代做32130 Fundamentals of Data Analytics Spring 2024帮做R程序

SUBJECT OUTLINE

32130 Fundamentals of Data Analytics

Subject description

Data analytics is the art and science of teasing meaningful information and patterns out of large quantities of data. It  combines statistical methods for identifying patterns in data and making inferences with a number of IT technologies, including database technologies for handling massive volumes of data, intelligent and smart systems technologies, visualisation and other multimedia techniques that appeal to human pattern discovery capabilities. The subject offers  broad background to data analytics and data analytics methods and their application in practice. It brings together the state-of-the-art research and practice in related areas and provides students with the necessary knowledge and capacity to initiate and lead data analytics projects that can turn company data into commercially valuable information.

Subject learning objectives (SLOs)

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

1. Identify skills and attributes required for the understanding and application of data analytics in Industry. (D.1)

2. Describe the methods involved in data analytics, their scope and limitations. (D.1)

3. Design a data analytics project in a business environment. (C.1)

4. Apply data analytics methods for descriptive and predictive analytics tasks in a business environment. (D.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

Subject presentation includes combined lecture and workshop sessions and practical data analytics tasks for the   assignments. Students will need to undertake preparation using material on Canvas to make effective use of their  workshop time. Lectures will present the theoretical aspects of data analytics, including guest lectures about case  studies of real-world business applications of data mining techniques. The workshop sessions focus on hands-on  experience in data analytics and data analytics tools, and understanding and interpretation of the results. Practical assignments can be performed anywhere.

Prepreparation will help students to participate in the in-class individual and group exercises. Regular zero mark quizzes throughout the semester will allow students to gauge their progress

Content (topics)

The subject will cover topics from the following:

a.  Introduction to data analytics: problems; data analytics concepts, types of data that we collect, the data mining and

knowledge discovery process (CRISP DM methodology), differences between data analytics and knowledge discovery, what can be discovered, overview of application areas, the data analytics professional.

b.  Data pre-processing and transformation: problems; small and large data sets; missing data and dealing with it; noisy data and sampling; missing data; techniques for data cleaning.

c.  Visual data exploration and analytics: data visualisation techniques and their applicability in data analytics, visual data analytics methods.

d.  Clustering: problems for cluster analysis; partitioning methods, hierarchical methods; k-means and related methods. e.  Classification and prediction: problems for classification and prediction; classification by decision tree induction;

classification by support vector machine; ensemble methods and random forest; classification accuracy; issues in prediction.

Assessment

Details about assignments and submission procedures are provided on the subject website. Assignments are to be submitted to UTS Canvas. Zero mark quizzes will help students to gauge their progress in the subject. Continuous monitoring and feedback is given to students during in-class activities that they can use to help with their assignments

Assessment task 1: Dream Jobs

Intent: The intent is to discloseskill gaps.

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

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

D.1

Type: Report

Groupwork: Individual Weight: 15%

Task: Individual assessment.

This assignment is an individual project where students look at several job advertisements in data    science / data analytics. From these they distil the skills and attributes required for the jobs. They write a short report outlining how their current experience and skills measure up and outline a set of experiences and a plan to gain the required skills and attributes.

Each student must also provide a reflection on how these skills and attributes are relevant to the application of data analytics in Industry.

Length: The task requires submission of a report of approx. 1000 words (2-3 pages in an 11 or 12 point font).

Due: 11.59pm Friday 23 August 2024

Week 3

Further Feedback processes: marks with feedback within 2-3 weeks of submission through returned work. information:

Assessment task 2: Data Exploration and Preparation

Intent: The intent is to master basic data exploration and preparation skills for data analytics.

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

2 and 4

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

D.1

Type: Report

Groupwork: Individual

Weight: 35%

Task: Individual assessment.

This assignment includes practical work on data visualisation, exploration and preparation (preprocessing and transformation) for data analytics.

Length: A report of about 2500-3000 work report (approx. 20 pages in an 11 or 12 point font).

Due: 11.59pm Friday 20 September 2024

Week 7

Further Feedback processes: marks with feedback within 2-3 weeks of submission through returned work.

Weighting in Assessment criteria is approximate. Please refer to marking guide for specific weighting allocation.

Assessment task 3: Data Analytics in Action

Intent: The intent is to utilise data analytics skills for problem-solving.

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

3 and 4

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: 50%

Task: Students will be allocated a group of classifiers to use for a predictive analytics task. They must use

at least all classifiers within the group, but more probably several other methods of their own

choosing to solve the problem. The best classification model will be submitted to the Kaggle website. Students must submit a 2000-3000 word report discussing how they solved the problem and give

results. This will contribute to 30 of the 50 marks.

Each student will also undertake a short oral defence of their work. This will contribute to 20 of the  remaining 50 marks and defences will be run throughout the session. At the oral defence, students will answer questions about their classifier(s) showing the workflow or code. Students who fail will

receive 0 out of 20 marks. Students showing their allocated classifier will receive 10 marks. Students showing several classifiers with some preprocessing and parameter setting will receive 15 marks.

Students showing an insightful and thorough investigation will receive 20 marks. Students who fail are allowed to undertake the oral once again and if they pass will receive a maximum of 10 marks.

Length: 2000-3000 word (approx. 10-12 pages) report. Oral defence of around 5 minutes.

Due: 11.59pm Friday 1 November 2024

Week 12

Further Feedback processes: For the report, marks with feedback within 2-3 weeks of submission through information: returned work.

Weighting in Assessment criteria is approximate. Please refer to marking guide for specific weighting allocation.