Financial Data Science Assignment Help Australia

Financial Data Science Assignment Help

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The financial data science course equips students with cutting-edge tools, skills, and knowledge making them capable enough to handle data problems on new scales and tackle complexity for tasks in context to portfolio optimisation, credit risks, and market modelling. Data science technologies such as machine learning have led to the transformation of world of asset management, altering how sophisticated investors approach risk management and portfolio construction.

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List of Institutes in Australia Offering Financial Data Science Course

• University of Technology, Sydney
• Macquarie University
• University of Sydney
• RMIT University
• Deakin University
• Queensland University of Technology
• University of South Australia
• Monash University
• University of Melbourne
• University of Western Australia
• University of New South Wales

The aforementioned universities or institutes in Australia are renowned for their quality of teaching and research and they offer distinct inter-related degree courses. If you are studying in any of these universities, then it is very important to follow all marking rubrics and meet deadlines while composing any assessment tasks.

Structure of Course

This course unit of financial data science comprises the following topics:

• Financial market instruments
• Fundamental of derivative security pricing
• Financial econometrics and statistics
• Credit risk model
• Interest rates
• Probability theories
• Stochastic analysis
• Machine learning
• Risk management
• Advanced Bayesian methods

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Learning Outcomes of Course

After completing the course successfully, students will become capable in:

1. Applying predictive analytics, inferential statistics, and data mining techniques to informatics interrelated fields.
2. Analysing datasets with supervised learning methodologies for functional approximation, forecasting, and classification, and unsupervised learning methodologies for dimensionality reduction or clustering.
3. Identifying, assessing, and selecting adequately among statistical learning methodologies and models to solve a specific real-world problem.
4. Writing programs for executing data analytics on large and complex datasets
5. Analysing computational complexity in context to algorithm development.
6. Depicting knowledge of finance, technology, and critical data science.

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Career Opportunities

After completing the course successfully, candidates may chose to pursue their career in a number of firms as listed below:

• Banks and financial institutions
• Consulting firms
• Businesses and corporations
• Environmental and conservation groups
• Defence agencies
• Government agencies and departments
• Financial technology firms
• Accounting firms
• Public service
• Investment firms
• Research and development companies
• Marketing and public relations firms
• Retail and e-business companies
• Reserve Bank of Australia
• Wealth management and financial advising firms
• Technology and software development companies

Potential job roles are listed below:

• Business or venture analyst
• Financial engineer
• Consultant
• Data engineer
• Credit risk analyst
• Data analyst
• Entrepreneur
• Financial adviser or analyst
• Finance manager
• Fintech manager
• Financial adviser or analyst
• Fund manager
• Information technology analyst
• Government adviser
• Market analyst
• Investment adviser
• Risk management specialist
• Mortgage broker
• Stockbroker

Our Experts Discuss the Approach to Draft Financial Data Science Assessment Answer

Our world leading experts will provide you the conceptual and applied knowledge in this field of financial data science. They are capable enough to teach you to create your own strategies and also the key components of data science in quantitative finance.

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Develop technical capabilities and gain creative capacity under the supervision of our experts to address the big challenges. The experts encourage students to follow the below mentioned approach to draft a perfect assessment solution:

• Check the type of assignment (business projects, portfolios, essay, reports, email communications, articles, case studies, thesis or dissertation, literature review, etc.) and formulate the solution accordingly.
• Note down the desired referencing style (APA, MLA, Chicago, etc.).
• Look for the required formatting style before commencing the assignment solution.
• Carefully note down the overview of the assignment and then prepare a draft after conducting in-depth and high performance computing research.
• Write the assignment solution by strictly following the marking rubrics provided by the university.
• Incorporate their management, leadership, problem solving, and critical thinking skills along with knowledge of machine learning, statistics, data science tools, methodologies, finance, portfolio optimisation, and market modelling to solve different assignments.
• Include all necessary elements, software, and methodologies desired in the assignment.
• After completion of the assignment solution written in a perfect layout format with proper citation of references, read the file at-least twice or thrice to rectify any error.

To have a clearer glimpse of the approach our online tutors follow in order to guide students with the solution, you can register on our website and view and download the Financial Data Science assignment sample online. Few snapshots of the assessment tasks solved under the expert’s guidance are depicted below for your reference:

Our experts cover each section of the task thoroughly. If you want to download the complete solution of the aforementioned assessment tasks, reach out to us and enhance your knowledge. Our academicians can guide you in every possible way.

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