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Overview

CPA Members only
CPA Australia has partnered with the Corporate Finance Institute (CFI) to curate a collection of data and digital courses exclusively for CPA members. CFI courses have an emphasis on hands-on, practical learning and skills acquisition. They teach in-demand industry practices, tools and techniques that help accounting and finance professionals build confidence, stand out from the competition, and make a positive impact on their careers.
Data science allows us to make data-driven insights. This course will guide you through the world of data science and machine learning, using applied examples to demonstrate real-world applications. Whether youre an aspiring data scientist or a c-level exec, this course will bring you up to speed on everything data science. Youll be introduced to machine learning, classification, exploratory data analysis, feature selection, and feature engineeringwhat they mean and how they are relevant to you.
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Our Privacy Policy includes the following statement under the subsection 'Links to external websites':
We may provide links to websites operated by third parties.
We are not responsible for the privacy practices or the content of such websites which will be governed by their own privacy policies to which you are advised to refer.
Your satisfactory completion of the course may be recorded in your CPD Diary.
CFI programs will be supplied to CPA Australia members as standalone courses for 12 months from the date of purchase and as such, any associated CFI certifications are not able to be supplied. Refunds or extensions for purchases of CFI courses will only be available in exceptional circumstances and will be based on CPA Australia and CFIs cancellation terms.
Course partner
What you'll learn
- Outline the data science cycle and machine learning process
- Explain the commonly used feature selection and feature engineering methods
- List the algorithms mostly used in supervised and unsupervised learning
- Read the key metrics used to evaluate a machine learning model
- Explain the techniques used to improve an underfitting or overfitting model
