical skills.

Assessment

Assignment 3      Data Analytics

Week 10

20%

Course Schedule

2             Data Access and ingestion                Accessing NoSQL DB, API data sourced,           -                           Quiz 1 CSV files, text files.

6                                      ---                                                                 ---                                        ---                        ----

8             Data Analytics Applied                     Classification example                                                                      Quiz 4 Techniques and Tools part1

*Note: The Course Schedule might change according to the progress of the topics and feedback      throughout the course. Although the quizzes are planned as indicated, but the actual number may vary depending on the progress of the weekly topics.

Changes made in response to MyExperience feedback

In the spirit of continuous improvement, the following adjustments were made to address students’ comments in the MyExperience Survey from the last offering:

•   Relying on two additional Tutors to ensure fast response on the Course Forum

•   Adopting maker-checker strategy for developing the Assignments’ specifications. This will help making sure everything is clear, checked and double checked.

•   Establishing stronger line of communication between the teaching team through     weekly stand-ups and frequent check-ins. This will minimize misunderstandings and increase consistency.

Student Conduct

The Student Code of Conduct (Information,Policy) sets out what the University expects from        students as members of the UNSW community. As well as the learning, teaching and research          environment, the University aims to provide an environment that enables students to achieve their  full potential and to provide an experience consistent with the University's values and guiding          principles. A condition of enrolment is that students informthemselvesof the University's rules and policies affecting them, and conduct themselves accordingly.

In particular, students have the responsibility to observe standards of equity and respect in dealing       with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as            behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW     courses or course work. Behaviour that is considered in breach of the Student Code Policy as                 discriminatory, sexually inappropriate, bullying, harassing, invading another's privacy or causing any   person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.

If you have any concerns, you may raise them with your lecturer, or approach theSchool Ethics Officer,Grievance Officer, or one of the student representatives.

Plagiarism isdefined asusing the words or ideas of others and presenting them as your own. UNSW    and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as      being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:

•   Plagiarism and Academic Integrity

•   UNSW Plagiarism Procedure

Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism.   In particular, you are also responsible that your assignment files are not accessible by anyone but you  by setting the correct permissions in your CSE directory and code repository, if using. Note also that   plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.

UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity.     Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is         defined as using the words or ideas of others and passing them off as your own.